# Maisa AI > Maisa AI: Redefining Automation with Accountable AI Agents --- ## Pages - [Chain of Work](https://maisa.ai/chain-of-work/) - [Terms of Service](https://maisa.ai/terms-of-service/) - [Privacy Policy](https://maisa.ai/privacy-policy/) - [Cookie policy](https://maisa.ai/cookie-policy/) - [Legal Notice](https://maisa.ai/legal-notice/) - [Careers](https://maisa.ai/careers/) - [AI Hallucinations](https://maisa.ai/ai-hallucinations/) - [AI Computer](https://maisa.ai/ai-computer/) - [Agentic Process Automation](https://maisa.ai/agentic-process-automation/) - [AI Agents](https://maisa.ai/ai-agents/) - [Agentic AI](https://maisa.ai/agentic-ai/) - [Digital Workers](https://maisa.ai/digital-workers/) - [Introducing Vinci KPU](https://maisa.ai/research/) - [Use Cases](https://maisa.ai/use-cases/) - [Manage your Digital Workforce](https://maisa.ai/manage-your-digital-workforce/) - [Resources](https://maisa.ai/agentic-insights/) - [Trust the Outcome](https://maisa.ai/trust-the-outcome/) - [Product Overview](https://maisa.ai/platform-overview/) - [Build a Digital Worker](https://maisa.ai/build-a-digital-worker/) - [Home](https://maisa.ai/) - [Manifesto](https://maisa.ai/about-us/) - [Contact](https://maisa.ai/contact/) --- ## Posts - [Maisa Named as a Front Runner in Gartner’s “Emerging Tech: AI Vendor Race - Startups to Watch in Agentic AI” Report](https://maisa.ai/agentic-insights/startups-to-watch-in-agentic-ai-gartner/) - [Maisa Wins the Rising Star Award at Deloitte’s Technology Fast 50 Spain](https://maisa.ai/agentic-insights/maisa-wins-deloitte-rising-star-award/) - [How Maisa Digital Workers Actually Work?](https://maisa.ai/agentic-insights/how-maisa-digital-workers-actually-work/) - [Agentic AI Cost Control: The Enterprise Leader’s Guide to Avoiding “Surprise Opex” at Scale](https://maisa.ai/agentic-insights/agentic-ai-cost-control/) - [Why Maisa Is Not an n8n-like tool or RPA+ AI](https://maisa.ai/agentic-insights/why-maisa-is-not-an-n8n-like-tool-or-rpa-ai/) - [What is business process automation (BPA)?](https://maisa.ai/agentic-insights/business-process-automation/) - [Agentic AI vs RPA: differences, similarities, and examples](https://maisa.ai/agentic-insights/agentic-ai-vs-rpa/) - [Maisa raises $25M seed investment](https://maisa.ai/agentic-insights/maisa-raises-25m-seed-investment/) - [Rising star of agentic AI delivering trustworthy ‘digital workers’ raises $25M from Creandum and Forgepoint](https://maisa.ai/agentic-insights/maisa-raises-25m-from-creandum-and-forgepoint/) - [Maisa Studio on AWS Marketplace: removing barriers to AI adoption](https://maisa.ai/agentic-insights/maisa-studio-on-aws/) - [CLATTER: Academic Validation for Our Maisa AI Hallucination Detection Strategy](https://maisa.ai/agentic-insights/clatter-hallucination-detection/) - [HALP: Maisa’s breakthrough in delivering reliability for enterprise automation](https://maisa.ai/agentic-insights/halp/) - [Why we built Maisa this way: scientific proof we're on the right track](https://maisa.ai/agentic-insights/science-behind-maisa-architecture/) - [Advancing our vision for Accountable AI together with Microsoft](https://maisa.ai/agentic-insights/microsoft-partnership/) - [Black Box AI. How can we trust what we can’t see?](https://maisa.ai/agentic-insights/black-box-ai/) - [Making AI accountable: Maisa raises pre-seed round](https://maisa.ai/agentic-insights/maisa-raises-pre-seed-round/) - [Introducing Vinci Knowledge Processing Unit (KPU)](https://maisa.ai/agentic-insights/vinci-kpu/) - [Hello world](https://maisa.ai/agentic-insights/hello-world/) --- ## Industries - [Insurance](https://maisa.ai/insurance/) - [Engineering & Infrastructure](https://maisa.ai/engineering-infrastructure/) - [Manufacturing](https://maisa.ai/manufacturing/) - [All Industries](https://maisa.ai/all-industries/) - [Banking & Financial Services](https://maisa.ai/banking-financial-services/) --- ## Use cases - [Equity Research Automation](https://maisa.ai/banking-financial-services/equity-research-investment/) - [RFP Response Generation Automation](https://maisa.ai/banking-financial-services/rfp-response-generation/) --- # # Detailed Content ## Pages - Published: 2026-03-16 - Modified: 2026-03-22 - URL: https://maisa.ai/chain-of-work/ Chain of work Chain of Work makes AI execution deterministic, traceable, and transparent, eliminating guesswork and enabling reliable, auditable automation at scale What is Chain of Work? Chain of Work is a structured, traceable log that records every step of AI execution. Every decision, process, and action is systematically logged, making AI workflows fully auditable and transparent. It is code-based, ensuring execution follows predefined logic rather than probabilistic estimations. Unlike Chain of Thought, which relies on sequential LLM calls to generate answers, Chain of Work doesn’t depend on probabilistic outputs. Instead, AI orchestrates tools, processes, and data in a deterministic and structured way, ensuring predictable and verifiable execution. This approach operates like a computational system, where execution follows a clear, logical sequence. This ensures:Traceability Every step is recorded and reviewable. Transparency AI actions are explainable and accountable. Reliability The same input always produces the same output. Why is Chain of Work needed? Most AI systems today function as black boxes—they generate responses, but we often don’t know how or why they reached a specific conclusion. This is because they rely on probabilistic models, which predict the most statistically likely answer rather than following a structured, logical process. This lack of transparency creates serious challenges for businesses that need AI to be reliable, explainable, and accountable in critical operations. Key problems with traditional AI systemsHallucinations AI can generate false or misleading information that sounds plausible but has no factual basis. Lack of Traceability There is no clear record of how decisions are made, making it difficult to verify outputs. Inconsistent Results The same input can lead to different outputs, reducing reliability in decision-making. In high-stakes environments—such as business operations, compliance, and automation—this unpredictability makes AI hard to trust. Without a way to verify each step of its reasoning, businesses risk relying on AI-driven decisions that cannot be explained or corrected. How Chain of Work solves this Instead of relying on probabilities, Chain of Work ensures AI follows a deterministic process, orchestrating tools, data, and logic in a fully traceable way. Every decision and action is logged, creating a structured audit trail that makes AI execution transparent and accountable. This eliminates randomness, ensuring:Determinism The same input always produces the same output. Traceability Every step, tool, and data source is recorded. Audibility Clear reasoning paths allow verification and refinement. Error correction – If something goes wrong, it’s identifiable and fixable, not an unpredictable failure. Chain of Work makes AI function like a computational system—navigating facts, executing processes logically, and delivering consistent, reliable results. It removes uncertainty from AI-driven automation, making it transparent, accountable, and ready for real-world use. Key differentiators of Chain of Work Chain of Work ensures deterministic, traceable, and structured AI execution, eliminating guesswork. Here’s how it compares: Feature Chain of Thought RAG Chain of Work Deterministic Chain of Thought No RAG No Chain of Work Yes Fully Traceable Chain of Thought No RAG Partial Chain of Work Yes Prevents Hallucinations Chain of Thought No RAG Partial Chain of Work Yes... --- - Published: 2026-03-13 - Modified: 2026-03-13 - URL: https://maisa.ai/terms-of-service/ Terms of Service These Terms of Service (“Agreement”) are the agreement governing your access to and use of the Services as defined below. This Agreement is between Maisa, Inc, a Delaware corporation, with offices at 1111B S Governors Ave STE 3624 Dover, DE 19904 (“Maisa”), and the entity you represent by entering into this Agreement (“Customer”). Any capitalized terms not defined throughout the Agreement will have the meaning given to them in Section 17 (Definitions). This Agreement is effective upon the earlier of (i) your acceptance of this Agreement, or (ii) the date you first accessed the Services, as applicable (“Effective Date”), and will remain in effect until terminated in accordance with this Agreement. 1. Binding Effect Access to and use of the Website is only permitted for individuals eighteen (18) years of age or older. Access to and use of the Website do not require the creation of a user account. However, in the future, Maisa Inc. may incorporate restricted sections or functionalities that do require user registration. Intellectual and industrial property By using the Services hosted in the Platform and/or entering into this Agreement, you represent and warrant that (i) you have read and understand this Agreement, (ii) you understand that the Services provided under this Agreement are for businesses, professionals and developers, not consumers, (iii) you are not a consumer as defined under applicable laws, (iv) you have full legal authority to bind Customer to this Agreement, and (v) you agree to this Agreement on behalf of Customer. If you or Customer do not agree with this Agreement, please refrain from accepting this Agreement and from using the Services. 2. Services Provision of Services. During the Term, Customer will have access to Maisa’s web-based artificial intelligence-powered studio (“Studio”) for the purpose of creating, configuring, and deploying multi-modal AI agentic cloud functions or Digital Workers (“Agents”) on the Platform (collectively, the “Services”) in accordance with this Agreement. Use of Services. Customer agrees only to use the Services in accordance with this Agreement. Customer’s use of the Services may include deploying the Services to develop Customer Applications and making available Customer Applications to End Users, provided, however, that Customer may not sublicense the Agents or the Services as a standalone or integrated product. Customer will ensure that End User’s use of the Services complies with this Agreement. Sign up/Account. Customer or End User must sign up on the Platform to create an account (“Account”) to use the Services. The Customer may do so by synchronizing its Google or Microsoft account or by completing the data fields requested by Maisa (name, surname, email) which will be processed in accordance with the Privacy Policy. Customer is solely responsible for all activities that occur under its Account, including using, managing and protecting the Account, including its security, both by Customer and End Users. Customer will not (i) disclose or otherwise share Account access credentials with unauthorized third parties, (ii) share individual login credentials between multiple users on an Account, or (iii) resell... --- - Published: 2026-03-13 - Modified: 2026-03-13 - URL: https://maisa.ai/privacy-policy/ Privacy Policy 1. IntroductionMaisa Inc. (hereinafter, “Maisa”, “we”, “us”, or “our”) is the owner of the platform (hereinafter, the “Platform”) available at https://maisa. ai/ (hereinafter the “Website“) and is responsible for the processing of the personal data of the users thereof (hereinafter the “Users“). As used herein, “you” and “your” refer to our Users. Through this Privacy Policy, and in compliance with Articles 12 and 13 of Regulation (EU) 2016/679 (hereinafter “GDPR“) and Article 11 of the Organic Law 3/2018, of December 5, on the Protection of Personal Data and guarantee of digital rights (“LOPDPGDD“), Maisa informs Users who use the Website and/or Platform about the processing of their personal data that may be collected through the Website and/or the Platform and processed by Maisa. In order to allow the User to access and use the Platform and Maisa services, Maisa may process the necessary data for the execution of the Terms and Conditions, and more generally to manage our contractual and/or commercial relationship with the Users, and to inform them promptly about any aspect related to the services provided or that can be performed by Maisa in the future. 2. Data controllerData Controller: Maisa AI Inc. EIN Number: 99-2702335Registered office: Delaware, with offices at 1111B S Governors Ave STE 3624 Dover, DE 19904Contact: privacy@maisa. ai 3. Data processed, purposes, legal basis and retention periodPersonal data collected on the Maisa Website:Data ProcessedPurposes Legal BasisRetention PeriodBrowsing data. The IP address of User’s computer and the type of browser User is using. We use this information to analyze general trends and improve our services, including the Website. Except for disclosures described herein, this information is not shared with third parties without User’s consent. Management of the functionality of the Platform and/or Website. Analysis of browsing behavior and statistics: The information collected through cookies and other similar tracking technologies allows an analysis of the browsing of Users. Consent of the User (Not necessary cookies). Legitimate Interest (Necessary Cookies). The retention periods depend on each specific cookie. For more information on the information retention periods for each type of cookie, see the Cookie Policy: https://maisa. ai/cookie-policy/Waitlist. Name, last name, email, and company. Send information and news about the launch of the Platform. ConsentThe personal data will be processed by Maisa for the necessary period of time to inform about the Platform launching. Such data will be blocked stored in order to comply with Maisa’s legal obligations and, after that time, will be definitively deleted. Personal data collected on the Maisa Platform:Data ProcessedPurposes Legal BasisRetention PeriodBrowsing and Maisa Platform usage data. The IP address of User’s computer and the type of browser User is using. We use this information to analyze general trends and improve our services, including the Platform. Except for disclosures described herein, this information is not shared with third parties without User’s consent. Management of the functionality of the Platform and/or Website. Analysis of browsing behavior and statistics: The information collected through cookies and other similar tracking technologies allows an analysis of... --- - Published: 2026-03-13 - Modified: 2026-03-13 - URL: https://maisa.ai/cookie-policy/ Cookie Policy MAISA INC. , (hereinafter “MAISA“) is the owner of the Website https://maisa. ai/ (hereinafter the “Website“) and it’s the owner of the platform https://studio. maisa. ai/ (hereinafter the “Maisa Studio”). Both of them use cookies that collect information related to the connection, browsers, and devices used by Internet users who access or use the Website and/or the Maisa Studio (hereinafter the “User/s“). MAISA uses this information to manage and improve the proper functioning of the Website and/or the Maisa Studio. This Policy describes what information these cookies collect, how they are used and for what purpose. It also indicates how the User can restrict or block the automatic downloading of cookies, however, this could reduce or even hinder certain elements of the functionality of the Website and/or the Maisa Studio. Likewise, the User can choose the category of cookies that he/she wishes to activate in the cookies banner that appears the first time he/she accesses the Website and/or the Maisa Studio. 1. Definition of CookiesCookies are small text files that are placed on the User’s computer, smartphone or other device when accessing the Internet. This is done to improve the User’s experience and for other purposes, such as recognizing Users when accessing the Website and/or the Maisa Studio, ensuring the security of your account and delivering targeted advertising. For more general information about cookies, please see the following article. 2. How we use CookiesIn summary, MAISA uses the cookies listed in Annex I for the Website and the cookies listed in Annex II of this Policy for the Maisa Studio to track how the Website and/or the Maisa Studio is used in order to optimize its operation. 3. What Cookies we useThe Website and/or the Maisa Studio use both its own and third-party cookies:First-party cookies: cookies sent to your device by MAISA through the web domain. Third-party cookies: these are sent to your device by domains that are not managed by MAISA but by another entity that processes the data collected through cookies. According to the purpose of the cookies, the cookies used by MAISA can be divided into the following categories:Technical cookies (necessary): cookies necessary for navigation and for the proper functioning of the Website and/or the Maisa Studio. Their use allows basic functions, such as access and secure navigation. The legal basis that allows the collection of data through these cookies is the legitimate interest of MAISA in the management of the Website and/or the Maisa Studio. No information collected through these cookies is shared with third parties. See the cookie table below for more details of these cookies. Analytical cookies: allow monitoring and analyzing the behavior of Users. The information collected through this type of cookies is used to measure the activity of the Website and/or the Maisa Studio and for the elaboration of browsing profiles of the Users, in order to improve the Website and/or the Maisa Studio and their services. The legal basis for collecting this data through these cookies is the consent... --- - Published: 2026-03-13 - Modified: 2026-03-13 - URL: https://maisa.ai/legal-notice/ Legal Notice “This legal notice (the “Legal Notice”) governs access to and navigation of the website www. maisa. ai (the “Website”). The Website is owned by Maisa Inc. , (“Maisa” or “we”), whose identifying and contact information is as follows: Address: 8 The Green STE R, Dover, Kent County, Delaware 19901. Contact email address: contact@Maisa. aiREG & C. o. Incorporation: State of Delaware, Division of Incorporations SR20233303552FN7632442This Legal Notice is binding for anyone accessing the Website (the “user” or “you”). Please note that by browsing the Website, you acknowledge that you have read and agree to be bound by the following documents: this Legal Notice, our Privacy Policy, and our Cookie Policy. If you do not agree with any of these texts, you should not access or use the Website. The original version of this Legal Notice has been drafted in Spanish. However, Maisa Inc. may, as a courtesy, provide users with versions of this Legal Notice in other languages (for example, in English). In case of contradiction between versions, the Spanish version will prevail. Conditions of access and use of the website Access to and use of the Website is only permitted for individuals eighteen (18) years of age or older. Access to and use of the Website do not require the creation of a user account. However, in the future, Maisa Inc. may incorporate restricted sections or functionalities that do require user registration. Intellectual and industrial property Maisa Inc. holds the intellectual and industrial property rights over the Website and all its related elements. This includes, for example: All rights to the source code, object code, interface, databases, and other elements of the Website. All content on the Website (images, texts, videos, etc. ). All rights to the trademarks, trade names, and other distinctive signs of Maisa Inc. Users are not authorized to reproduce, distribute, publicly communicate, or transform the Website or its contents. By way of example, this means that users may not extract or reuse, in whole or in part, the information available on the Website, regardless of whether the extraction is done through automated techniques (screen-scraping, bots, spiders, etc. ) or manually. Permitted uses of the website As a user of the Website, you declare and warrant that you will make appropriate use of it. The following list includes, for example, some of the commitments you undertake:You will not use the Website to transmit or install viruses or other harmful elements. You will not attempt to access restricted sections of the Website or its systems and networks. You will not try to breach the security or authentication measures of the Website. You will not replicate or reverse engineer or decompile the Website (except in cases where the law expressly authorizes it). You will not engage in abusive use of the Website or use it in a way that could cause saturation of the Website. You will not use the Website to extract information that allows you to offer a product or service (analog or digital)... --- - Published: 2026-03-12 - Modified: 2026-04-01 - URL: https://maisa.ai/careers/ We are building the biggest global
employer of Digital Workers A workforce that will define the next century. View open roles The team behind Maisa David and Manu lead Maisa with a shared passion for building practical AI. Coming from engineering and product backgrounds, they focus on turning complex ideas into tools teams can actually use. They believe great technology comes from curiosity, collaboration, and solving real problems that matter. How we collaborate and build We’re curious, rigorous, and pragmatic. Some of us are just starting out; others have already built companies, products, and teams. What we all share is the same obsession: making AI useful, applied, and transformative. Our technical mindset and approach We drink coffee a little too often. We debate models, architectures, and prompts. We read papers, test tools before they’re mainstream, and celebrate when something finally works in production. Who you’ll grow and build with Behind every great journey is a team that supports, challenges, and inspires you. Here, you'll work alongside people who believe in collaboration, share knowledge openly, and help each other move forward every day. Maisa’s Values Our Values are the foundation of our culture and guide every decision we make. As you begin your journey with us, we hope you'll embrace these principles and see them reflected in your daily work and interactions with the team. Pioneers Don’t Have a Playbook We're building something that's never been built and there's no manual for this! We figure it out, improve as we go, and learn quickly from what doesn't work. Clarity Creates Impact We hold ourselves to the same standard. We’re clear on priorities, honest about what we know and what we don’t, and we share information openly. Team Wins We trust each other from day one. We win collectively, we create room for people to shine and grow, and we care about the people around us. We Welcome Pressure Our work has real impact, internally and externally. We own it, solve it, deliver it, and learn from it. We don’t panic... we navigate the storm. Customer Obsessed We’re relentless about their success. That’s why we do the hard things, care about the details, and take pride in what we deliver. Our interview process 1. Getting to know you An introductory conversation to learn about your background, motivations, and alignment with Maisa’s mission, culture, and values. 2. First interview A deeper discussion about your experience, key achievements, and how you approach challenges and decision-making in your field. 3. Technical test A practical exercise to assess your problem-solving skills and how you apply your expertise to real-world business scenarios. 4. Final Interview A final conversation to explore team fit, mutual expectations, and how you can contribute to Maisa’s long-term growth. 5. Decision and feedback We’ll share our decision promptly and provide clear, thoughtful, and constructive feedback on your application. Compensation & Rewards As an early-stage company, we've built compensation and benefits around one principle: shared success. Competitive salaries, real equity so everyone has skin in... --- - Published: 2026-03-03 - Modified: 2026-03-27 - URL: https://maisa.ai/ai-hallucinations/ AI Hallucinations AI hallucinations occur when models generate plausible but false information. Learn why they happen and how to reduce them for more reliable AI outputs. https://youtu. be/xE9PmgSfPD8 What are AI Hallucinations? An AI hallucination happens when a generative AI model produces information that sounds correct but is factually wrong. The output reads fluently, uses the right tone, and might even cite sources. But the underlying content is fabricated. In short, the model is optimizing for plausibility, not accuracy. The term borrows from human psychology, but the mechanism is completely different. A person who hallucinates perceives something that isn't there. An AI that hallucinates generates something that was never there, and presents it with full confidence. Why Do AI Hallucinations Happen? Large language models don't understand what they're saying. They predict the next word in a sequence based on patterns in their training data, which is billions of examples of human text. When a model hits a question it wasn't explicitly trained on, it doesn't stop and say "I don't know. " It fills the gap with whatever sounds right. That's the core issue. These models are built to sound convincing, not to be accurate. A confidently written paragraph about a court case that never happened looks identical to one about a real case. The model can't tell the difference, and unless you check, neither can you. How to mitigate hallucinations There are several strategies to mitigate hallucinations in AI systems’ output What Actually Reduces Hallucinations? There's a common assumption that bigger models hallucinate less. That's partially true, but it misses the point. What matters more than size is how the model was trained, what data it learned from, and what guardrails sit around it. Techniques like reinforcement learning from human feedback (RLHF) teach models to favor accurate, grounded responses over fluent-sounding guesses. Better data curation removes contradictions and low-quality sources from training sets. And frameworks like RAG (covered in more detail below) give models access to verified external information when generating a response, rather than forcing them to rely on memory alone. Scale helps. Larger models can hold more nuanced representations of knowledge. But scale without these other improvements just produces a model that's better at sounding right while still being wrong. How to Reduce AI Hallucinations Hallucinations can't be fully eliminated, but they can be reduced significantly. Some of this is on the user side, some on the developer side, and the most effective approaches combine both. Writing Specific Prompts: The more context and detail you give a model, the less room it has to guess. Vague or open-ended prompts are an invitation for fabrication. Tell it what you need, what format you want, and what constraints apply. Provide reference material. If you give an AI specific documents, datasets, or sources to work with, it will ground its output in that material rather than falling back on pattern-matching from training data. Constrain the model's behavior. Models should be configured to flag uncertainty rather than fill gaps. If a... --- - Published: 2026-03-03 - Modified: 2026-04-14 - URL: https://maisa.ai/ai-computer/ AI Computer An AI Computer is a new computing paradigm, where AI acts as the core orchestrator. It manages tools, data, and tasks to deliver real outcomes, not just answers. What is an AI Computer? An AI Computer is a computational system designed around artificial intelligence as its central orchestrator. At its core lies an AI orchestrator or kernel, responsible for managing and coordinating all activities within the system. Consider this analogy: traditional computers rely on kernels, like Windows or Linux, to manage hardware and software seamlessly. Similarly, the AI Computer uses an AI kernel to autonomously coordinate tools, modules, and real-time data sources, effectively handling tasks and workflows without continuous human oversight. In essence, just as a computer kernel abstracts complex processes to make computing efficient and accessible, an AI Computer abstracts the complexity of AI-driven tasks, orchestrating various tools and processes to deliver outcomes aligned precisely with user objectives. Why the AI Computer? Today’s AI systems come with common frustrations: they can produce incorrect or entirely fabricated answers (known as hallucinations), struggle with maintaining adequate context, and often require complicated setups involving integrations, data flows, and prompt engineering. These issues can make AI unreliable and difficult to work with effectively. The AI Computer addresses these problems head-on:Reliability: Rather than generating standalone answers, the AI Computer orchestrates tasks between tools, processes, and calculations, using clear, code-based steps. This means any error can be traced and understood through a transparent log of actions, avoiding the “black box” uncertainty common in other AI systems. Context Management: AI needs relevant context to produce accurate results, yet most AI models have strict limits on how much context they can handle at once. The AI Computer solves this by incorporating a dedicated memory module, intelligently selecting and providing only the necessary information at each step—much like how traditional computers use RAM and cache to efficiently manage data. Simplified Complexity: Just as your computer’s operating system handles memory management, hardware interactions, and other complex tasks behind the scenes, the AI Computer abstracts away AI-related complexities. Users simply define their objectives, specify tools and information, and let the AI Computer manage the execution seamlessly. Key capabilities Real-Time Data Integration Continuously pulls current data from APIs, web services, and databases, ensuring your business decisions are always based on the latest information Limitless Context Management Intelligently organizes and accesses memory, making sure the AI always uses the precise context needed at every step. Transparent Reliability Executes tasks through clear, step-by-step processes, leaving a detailed trail. This transparency prevents errors and removes uncertainty. Autonomous Orchestration Understands your goals, automatically choosing and coordinating the best tools, models, and processes to complete tasks effectively and independently. Maisa KPU The KPU is our AI Computer, built from our research to orchestrate tools, data, and workflows clearly and effectively based on user intent. It serves as the technological foundation for Maisa Studio and our Digital Workers, enabling automation of complete business processes seamlessly and transparently. At the heart of our mission is making... --- - Published: 2026-03-03 - Modified: 2026-04-14 - URL: https://maisa.ai/agentic-process-automation/ Agentic Process Automation Advanced automation powered by AI Agents to handle complex, dynamic business processes What Is Agentic Process Automation? Agentic Process Automation (APA) is an advanced automation approach that leverages AI agents, to autonomously handle and execute complex business processes requiring cognitive abilities such as interpretation, reasoning, and decision-making. Unlike traditional automation methods reliant on predefined rules or scripts, APA utilizes intelligent AI agents capable of understanding unstructured data, interpreting human language, and dynamically adapting their approach as circumstances change. APA systems are characterized by being:Autonomous: Agents independently determine how to achieve objectives without continuous guidance, reducing the need for detailed scripting. Goal-driven: Users define desired outcomes, and agents automatically identify and execute optimal paths to achieve them. Context-aware: Agents adjust in real-time to handle unexpected situations and exceptions smoothly, maintaining efficient workflows. APA enables businesses to automate tasks that were once too complex or nuanced for traditional automation, significantly expanding the possibilities of process automation. Why Agentic Process Automation? Agentic Process Automation brings flexibility and intelligence to process automation, making it possible to automate tasks that once required human judgment. Handles higher complexity APA goes beyond simple, repeatable tasks. It interprets unstructured information, reasons through ambiguity, and adapts in real time to shifting conditions. Flexible and efficient to scale With a goal-based setup, there’s no need to define every step in advance. This reduces setup time, lowers maintenance overhead, and makes it easier to scale automation alongside evolving processes and needs. Continuously adapts and improves APA agents learn from feedback and evolve over time. They adapt to context, handle exceptions, and refine their performance with each execution. How APA Works Using Agentic Process Automation is simple and adaptable. Instead of hardcoding every step, you define what you want to achieve, and the system figures out how to get there. 1. Define the goal Start by describing the outcome you want. Add any relevant instructions, tools, or context the system should use to get the job done. 2. Run the automation The agent plans and executes the necessary steps using the available tools and data. It adapts along the way, handling unexpected inputs or changes without manual intervention. 3. Improve over time The system learns from each execution. With every run, it becomes more efficient, more accurate, and better aligned with how work actually happens. Comparison with Traditional Automation Traditional automation tools are great at executing simple, repetitive tasks—especially when everything follows a predictable path. But most real-world processes aren’t that clean. They involve exceptions, unstructured inputs, and decisions that don’t fit into fixed rules. This is where Agentic Process Automation stands apart. Instead of automating individual tasks with hard-coded logic, APA enables full processes to be handled end-to-end, even when they’re complex, unpredictable, or involve judgment. APA takes a different approach. It understands the broader context, adjusts on the fly, and makes informed decisions at each step. Traditional tools can’t do that. This shift opens up new possibilities. It makes it possible to automate workflows that were previously... --- - Published: 2026-02-27 - Modified: 2026-04-14 - URL: https://maisa.ai/ai-agents/ Digital AI Agents AI Agents explained: what they are, how they work, and why they matter for automation. What are AI Agents AI agents are software systems that use AI to pursue goals and complete tasks. AI is at the core of how agents work. It lets them understand goals, reason through tasks, and make decisions based on context. They don’t just follow instructions. They adapt to the context around them. They also take action. AI agents can connect to tools and systems to do things like send an email or update a database. The term AI agent is used in many ways today, and the market is full of noise. Some describe any system that includes AI in part of the process as an agent. We define AI agents as systems with AI at their core—systems that are objective-driven, capable of reasoning, and able to decide how to accomplish tasks. Workflows with agent-like behavior: These follow predefined paths but may include AI models or tools in some steps. The logic is fixed, and outcomes are limited to what’s been planned. AI Agents: These are systems where language models dynamically direct their own processes and tool usage, maintaining control over how they accomplish the task from start to finish. AI Agents vs AI Assistants We’re all familiar with AI assistants. Tools like ChatGPT or Copilot help us write, summarize, or answer questions. They’re helpful, but they rely on us to guide them, one prompt at a time. AI agents are a step further. They’re not just responding. They’re acting. Agents can make decisions, use tools, and complete tasks on their own, without being told what to do at every step. AI Agent AI Assistant Purpose AI Agent Acts on its own to complete tasks and reach goals AI Assistant Helps users by following instructions or prompts Capabilities AI Agent Can handle complex tasks, make decisions, adapt, and learn over time AI Assistant Provides answers, suggestions, or simple actions based on input Interaction AI Agent Proactive and goal-driven AI Assistant Reactive and prompt-based How AI Agents Work AI agents work by combining different parts that let them reason, act, and in some cases, learn from experience. The way these parts are used can vary depending on how the agent is designed. 1. AI Model At the center is usually a language model. It’s what lets the agent understand a goal, break it into steps, and make decisions along the way. This is the reasoning engine. It plans, reacts, and adjusts based on the context. 2. Tool Access To take action, agents rely on tools. These can include APIs, databases, or other software. The model decides what to use and when, depending on the task. This is how agents move from planning to actually getting things done. Deploy & Learn After validation, the Digital Worker begins handling tasks, improving over time based on operational feedback and real-world experience. Capabilities of AI Agents AI agents can do more than just respond to prompts.... --- - Published: 2026-02-27 - Modified: 2026-04-17 - URL: https://maisa.ai/agentic-ai/ Agentic AI The shift from reactive AI to proactive, autonomous agents What is Agentic AI ? Agentic AI refers to AI systems that can decide and act on their own to reach a goal. These systems decide how to act, plan the necessary steps, and execute them independently. This represents a shift from reactive to proactive systems. Reactive systems respond to each input with an independent output, like ChatGPT answering individual questions. Proactive systems are goal-oriented. They work toward objectives on their own, adapting their approach as needed. Agentic AI is the capability behind truly autonomous AI agents. But many systems labeled as “AI agents” in the market don’t exhibit full agentic behavior. They follow pre-scripted logic or fixed workflows. In contrast, agentic systems let the AI itself drive both planning and execution, continuously adapting its approach to reach the intended goal. Key Capabilities Agentic systems can take various forms, yet certain core capabilities characterize how these systems operate: Autonomy Agentic AI exhibits goal-oriented behavior, completing full end-to-end workflows without constant human direction. Once given an objective, it determines and executes the necessary steps independently. Adaptability When conditions change or obstacles appear, these systems adjust their strategy dynamically. They don’t fail when the unexpected happens; they find alternative paths forward. Handling complexity Agentic AI manages multi-step tasks that require reasoning and decision-making. It navigates through interconnected processes, making choices at each stage to progress toward the goal. How it works Agentic AI usually combines several mechanisms to enable autonomous action. 1. Reasoning At the core, these systems use reasoning capabilities, often powered by large language models. They interpret goals, understand context, and make decisions toward achieving the objective. This reasoning layer determines what needs to be done and evaluates the best approach given current circumstances. 2. Tool and Data Access To act on their decisions, agentic systems connect to APIs, software, and databases. This allows them to gather necessary context, retrieve information, and take concrete actions in external systems. 3. Orchestration These systems orchestrate all the components needed to reach a goal: information, tools, and sequential steps. They coordinate multiple processes, manage dependencies between tasks, and ensure each action builds toward the intended outcome. Benefits of Agentic AI Agentic AI offers practical advantages that directly impact operations. Efficiency These systems reduce operational bottlenecks and eliminate repetitive manual work. By automating entire workflows rather than individual tasks, they streamline processes that previously required constant human intervention. Scalability Agentic AI handles increasingly complex processes without proportional increases in resources. As operations grow or requirements become more sophisticated, these systems adapt and manage the additional complexity without requiring equivalent expansion in staffing or infrastructure. Challenges While powerful, agentic AI systems present distinct challenges that organizations must address. Reliability These systems face risks like hallucinations or compounding errors across multiple steps. When AI makes sequential decisions autonomously, a single error early in the process can cascade through subsequent actions, potentially amplifying the impact. Control Organizations need guardrails to balance autonomy with oversight. The challenge... --- - Published: 2026-02-24 - Modified: 2026-04-20 - URL: https://maisa.ai/digital-workers/ Digital Workers AI Agents built for business processes that adapt, collaborate, and operate with full accountability and transparency https://youtu. be/XwZCMHUtMNg What are Digital Workers? Digital Workers are AI Agents built to execute tasks within business processes. They adapt to changing conditions, make decisions, and interact with both teams and tools to complete work. Unlike traditional automation, Digital Workers operate with clear goals and follow the logic of the process. They handle complex tasks that require reasoning, communication and judgement while keeping every step traceable. They are built for operational environments where work changes often and where traditional automation falls short. Why Do Businesses Need Them? Most business processes mix predictable steps with those that demand interpretation, validation, or decision-making. AI Digital Workers can automate these processes while preserving control, adaptability, and auditability. Traditional automation tools follow fixed rules, failing when work demands judgment and flexibility. Conventional AI solutions could process complex information but they act as “black boxes”, making it difficult for organizations to trust or audit their outputs. Digital Workers solve both problems. They adapt to real operating conditions, follow business rules, and leave a transparent trail of their decisions. This allows companies to automate work that was previously too variable, too risky, or too costly to automate. Key Capabilities of Maisa Automated Workers Our Digital Workers are built for enterprise processes and the operational demands that come with them: traceability, workflow awareness, adaptability, and effective collaboration. Traceability and accountability Every action and decision is captured in a detailed execution log, what we call “Chain of Work”. Teams can review how data was interpreted, which rules were applied, and why a decision was reached. This level of visibility makes the automated work fully auditable. Works with teams and stakeholders Digital Workers operate within business workflows, knowing when to seek approval, escalate issues, or notify the right people. They reinforce business rules rather than working around them. Adaptive problem-solving Instead of following static scripts, Digital Workers pursue goals. When conditions change or information is missing, they evaluate alternatives and select the path that gets the task completed correctly Understand business data Most processes rely on information scattered across systems, emails, documents, and databases. Digital Workers can interpret both structured and unstructured data and use it to drive decisions or trigger further actions. How setting up a Digital Worker looks like Setting up a Digital Worker is simple and doesn’t require technical expertise. Domain experts, those who know the process best, can configure it directly. Describe the work The user defines the goal of the task in natural language, and the Digital Worker proactively asks for the details it needs. At this stage, the user provides access to relevant data sources, tools, and integrations. Testing & Refinement The Digital Worker operates in review mode, allowing teams to evaluate its decisions and refine workflows through feedback in natural language before implementation. Deploy & Learn After validation, the Digital Worker begins handling tasks, improving over time based on operational feedback and real-world experience.... --- - Published: 2026-02-18 - Modified: 2026-03-22 - URL: https://maisa.ai/research/ November 26, 2024 Introducing Vinci KPU Introduction On March 14, 2024, at Maisa AI, we announced our AI system to the world, enabling users to build AI/LLM-based solutions without worrying about the inherent issues of these models (such as hallucinations, being up-to-date, or context window constraints) thanks to our innovative architecture known as the Knowledge Processing Unit (KPU). In addition to user feedback, the benchmarks on which we evaluated our system demonstrated its power, achieving state-of-the-art results in several of them, such as MATH, GSM8k, DROP, and BBH— in some cases, clearly surpassing the top LLMs of the time. Vinci KPU Since March, we have been proactively addressing inference-time compute limitations and scalability requirements, paving the way for seamless integration with tools and continuous learning. Today, we are excited to announce that we have evolved the project we launched in March and are pleased to present the second version of our KPU, known as Vinci KPU. This version matches and even surpasses leading LLMs, such as the new Claude Sonnet 3. 5 and OpenAI’s o1, on challenging benchmarks like GPQA Diamond, MATH, HumanEval, and ProcBench. What’s new on the Vinci KPU (v2)? Before discussing the updates in v2, let’s do a quick recap of the v1 architecture. KPU OS ArchitectureOur architecture consists of three main components: the Reasoning Engine, which orchestrates the system’s problem-solving capabilities; the Execution Engine, which processes and executes instructions; and the Virtual Context Window, which manages information flow and memory. In this second version, we’ve made significant improvements across all components:Reasoning Engine Improvement: We have enhanced the KPU kernel, furthering our commitment to positioning the LLM as the intelligent core of our OS Architecture. This advancement allows for more sophisticated reasoning and better orchestration of system components. Execution Engine Enhancements: We have successfully integrated cutting-edge test-time compute techniques and made the execution engine more robust, secure, and scalable. This ensures reliable performance while maintaining high-security standards for tool integration and external connections. Virtual Context Window Refinements: We have refined our Virtual Context Window through improved metadata creation and LLM-friendly indexing. This enhancement optimizes how information flows through the system and lays the groundwork for unlimited context and continuous learning capabilities. KPU Architecture Benefits What makes these results particularly significant is that they’re achieved by our KPU OS, acting as a reasoning engine, which focuses on understanding the path to solutions rather than providing answers. As main benefits, we can highlight:Model Agnostic Architecture (Better base models, better performance)Full multi-step traceability:configurable observability: Debug mode, visual representation, et. al. Provides better human-in-the-loop and over-the-loop control. Mitigate, almost fully eliminates, hallucinations:While this approach minimizes AI-generated inaccuracies, it may still encounter issues like errors in tool execution, incorrect data sources, or suboptimal approaches to solving the problem. Lower Latency to resolve problems than other systems in the market. Cost-efficient (up to 40x times cheaper than RAG, reasoning engines and Large Reasoning Models). Fully flexible and customizable with out-of-the-box functionalities: Unstructured data management, tools integrations, data processing... Autonomous execution with self-recovery/self-healing. It... --- - Published: 2026-02-18 - Modified: 2026-04-17 - URL: https://maisa.ai/use-cases/ Agentic AI Use Cases From compliance-heavy processes to everyday workflows, Maisa Digital Workers deliver traceable, auditable, and secure automation that adapts to the needs of every industry. Banking & Financial Services Engineering & Infrastructure Insurance Manufacturing All Industries --- - Published: 2026-02-17 - Modified: 2026-04-01 - URL: https://maisa.ai/manage-your-digital-workforce/ Manage your Digital Workforce with Maisa Studio Everything you need to deploy, monitor, and improve Digital Workers with confidence in one platform, Maisa Studio. Contact Sales Deployment on your terms Choose the setup that works best for your organization. From fully managed On Subscription, to On-prem/Private Cloud deployments, built in for scale. On-prem/Private Cloud or On Subscription Run Digital Workers in your environment or through our hosted platform. API generation Each worker automatically exposes an API, making it easy to share across teams. Integrate with your UI Embed Digital Workers into your own interfaces for a seamless employee experience. Data Security, Governance & Compliance Ensure every Digital Worker operates within strict enterprise-grade controls. Maisa Studio protects sensitive information, enforces governance policies, and maintains compliance across all data flows. Data access control Define precisely what data each Digital Worker can access or modify, keeping sensitive information secure. Data governance & compliance requirements Meet regulatory and internal standards through built-in rules, auditability, and controlled data handling. Traceability of data used Track every piece of data a worker uses, ensuring full visibility and accountability in every decision and action. Integration with all your systems, including legacy Maisa Studio connects Digital Workers to every part of your enterprise ecosystem, from decades-old infrastructure to modern SaaS platforms, ensuring uninterrupted operations wherever your data and processes live. Legacy systems, RDA, Mainframe and SFTP Digital Workers connect to mission-critical systems such as Mainframe environments, legacy applications, RDA-based desktop tools and secure SFTP file exchanges without requiring modernization or system changes. APIs or no APIs (Browser) Workers integrate securely through APIs when available and operate reliably even when no APIs exist by interacting directly with the browser. Unified connectivity across the ecosystem Digital Workers move information across disconnected systems to eliminate fragmentation and create a single operational layer that brings all enterprise tools together. Model agnostic architecture for freedom of choice Maisa Studio allows you to use any AI model your organization prefers, whether it is commercial, open source or privately hosted. Digital Workers stay stable even as your model strategy evolves, ensuring long term flexibility and resilience. Use any model Run Digital Workers on commercial models, open source models or private models to match performance, security and cost requirements. Avoid vendor lock in Update or switch models without breaking your Digital Worker. Its logic, rules and behavior remain consistent regardless of the underlying model. Improve performance over time Test new models, compare outcomes and upgrade your AI stack safely without interrupting daily operations. Scalability and monitoring Maisa Studio is built to operate at enterprise scale, giving you full visibility and control over thousands of Digital Workers running across teams, processes and systems. As your workforce grows, oversight, reliability and governance remain effortless. Operate at massive scale Deploy and manage thousands of Digital Workers simultaneously, each running independently while following consistent governance, security and operational standards. Centralized monitoring and control Use a unified control tower to manage rules, permissions and guardrails for every Digital Worker, ensuring all activity... --- - Published: 2026-02-17 - Modified: 2026-04-17 - URL: https://maisa.ai/agentic-insights/ Resources AI in the Enterprise Digital Workers News Research and Tech All --- - Published: 2026-02-17 - Modified: 2026-04-01 - URL: https://maisa.ai/trust-the-outcome/ Trust the Outcome with Reliable Digital Workers Everything you need to deploy, monitor, and improveSo your teams can trust the outcome and scale confidently. Maisa Studio ensures every Digital Worker acts transparently, predictably, and within guardrails — giving your organization confidence in every result. Contact Sales Hallucination-resistance Reduce the impact of AI mistakes with a step-by-step, code-based approach. Combining LLM adaptability with our propietary Knowledge Processing Unit technology, Digital Workers deliver reliable outcomes with safeguards against failure. Step-by-step code execution Every action follows a code-driven path for consistency and accuracy. Self-healing Workers detect and resolve issues automatically, minimizing downtime. No false positives Built-in checks eliminate unnecessary errors and noise. Auditability & transparency Every decision is traceable from start to finish, with clarity built into the process so teams and regulators alike can understand and trust the outcome. Audit trail Complete logs for oversight and compliance. Linked business logic and rules Connect company rules directly to actions for transparent execution. Explainability Clarity from the beginning, every step can be understood and reviewed. Verification built-in Ensure accuracy before deployment. Maisa Studio’s pre-assurance layer validates the data and results, reducing risk and saving verification time. Data integrity Confirm that inputs and references are correct. Pre-deployment checks Outcomes are validated before being released. Reduced verification time Built-in assurance speeds up trusted execution. Enterprise-grade security & guardrails Protect your business with the same standards you expect from mission-critical enterprise systems. Cybersecurity & risk mitigation Advanced protections safeguard sensitive data. Governance Built-in controls align execution with organizational policies. Compliance Meet regulatory standards without added complexity. Explore every part of the platform From building Digital Workers to managing your digital workforce and trusting the outcomes. Create, scale, and govern AI workers with confidence Product Overview Discover the One control plane, for mission-critical Digital Workers. Design, deploy, observe and improve Digital Workers running your most critical workflows. Governed by design Observable by default Built for mission-critical scope Learn More Manage your Digital Workforce All the challenges of running multiple Digital Workers in production are solved in one place. Assign roles, responsibilities, and access levels Scale effortlessly across teams, regions and departments Maintain full oversight with enterprise-grade governance and auditability Learn More Build a Digital Worker Business teams onboard Digital Workers into existing processes just like they would new colleagues, fast, guided and compliant. Natural language creation and feedback Connect effortlessly to your tools and systems Digital Workers follow your business rules and logic like a member of the team Learn More Onboard your first Digital Worker today Ready to Transform How Your Business Works? Join industry leaders in regulated sectors who trust Maisa to automate their most critical processes Schedule a Demo --- - Published: 2026-02-17 - Modified: 2026-04-01 - URL: https://maisa.ai/platform-overview/ One control plane for mission-critical Digital Workers  Design, deploy, observe and improve Digital Workers running your most critical workflows. Governed by design Observable by default Built for mission-critical scope Contact Sales Watch Demo Studio Platform Overview A unified platform to build, manage, and govern Digital Workers, with full visibility, security, and enterprise-scale control. How it works Onboard, test, deploy, and monitor Digital Workers across their entire lifecycle, all within a single, governed platform. Onboard Set up the digital worker with the required access, context, and configurations so it’s ready to operate within the organization. Test Validate the digital worker’s behavior and performance in controlled scenarios to ensure it meets expectations before going live. Deploy Release the digital worker into the production environment where it can start delivering value in real workflows. Monitor Continuously track performance, reliability, and outcomes to optimize behavior and ensure ongoing compliance and efficiency. https://maisa. ai/wp-content/uploads/2026/02/Studio_trimed-ezgif. com-gif-maker. webm What makes us truly reliable Hallucination-Resistant Verifiable, deterministic execution that minimizes errors and builds trust in mission-critical processes. Write another line to complete the copy. Natural Language Onboarding Enable process owners to design, launch, and refine Digital Workers using plain language or even voice. Write another line to complete the copy. Organizational Memory Capture best practices and undocumented expertise into an updated knowledge base. Write another line to complete the copy. Seamless Integrations Connect Digital Workers with 300+ tools, APIs, cloud platforms, and legacy systems without disruption. Write another line to complete the copy. Self-Healing & Adaptability Automatically adjusts to variations, exceptions, and real-world complexity while maintaining accuracy. Write another line to complete the copy. Enterprise-Grade Security Zero-trust architecture, encryption, and GRCS controls to meet the highest compliance standards. Write another line to complete the copy. Explore every part of the platform From building Digital Workers to managing your digital workforce and trusting the outcomes. Create, scale, and govern AI workers with confidence Build a Digital Worker Business teams onboard Digital Workers into existing processes just like they would new colleagues, fast, guided and compliant. Natural language creation and feedback Connect effortlessly to your tools and systems Digital Workers follow your business rules and logic like a member of the team Learn More Manage your Digital Workforce All the challenges of running multiple Digital Workers in production are solved in one place. Assign roles, responsibilities, and access levels Scale effortlessly across teams, regions and departments Maintain full oversight with enterprise-grade governance and auditability Learn More Trust the Outcome Every workflow is reliable, auditable, and aligned with enterprise standards. Hallucination resistant by design Automated results and verification for every workflow Clear ROI insights that prove impact and value Learn More Onboard your first Digital Worker today Ready to Transform How Your Business Works? Join thousands of companies already using Maisa to automate workflows, gain insights, and accelerate time-to-value. Schedule a Demo --- - Published: 2026-02-17 - Modified: 2026-04-01 - URL: https://maisa.ai/build-a-digital-worker/ Build Trustworthy Digital Workers with Maisa Studio Maisa Studio is the Agentic AI platform that empowers business users to create, test and deploy trustworthy Digital Workers ready for production. Contact Sales Onboard your Digital Worker Bringing a Digital Worker to life is just like onboarding a new employee. Business teams explain the goals in natural language, provide access to the right tools, and share the data and rules that guide how work gets done. Build in natural language Set up Digital Workers simply by describing their in plain language with no technical skills required. Test & feedback Let the employees who know their jobs best provide feedback to refine accuracy and performance. Deploy & share Roll out workers across teams and departments, ensuring consistent adoption throughout the organization. Maisa Studio connects seamlessly to your stack From modern SaaS apps to legacy platforms, your Digital Workers plug into the systems you already use, unifying fragmented data and processes without disrupting workflows. 300+ integrations Choose from hundreds of ready-made connections to your current tools. Access legacy systems Enable workers to interact with older systems without needing costly updates. Model-agnostic capability Maisa Studio works with multiple AI models, letting you choose or switch while keeping consistent performance. Business teams create their own Digital Workers Empower the people who know the work best. With Maisa Studio, business users can design and adapt Digital Workers directly, without relying on technical expertise. No process mapping required The worker autonomously defines the best path to achieve its goal. Explainability from the beginning Instructions are transparent, so your teams can understand what the Digital Worker’s plans are. Self-healing & feedback loop Digital Workers self-correct while learning from team input to maintain reliability and improve over time. Following your organization’s wayof working By combining structured business logic with your team’s tacit knowledge, Digital Workers continuously improve performance based on your company expertise. Upload your rules Incorporate your company-specific policies and business logic directly. Learn from execution Every task completed helps the worker what worked and what didn’t for future performance. Capture undocumented knowledge Document and embed the informal know-how that lives only in your team’s heads. Digital Workers get better from experience Maisa Studio ensures your Digital Workers continuously improve over time. With built-in learning loops, they adapt to feedback, self-correct, and refine performance on every task. Continuous learning Workers evolve by leveraging prior executions to enhance accuracy. Feedback Employees provide direct input, guiding improvements where they matter most. Self-healing Digital Workers detect issues and resolve them autonomously to maintain reliability. Explore every part of the platform From building Digital Workers to managing your digital workforce and trusting the outcomes. Create, scale, and govern AI workers with confidence Product Overview Discover the One control plane, for mission-critical Digital Workers. Design, deploy, observe and improve Digital Workers running your most critical workflows. Governed by design Observable by default Built for mission-critical scope Learn More Manage your Digital Workforce All the challenges of running multiple Digital Workers in production are solved... --- - Published: 2026-02-11 - Modified: 2026-04-21 - URL: https://maisa.ai/ Gartner names Maisa a Set Diamond Front-Runner in Emerging Tech: AI Vendor Race - Startups to Watch in Agentic AI. Read the Full Story Hallucination-Resistant Digital Workers for process automation for regulated industries built by business teams delivering ROI pre-built or customized Maisa enables business users in regulated industries to deploy pre-built Digital Workers or build custom ones in natural language to automate processes end-to-end. Our proprietary KPU turns any LLM into deterministic, auditable execution, regulator-ready from day one. Schedule a Demo How Maisa works Trusted by Industry Leaders The Pragmatic Approach to AI Agents Productivity with Digital Workers Strategy BPO-like Approach AI agents designed as specialized digital employees One job, defined scope, measurable outcomes Structured execution aligned with business processes Proprietary Technology Hallucination-resistance Large + Small Model Combo Auditable. Deterministic Business users create digital workers with natural language Business Model Predictable pricing Like employees, not API calls. No unpredictable token usage or surprise costs Easy ROI calculation Named by Gartner® Across the AI Agent Race Reports A Front-Runner in Agentic AINamed a Set Diamond front-runner in Emerging Tech: AI Vendor Race, Startups to Watch in Agentic AI, listing Maisa among the leaders at the forefront of the agentic AI revolution. Read the full article The AI Race — Who Will Clients Buy AI Agents From? Maisa is listed by Gartner® as an AI Agent Management & Process Specialistin Tech Service Leaders: Decide Which BOAT to Bet On to Support Revenue and Margin Growth. Read the full article A Front-Runner in Agentic AI Emerging Tech Maisa has been named by Gartner® a Set Diamond front-runner in the Emerging Tech: AI Vendor Race – Startups to Watch in Agentic AI, spotlighting our work at the forefront of the agentic AI revolution. Read the full story Get more info Named by Gartner® Across the AI Agent Race Reports Read all the reports A Front-Runner in Agentic AINamed a Set Diamond front-runner in Emerging Tech: AI Vendor Race, Startups to Watch in Agentic AI, listing Maisa among the leaders at the forefront of the agentic AI revolution. Read the full article The AI Race — Who Will Clients Buy AI Agents From? Maisa is listed by Gartner® as an AI Agent Management & Process Specialistin Tech Service Leaders: Decide Which BOAT to Bet On to Support Revenue and Margin Growth. Read the full article Named by Gartner® Across the AI Agent Race Reports Read all the reports A Front-Runner in Agentic AINamed a Set Diamond front-runner in Emerging Tech: AI Vendor Race, Startups to Watch in Agentic AI, listing Maisa among the leaders at the forefront of the agentic AI revolution. Get more info The AI Race — Who Will Clients Buy AI Agents From? Maisa is listed by Gartner® as an AI Agent Management & Process Specialistin Tech Service Leaders: Decide Which BOAT to Bet On to Support Revenue and Margin Growth. Read the full story Named by Gartner® Across the AI Agent Race Reports A Front-Runner in Agentic AINamed a Set Diamond front-runner in Emerging Tech: AI Vendor Race, Startups to... --- - Published: 2023-09-01 - Modified: 2026-04-17 - URL: https://maisa.ai/about-us/ Manifesto https://youtu. be/H5jmK0uMIVI Computing a better future for AI It’s time again to make an important decision. Do you flip a coin, roll the dice, or ask the Magic 8-Ball? Or do you return to first principles, consider everything you’ve learned, and reason your way to a solution? We know what we would do. Which is why it feels a little strange that the services we’re increasingly looking to for answers are built around probabilities. Yes, we’re talking about AI: our latest and greatest technological innovation. For acts of creativity, AI’s fuzzy logic can be remarkable at helping us imagine new solutions. But in the complex world of business, we need a lot more accountability. We need airtight, traceable processes for getting to answers... as much as we need the answers themselves. We demand a new kind of common sense made for our new AI era. In short, we need evidence to justify important decisions like who gets a loan, which insurance claims are denied, what drugs get researched, or deciding who gets laid off. And with today’s AI, this evidence is abstracted into oblivion... and the risk of hallucinations threaten to make it all useless anyway. And so, less than 6% of corporations are actively using AI to do anything more than create question and answer bots. To make AI truly invaluable to businesses, we need a new way of thinking. Throughout history, humans have developed computational systems, formal logic, and scientific methods to verify our thinking. Can we do the same for AI? We believe the solution isn’t training larger models, illuminating their chain of thought, or implementing RAG systems, which are all still based on the same fundamental issues. So, we believe the path forward lies in a new kind of computing system that combines the creative problem-solving capabilities of AI with the determinism of traditional computational systems. An AI computer that shows its chain of truth, not just its chain of thought. In this world, AIs can be trusted to act as intelligent problem solvers executing tasks, not mysterious oracles giving inscrutable answers. To us, this is the key that will take AI into its next age of utility. When AI evolves from a black box... to an open book. Introducing Maisa Maisa is not an AI or an LLM, but a system for making them work better. It’s a new kind of automation tool for enterprises, data teams, builders, and tinkerers who are tired of chasing ghosts and trying to divine the meaning behind AI’s answers. A self-learning intelligence built on fact, not fiction. With Maisa, you can create accountable AI agents you can plug in to any part of your business. Define your desired outcome, set your goals, give them access to company knowledge and tools, and instruct them using natural language. Then, they approach problems by navigating all your knowledge, not just retrieving it, and processing information with a clear, auditable logic that’s easily traceable. Think of Maisa as the next evolution of... --- - Published: 2023-09-01 - Modified: 2026-03-31 - URL: https://maisa.ai/contact/ Reimagine how work gets done with Maisa Digital Workers See why Maisa customers move from pilot to production in record time. Our Digital Workers are built for regulated environments and designed to deliver outcomes from day one. Real results in live operations, not someday, but now. Director of Digital Transformation & InnovationWe have been very happy with Maisa’s adaptation to our needs and with how it has helped our team succeed in operationalising our standard operating procedures. It has been a collaborative experience in which, within a matter of weeks, we were able to transform semi-structured operational guidance into a fully functional Digital Worker. This enables us to manage our first AI-driven business operation and, as a by-product, gives us a written standard operating procedure that supports both alignment and business goals. 0 % average cost savings in AI operations after strategic optimization x 0 faster time-to-value from AI initiatives, turning pilots into measurable ROITrusted by Industry Leaders Get in touch Onboard your first Digital Worker today Ready to Transform How Your Business Works? Join thousands of companies already using Maisa to automate workflows, gain insights, and accelerate time-to-value. Schedule a Demo --- --- ## Posts - Published: 2026-03-30 - Modified: 2026-03-31 - URL: https://maisa.ai/agentic-insights/startups-to-watch-in-agentic-ai-gartner/ Maisa has been named as a Front Runner in Gartner’s “Emerging Tech: AI Vendor Race - Startups to Watch in Agentic AI” report, spotlighting the companies that are turning agentic AI into real enterprise impact globally. Recognising the Next Wave of Enterprise AI Gartner’s research evaluates 129 global startups building in the rapidly evolving agentic AI space, identifying those demonstrating strong market traction, differentiated technology, and the ability to deliver real enterprise value. Within this landscape, Gartner has identified an exclusive group of 4 global companies as “Set Diamonds”, which are Front Runners in the space. These are organizations whose solutions are already proving their value in real-world environments and are beginning to define the next generation of enterprise software. Maisa is proud to be included in this category. “Maisa enables business users in regulated industries such as banking to deploy prebuilt digital workers or build custom ones in natural language. Their proprietary Knowledge Processing Unit acts as an enterprise-grade OS on top of any LLM, turning predictions into deterministic code execution with full chain-of-work traceability, auditable and regulator-ready from day one. These digital employees are gaining rapid market momentum because their value is tied to outcomes, allowing business leaders to clearly identify use cases and calculate a direct ROI. ” The End of Single-Task Agents For years, enterprise automation meant isolated tasks, a bot that processes an invoice, a script that moves data from one system to another. Useful, but limited. Gartner's latest report signals that enterprises are now demanding more: complex, end-to-end process execution where AI doesn't just assist, but takes ownership of entire business workflows with accountability and reliability. "AI employees" are also mentioned as autonomous agents with clearly defined roles and domain-specific capabilities, built to deliver measurable, accountable outputs across the full length of a process. One of the biggest barriers to full process automation has always been fragmentation. Enterprises run on dozens of disconnected systems including ERPs, CRMs, legacy platforms, and modern SaaS tools, and the inability to connect them end-to-end has kept automation confined to isolated pockets of the business. Maisa's enterprise-grade OS connects to any modern application or legacy system, with or without APIs, removing the connectivity constraints that have historically kept automation fragmented and limited in scope. In production environments, this means Digital Workers operating across complete, end-to-end workflows rather than isolated steps, with the accountability and reliability that enterprise teams in legal, compliance, security, and IT require. With Maisa Studio, business teams can onboard Digital Workers by simply describing the job that needs to be done. No process mining required. Real Business Impact Happens in Production For many enterprises, AI has lived in a pilot phase for longer than anyone anticipated. The technology showed promise, but translating that promise into measurable, auditable business outcomes proved harder than expected. Gartner's report identifies traceable outputs, deterministic execution, and auditability by design as the defining characteristics of production-ready AI systems. These are the capabilities that allow organizations to deploy AI at enterprise scale with... --- - Published: 2026-02-13 - Modified: 2026-03-30 - URL: https://maisa.ai/agentic-insights/maisa-wins-deloitte-rising-star-award/ Maisa has been named winner of the Rising Star category at the Deloitte Technology Fast 50 Spain Programme, one of the country’s most prestigious recognitions for high growth technology companies. Recognizing High Growth Technology Leaders The Deloitte Technology Fast 50 Spain Programme identifies, recognizes, and showcases the fastest growing technology companies within Spain’s startup and scaleup ecosystem. The ranking distinguishes organizations that have achieved sustained and significant revenue growth over the past four years, driven by innovation, technology, and scalable business models. Beyond recognition, the Fast 50 Programme serves as a growth platform for participating companies. It facilitates connections with investors, corporations, institutions, and other key ecosystem players, while providing access to specialized expertise, media visibility, and valuable networking opportunities. The programme has become a benchmark within Spain’s entrepreneurial and technology landscape, aligning with Deloitte’s international Fast 50 initiatives and contributing to the strengthening of the innovation ecosystem. A Milestone for Market Trust In today’s market, bold claims around AI agents are everywhere. New platforms launch daily, promising autonomy, transformation, and disruption. But real trust in enterprise technology is not built on hype. It is earned through execution, customer impact, and independent validation. Winning the Rising Star category is meaningful because it represents recognition from the market itself. Leadership cannot be declared. It must be recognized by credible third party institutions that evaluate performance, growth, and scalability with rigor. Being recognized by Deloitte, a globally respected and independent authority, signals that Maisa stands apart in a crowded AI landscape. It validates not only our growth, but also the strength of our model and the tangible results delivered to enterprises. In a space often defined by experimentation and noise, this award reflects something more enduring: sustained traction, measurable business impact, and genuine market trust. Building the Future of Enterprise AI Execution As organizations move beyond experimentation and into production grade AI adoption, the need for traceable, scalable, and reliable execution systems becomes critical. Maisa is built to meet that challenge. We enable enterprises to execute complete processes, maintain full oversight, and scale with confidence. Being recognized by Deloitte as a Rising Star reinforces our conviction that enterprise AI must be more than impressive demonstrations. It must be accountable, auditable, and designed for long term operational value. We are proud to join the Deloitte Technology Fast 50 community and remain focused on building the infrastructure that allows enterprises to move from AI hype to AI execution, with trust at the center. --- - Published: 2026-01-12 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/how-maisa-digital-workers-actually-work/ Maisa Digital Workers take a different approach to traditional automation. Instead of encoding every possible path in advance, they generate and execute code at runtime, adapting to each case as it unfolds. Most enterprise automation covers the core process well enough. The exceptions are the problem. Judgment calls, documents in unexpected formats, fields that moved since last month, data that contradicts itself across sources. Workflow tools handle predictable sequences, but regulated industries rarely offer predictable sequences. Banking, insurance, trade finance: these run on variance. We built Digital Workers to handle that variance without requiring months of workflow engineering upfront. Onboarding a Digital Worker We think of setup as onboarding a new team member. Take a Digital Worker responsible for loan approvals. Let's call him Manolo. You describe the process you do in plain language. What steps you take manually, what counts as a complete application. What policies apply. What the acceptance criteria look like. Where he should be looking... Manolo converts this into executable instructions he can act on. You attach the knowledge he needs: underwriting guidelines, compliance requirements, internal documentation. You connect the systems: your CRM, document storage, credit bureau APIs, core banking. Standard integrations, nothing custom. Manolo then gets access to our KPU, the secure environment where he generates and runs code. Setup done. He can start processing. A case arrives A loan application comes in. It routes to Manolo. He reads the submission and figures out what he has, what's missing, and what to do first. Maybe that's extracting income data from uploaded pay stubs. Maybe it's calling an external API for a credit check. He writes the specific code for that action, not generic code, specific to this submission. The code runs inside the KPU. The output gets checked against the rules you defined during setup. If the pay stub shows income of €45,000 but the application form says €52,000, that discrepancy gets flagged and handled according to your policies. The verified result feeds into the next step. This continues until the process completes. Clean applications resolve fast. Messy ones with missing documents, inconsistent data, or unusual circumstances take more steps as Manolo works through each problem. The output is a decision with full supporting evidence. Every number ties back to a document. Every check is recorded. The Chain of Work We call the complete record of a case the Chain of Work. Each entry shows what the task was, what tools and sources it used, what it returned, what validations happened, and whether they passed. Inputs connect to outputs through explicit steps anyone can follow. This gives you something most AI systems cannot: reproducibility. Same input, same configuration, same output. When an auditor asks why an application was declined, you can show them the exact evidence and the exact rules that produced that decision. No reconstruction. No guesswork. Plenty of systems try to log what they do. The Chain of Work goes further by making the reasoning reproducible, not just falsely recorded. Running at scale... --- - Published: 2026-01-07 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/agentic-ai-cost-control/ Agentic AI is moving quickly from pilots into real production environments. That shift changes the cost story. What starts as a manageable line item for model usage often turns into a broader operational expense that includes orchestration runtime, tool infrastructure, scaling overhead, and observability. Many of these costs are not obvious in early demos because the drivers are architectural. If cost control is not designed in from the beginning, teams typically end up retrofitting guardrails while the business is already depending on the system. But surprise opex rarely comes from one place. In real enterprises, cost overruns typically emerge across two tracks that often accelerate at the same time. Pilot to Production As systems become reliable enough for real operations, costs expand beyond tokens into orchestration runtime, tools, observability, reliability engineering, and scaling overhead. Democratization without guardrails As access spreads across teams, usage scales faster than governance. Employees use general AI tools for quick wins without cost estimation, budgets, or limits, so small actions can create outsized spend. This article lays out what cost control looks like for agentic systems in real enterprises, why costs balloon when they are not planned early, which technical decisions most affect spend, and how a modern agent platform can address them without turning your organization into a finance only operation. Why agentic AI costs spiral when you don’t plan early The most common pattern is that organizations scale capability before they scale control. Early systems optimize for outputs and autonomy. They add more tools, more workflow steps, and sometimes more agents, without instrumenting what is happening per run and per use case. Democratization turns cost into a scaling problem, not a procurement problem When AI tools become widely available, spend scales with behavior rather than architecture alone. People treat AI as a silver bullet, iterating by trial and error and pushing larger inputs just to see if it works. A simple action can explode in cost. Uploading a long document such as a one hundred fifty page file into a general purpose assistant and repeatedly prompting over it can trigger large token usage across many attempts, especially when multiple people repeat the same pattern. Without cost estimation, budgets, and guardrails, organizations discover the unit economics only after adoption has already spread. General purpose assistants like ChatGPT and Copilot make this behavior easy. They are designed for maximum convenience and repeated usage, not enterprise wide cost attribution and enforcement. In practice, they rarely stop users mid-flow with warnings such as this will cost X dollars or with budget aware routing, because friction reduces usage. Enterprises then inherit the bill without the controls they would expect in any other production system. Latency and spend rise together As workflows become more complex, prompts grow, tool calls increase, and retries become more common. Execution time goes up, and infrastructure and token usage rise with it. If you do not intentionally decouple performance from cost, both deteriorate at the same time. Open source and frontier models still hallucinate, especially... --- - Published: 2026-01-07 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/why-maisa-is-not-an-n8n-like-tool-or-rpa-ai/ A different model for enterprise automation In almost every enterprise conversation, our sales team hears the same shortcut. So you are like n8n? It is a reasonable comparison point. Both platforms automate work across systems. Both can call tools and run code. Both can connect to the same operational stack of data sources, internal services, and SaaS systems. But when automation becomes business critical, enterprise buyers care less about the workflow builder interface and far more about the properties of the system underneath. They care about verifiable outcomes, resistance to hallucinations, traceability that holds up in audits, governance and security controls that scale, faster iteration without workflow sprawl, and ownership that does not collapse into a small group of specialists. That is where the comparison breaks down. The similarity that creates confusion is real. Both approaches use code to get work done. The difference is when that code is formed, how the process is defined, and what the system treats as proof. Those choices determine cost, risk, and speed once you move from prototypes into production. This article explains where the difference really comes from and why it matters for complex, exception heavy enterprise processes. A focused comparison on what enterprise buyers actually care about The real distinction is the unit of automation n8n is excellent at orchestration. You assemble a workflow graph at design time using nodes, conditions, retries, and integrations. Reliability is a function of how thoroughly you modeled the process and how well you handled exceptions inside the graph. That works extremely well when the process is stable and inputs are predictable. Maisa starts from a different premise. In Maisa Studio you define work in natural language at the level of objective, constraints, policies, and acceptance criteria. Then the KPU executes the work. The key point is when and how the workflow exists. With Maisa, the process is formed at runtime. The KPU generates executable steps, runs them in a controlled environment, observes the results, validates them against rules and invariants, and then decides the next step. The system is not following a fixed blueprint you had to anticipate in advance. It assembles and verifies the steps as each transaction unfolds, under the governance you define. This is not just a product design preference. It changes the economics of automation when the real world is messy. If your process looks like a stable integration diagram, a workflow graph is a natural fit. If your process looks like documents, exceptions, inconsistent formats, and edge cases, objective driven execution changes what is feasible. How you define the process determines who can own it In a workflow graph model, the process is defined as structure. Nodes represent operations. Conditions represent decisions. Branches represent exceptions. Retries represent resilience. The process is the graph. In an enterprise setting, that definition has consequences. When the process is encoded as a detailed graph, the people who can safely modify it are typically the people who understand the graph as a system. Over time, ownership... --- - Published: 2025-11-11 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/business-process-automation/ What is business process automation? Business process automation is the process of using AI technology to automate repetitive manual tasks and processes while maintaining efficiency, productivity, and accuracy. The goal of BPA is to streamline daily operations and allow businesses to operate smoothly. Automation of business processes can help companies in almost every field to achieve higher effectiveness and improve customer satisfaction and experience. Why is BPA important for the companies? Business process automation is not just the next trending topic. It is the new reality - adaptation to technology that will exist in the world, changing the old manual labor into new digital processes. Automation workflow helps in every step of the BPA by making it more transparent and accountable, reducing errors, improving turnaround times, and cutting costs. The 5 Types of Business Process Automation Task Automation This is the most basic type of business process automation tool. It automates repetitive, straight forward tasks that are time consuming and require no decision making in the process. What is task automation? Task automation uses no-code platforms or software like scripts to perform repetitive rule-based tasks. Task automation fields and examples Email management - You can use it to manage your emails by filtering, tagging and organizing them into specific folders. Finance - automatic reporting, invoicing and overdue reminders. Data management - moving information from a document to a database, or comparing information in different databases and flagging inconsistencies. Customer service - instant replay to common questions or assign task based on certain criteria. Task automation advantages Reduced errors - tasks are performed following strictly the process removing the manual labor and copy/pasting. Increased productivity - tasks are processed 24/7, no coffee breaks or distractions. Cost savings - improved performance and less errors mean lower corporate operational costs. Improved job satisfaction - employees can finally focus on meaningful tasks and move away from repetitive work. Workflow Automation Unlike task automation where individual tasks are executed, workflow automation deals with a chain of tasks to complete a business process based on predefined set of rules. What is workflow automation? The workflow automation makes sure a set of tasks are completed between human and software with minimal or no human interaction. Each task follows the next logical one in the process and only stops when external approval, consent or additional information is required. Workflow automation fields and examples Sales - user fills out a “Contact us” form, the workflow instantly checks for some additional information that is needed (ex. : company size, location) and assigns the lead to the correct sales personnel and updates the follow-up tasks in the CRM. Customer service - a task comes in, it is assigned to the right customer support representative but is not completed in the expected timeframe, a reminder is triggered and sent to a senior support specialist to ensure no complaint is left unsolved. IT Help desk - a ticket comes in, based on the category (ex. “Hardware problem”, “Software issue”) it is assigned to... --- - Published: 2025-10-24 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/agentic-ai-vs-rpa/ Enterprises are trying to automate everything today, and the technical landscape is being changed by Agentic AI and RPA (Robotic Process Automation). RPA works with exact, predefined instructions, while Agentic AI can aim to reach a goal by planning and executing a sequence of tasks with minimal human intervention. The potential of both approaches is huge. Many industries, including finance, insurance, healthcare, and customer service, are already adopting Agentic AI in their internal systems to solve complex problems. Both are trying to automate repetitive work and improve efficiency, but they achieve it in very different ways. What is Agentic AI? Agentic AI refers to an autonomous AI system. It can operate independently to achieve complex, long-term goals without constant human involvement. Agentic AI can understand its environment, reason, plan, act, and learn from its actions to become more efficient. How it works Goal intake: The AI collects the information it needs to understand the objective and explain it in simple language. Reasoning: The AI, powered by large language models (LLMs), analyzes data, plans all steps, and chooses the actions. Tool & data access: Connects with external APIs, software, and databases to collect information. Orchestration: Breaks down complex tasks into smaller ones for easier execution, planning their sequence to achieve the desired outcome. Action: Executes the task order, working across internal and external systems, tools, and even other AI agents. Strengths Agents manage end-to-end workflows. Adjusts constantly when processes shift or data changes. Scales to support complex, multi-step operations. Removes humans from repetitive manual work. Limitations Errors in one step can move through the process. Needs clear guardrails and human oversight to operate safely. Highly dependent on quality data. Requires transparency so organizations can track and understand decisions. What is RPA (Robotic Process Automation)? Robotic Process Automation (RPA) uses software bots to act like a human on a computer. It follows a predefined script-based path to complete tasks when working with digital systems. RPA systems allow enterprises to reduce errors and speed up operations while maintaining quality. How it works Process mapping: Every click, action, rule, and sequence itself is programmed in advance by IT teams. Execution: Bots replicate these steps across applications with speed, accuracy, and consistency. Scope: Ideal for large-scale, well-organized, and repetitive processes. For example, data entry, form filling, and transferring information between systems. Maintenance: Any change in process or interface requires updates to the scripts. Strengths Reliable for high-volume, repetitive, structured tasks. Reduces human error and increases speed in routine business processes. Widely adopted in enterprise operations like finance, HR, and back-office functions. Limitations Easily breaks when processes or systems change. Unable to process unstructured or dubious data. Dependent on IT teams for setup and maintenance. Limited scalability for complex or changing workflows. What are the differences between Agentic AI vs RPA? The two approaches reflect different philosophies of automation. Agentic AI focuses on adaptive goal achievement, while RPA focuses on the strict execution of predefined steps. Aspect RPA Agentic AI Primary role Executes predefined tasks Achieves... --- - Published: 2025-08-28 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/maisa-raises-25m-seed-investment/ Less than a year after our $5M pre-seed, we're back with news that validates our approach to making AI accountable. Our technology has matured, we've moved from pilots to production deployments, and our team has grown across two continents. Yet the core challenge we set out to solve remains unchanged: enterprises still struggle with AI they can't trust or trace. This new funding proves that the market is ready for a different approach, one where reliability comes first. The AI adoption reality The numbers tell a stark story. Despite the AI hype, 87% of enterprise AI projects never make it past proof-of-concept. Only 4% deliver meaningful value. Companies pour resources into pilots that impress in demos but fail in production, where accuracy and accountability matter. Enterprises need AI they can verify and AI their business teams can build and scale. They need full visibility into how decisions are made and the ability to create agentic automation without technical complexity. Current AI offers neither, keeping it stuck in experiments while real work stays manual. Digital Workers in action Business experts teach Digital Workers through natural language, just like onboarding a new colleague. They describe the process, the decisions to make, and the goals to achieve. The Digital Worker learns from this guidance and real-world feedback, creating automation that mirrors how work actually gets done. Fueling our mission This funding enables us to expand our engineering, sales, and customer support teams across Europe and North America while advancing our R&D efforts. We're building the infrastructure to help more enterprises deploy Digital Workers at scale. Every technical advancement and every new hire serves our core mission: making AI truly accountable for business-critical work. With these resources, we can help more organizations move from AI experiments to AI that executes with full transparency. Our partners in accountable AI We're honored to partner with investors who recognize that AI must be built on accountability. Maisa is solving one of the toughest challenges in AI: making it reliable and safe to use in mission-critical business operations. With a string of top global companies already using the technology, we are extremely confident of seeing significant growth at scale. Peter Specht, General Partner from Creandum Maisa’s platform is purpose-built for compliance-conscious industries like finance, where decisions must be traceable and outcomes consistent. The company’s early success demonstrates there’s real market pull for AI done the right way and Studio, launched today, will make it easy for anyone in an organisation to use digital workers quickly and effectively. Alberto Yepez, from Forgepoint Capital International Come join us If you're interested in building the future of accountable AI and helping enterprises transform how they work, visit our careers page. --- - Published: 2025-08-28 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/maisa-raises-25m-from-creandum-and-forgepoint/ Maisa, a dual US/Europe-headquartered company, provides fully auditable ‘digital workers’ to enterprises New Maisa Studio platform, unveiled today, allows non-technical staff - ‘citizen developers’ - to create AI agents using natural language without developer support Maisa Studio being piloted by global financial institutions and enterprises to automate complex workflows in compliance, finance and operations Creandum, early investor in Spotify, Klarna and Lovable, leads round, joined by Forgepoint Capital International via its European JV with Banco Santander. US funds NFX and Village Global also participated, doubling down on their investment last year Follows Maisa’s recent inclusion and naming in influential Gartner Hype Cycle reports alongside Google, Amazon and Salesforce as a top AI vendor in the world 28 August 2025, Valencia, Spain, and San Francisco, USA – Maisa, a leader in developing hallucination-resistant AI agents, today announces a $25 million seed investment led by Creandum, with participation from Forgepoint Capital, via its European joint venture with Banco Santander. The news comes only a few months after the company raised $5m pre-seed in December 2024 from NFX and Village Global, which is backed by Mark Zuckerberg, Jeff Bezos and Eric Schmidt. Both NFX and Village Global followed on and participated in today’s round. The new funding will support hiring across AI R&D, engineering, sales and customer success, as well as expanding Maisa’s growing footprint in Europe and North America. Alongside the fundraise, the company is launching Maisa Studio, an agentic process automation platform which enables ‘citizen developers’ - users who are experts in their business field but without an IT background - to deploy powerful and fully auditable AI ‘digital workers’ trained through natural language. The platform is being piloted at scale inside global banks, car manufacturers and energy companies, among others, to run multi-step, complex and compliance-sensitive workflows with full traceability and reliability. It allows non-technical staff to onboard digital workers with the ease of onboarding new colleagues. They can easily and quickly install a digital worker to perform complex, knowledge-intensive processes with full transparency, from evaluating risk and reconciling transactions to monitoring supply chain disruptions - any task that would traditionally demand extensive hours of repetitive human effort for poorly equated output. The digital workers require no dataset or developers, and users only need to write a series of natural language commands into the platform. The digital workers learn on the job by doing, through a method Maisa calls ‘HALP’ (human-augmented LLM processing), which is a fast and enterprise ready way to train digital workers. Maisa was founded in 2024 by CEO David Villalón and CSO Manuel Romero, two leaders in applied and foundational AI. Villalón was formerly Chief AI Officer at Clibrain and Director of Product at Voicemod. Romero is one of the top HuggingFace contributors globally, with more than 700 open-source models and 15 million downloads per month. Today’s raise comes less than a year since the company raised $5m pre-seed in December 2024 from NFX and Village Global, which is backed by Mark Zuckerberg, Jeff Bezos and... --- - Published: 2025-07-23 - Modified: 2026-03-13 - URL: https://maisa.ai/agentic-insights/maisa-studio-on-aws/ Despite the bottlenecks and challenges, agentic AI is becoming a reality for business processes. As part of this movement, AWS has introduced a new category in their marketplace: AI Agents. We're proud to be among the pioneering agentic AI players selected for this launch This helps enterprises access the technology through trusted channels they already use. The same procurement and security frameworks that govern other business software now apply to AI agents. The AI Agents landscape in enterprise AI agents mark a shift from assistants that respond to prompts to systems that pursue goals autonomously. They decide how to complete tasks, use tools, and adapt based on what they encounter. But enterprises face real barriers. AI agents hallucinate facts, make unexplainable decisions, and operate as black boxes. When you can't trace why an agent did something, you can't trust it with critical processes. This is why most businesses stay limited to basic chatbots. Which brings us to Digital Workers. They're AI agents built for business processes where every action is traceable and every decision explainable. Through Maisa Studio on AWS Marketplace, enterprises can now access AI that works with full visibility: you see the reasoning, the steps taken, and the tools used. AI designed for how business actually operates. Removing the barriers to AI adoption We joined AWS Marketplace as one of the first in this new AI Agents category to make Digital Workers accessible through the channels enterprises already trust. Too many AI projects stall in procurement and security reviews, keeping valuable technology out of reach for teams that need it. AWS Marketplace changes these realities. Security and compliance reviews are already complete. Procurement happens through existing AWS agreements, eliminating new vendor processes. We want enterprises to skip the bureaucracy and compliance marathons and go straight to the business decision: what do we automate first? Making AI accountable This advances our core mission: making AI truly accountable for business-critical work. Through AWS Marketplace, enterprises can access Digital Workers that combine autonomous capabilities with the transparency business demands. We're helping organizations move beyond AI experiments to real implementation, where AI doesn't just assist but executes work with full oversight. The future of business isn't about choosing between powerful AI and trusted systems. It's about having both. Buy with AWS --- - Published: 2025-06-20 - Modified: 2026-03-13 - URL: https://maisa.ai/agentic-insights/clatter-hallucination-detection/ The recent publication of "CLATTER: Comprehensive Entailment Reasoning for Hallucination Detection " couldn't have come at a better time. This groundbreaking research not only addresses one of AI's most critical challenges but also provides academic backing for the architectural decisions we've made in developing Maisa AI. The Hallucination Problem: More Critical Than Ever Analysts estimated that chatbots hallucinate as much as 27% of the time, with factual errors present in 46% of generated texts, making hallucination detection a make-or-break factor for AI deployment in production environments. The stakes are particularly high in domains like healthcare, legal services, and financial advice, where incorrect information can have severe consequences. Traditional hallucination detection methods have largely relied on simple fact-checking or confidence scoring, but these approaches often miss subtle fabrications or fail when dealing with complex, multi-step reasoning scenarios. CLATTER's Innovative Divide-and-Conquer Approach The CLATTER methodology introduces a systematic four-step process that mirrors what we've independently developed for Maisa AI: 1. Decomposition: Breaking Down Complexity The method decomposes generated text into factual claims, attributes these to source evidence, transforming complex outputs into manageable, verifiable units. This granular approach allows for precise identification of problematic content rather than broad-brush rejection of entire responses. 2. Knowledge Base Verification Each extracted claim undergoes rigorous verification against a trusted knowledge base. The system determines whether there's entailment (support), contradiction, or insufficient evidence for each claim. This step is crucial for maintaining factual accuracy while avoiding over-conservative filtering. 3. Parallel Processing Architecture One of CLATTER's most elegant features is its ability to process multiple claims simultaneously. This parallel verification approach significantly improves efficiency while maintaining accuracy, especially important for real-time applications. 4. Intelligent Reconstruction The final step involves feeding the original input, extracted claims, and their verification status back to the model for response refinement. This creates a self-healing mechanism that improves output quality without requiring complete regeneration. How CLATTER Validates Our Maisa AI Architecture Knowledge Base Integration Our decision to build Maisa AI with an integrated knowledge base consultation system inside the KPU aligns perfectly with CLATTER's approach. When our orchestrator determines it's necessary, we can query our knowledge base to verify claims in real-time, providing the same systematic verification that CLATTER demonstrates is essential. Step-by-Step Verification Pipeline The parallel claim checking that CLATTER employs mirrors our internal verification processes. We've implemented similar step-by-step checking mechanisms that allow us to identify and correct potential hallucinations before they reach the end outcome. Multi-Hop Reasoning Support CLATTER specifically addresses long-form, multi-hop question answering scenarios, exactly the type of complex reasoning tasks that Maisa AI is designed to handle. The research validates our architectural decision to implement comprehensive verification at each reasoning step rather than only at the final output. Self-Healing Capabilities Perhaps most importantly, CLATTER's reconstruction phase aligns with our self-healing approach to AI reliability. Rather than simply flagging problems, the system actively works to improve outputs based on verification feedback. Beyond Academic Theory: Real-World Implementation What makes CLATTER particularly valuable isn't just its theoretical framework, but its practical... --- - Published: 2025-06-04 - Modified: 2026-04-17 - URL: https://maisa.ai/agentic-insights/halp/ AI has made headlines for its potential to transform work, but inside most organizations, turning that potential into reliable automation remains a challenge. Business teams aren’t looking for impressive demos or clever assistants. They need AI systems they can trust to follow business logic, respect context, and stay consistent as things evolve. Yet the methods used to build these systems today often work against that goal. What if reliability didn’t depend on perfect data or complex training pipelines? What if AI could learn by doing, through real tasks and real feedback, inside the business itself? The limits of training methods for enterprises Human-in-the-loop (HITL) methods are used to make AI systems more accurate and aligned with human expectations. They rely on human feedback such as labeled examples, corrections, and supervision to teach models how to behave. This approach has been key to training today’s most advanced language models. Systems like GPT and Claude were refined through large-scale HITL processes, helping them perform well across a wide range of generic tasks. But when it comes to enterprise use, this method starts to show its limits. Business processes are specific, tools are unique, and rules change often. Applying HITL in this context means building custom datasets, coordinating technical teams, and retraining models just to keep systems functional. It is slow, expensive, and difficult to scale. For teams that need automation to adapt with the business, this approach becomes a bottleneck. Business logic should not have to wait for model retraining. Human-Augmented LLM Processing (HALP). A new way to teach AI What if AI could learn through real work, just like a new team member? HALP changes how we build reliable systems. Instead of relying on retraining cycles or complex setup, it enables AI to learn by doing. HALP stands for Human-Augmented LLM Processing, and it powers Digital Workers that learn directly from the way work happens. Configuring a Digital Worker through natural language Teams explain the task, walk through the logic, and share the tools they use. The system picks up that knowledge through natural interaction, without prompt engineering or rigid rules. Unlike traditional methods, HALP doesn't require labeled datasets or offline feedback loops. The learning happens in context, during real tasks. The system stays aligned with how the business actually works, even as things evolve. The reliability enterprises have been missing HALP unlocks what enterprise automation has long lacked: reliability in real work. Fast setup with less effort Digital Workers don’t need large datasets or precise prompts. They start from natural interaction and real context. Teams can build and adjust them without relying on IT or external consultants. Lower cost to launch and maintain Less time is spent configuring, correcting, or integrating. Business users can stay involved, reducing handoffs and rework. Scales across teams and processes Digital Workers adapt to different workflows. Logic can be reused, updated, and shared as the business evolves. Trust built into every step Each decision is traceable to a rule or piece of business logic. There... --- - Published: 2025-04-24 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/science-behind-maisa-architecture/ The architecture behind Maisa is the result of deliberate choices informed by research. A growing body of work has made it clear: while large language models offer impressive generative power, they fall short in several critical areas when used in isolation. Maisa’s strategic design responds directly to those gaps. Below is an overview of how each component is supported by scientific insight. Bridging reasoning and execution ReAct: Synergizing Reasoning and Acting in Language Models ReAct remains one of the most important foundations in the evolution of agentic AI. It introduced a core loop: reason, act, observe and repeat. This core helped reframe LLMs as active decision-makers rather than passive responders. This concept triggered the shift toward treating AI systems as agents capable of planning, adapting, and executing tasks in dynamic environments. While it's widely implemented today, its influence remains central to the architecture of AI systems designed for real-world decision-making. Hallucination is Inevitable: An Innate Limitation of Large Language Models LLMs are prone to fluent but inaccurate output. This limitation stems from architecture, not data. Steering LLMs Between Code Execution and Textual Reasoning Executable Code Actions Elicit Better LLM Agents Code to Think, Think to Code Chain of Code: Reasoning with a Language Model-Augmented Code Emulator These studies confirm the advantage of pairing LLMs with code execution: performance improves through verifiable logic, runtime validation, and structured task decomposition. While visible reasoning chains can appear coherent, they often mask logical gaps. Reliability increases when reasoning is grounded in execution, where each step is tested, not just described. Chain-of-Thought Reasoning in the Wild Is Not Always Faithful This paper highlights that exposing reasoning chains through techniques like Chain-of-Thought prompting does not ensure factual accuracy. The presence of a detailed explanation can create a false sense of confidence, even when the underlying logic is flawed or unsupported. The model may appear to reason more deeply, but the steps often serve as post-hoc rationalizations rather than evidence-based logic. This distinction is critical: coherence doesn’t equal truth. Executable validation remains essential for ensuring that each step reflects actual reasoning. How this shapes Maisa: The research outlined in these papers affirms a path we had already taken. Each finding reinforces architectural choices we made early on, confirming that the principles behind Maisa’s design are supported by emerging scientific consensus and designed to operate under real-world enterprise conditions. At the core is a reasoning engine structured around iterative decision-making loops, where each action is informed by observation and continuously adjusted until a defined goal is met. Instead of following fixed instructions, the system adapts continuously as conditions change and new inputs emerge. To support this, Maisa integrates a live code interpreter within the reasoning process, enabling the system to test assumptions, validate outcomes, and apply logical operations as part of its workflow. Rather than relying on text-based reasoning alone, every step can be executed, verified, and corrected in real time. Code is fundamental, not an add-on. Actions are embedded in executable logic and connected to internal tools,... --- - Published: 2025-04-07 - Modified: 2026-04-21 - URL: https://maisa.ai/agentic-insights/microsoft-partnership/ We are excited to announce that Maisa has been selected by Microsoft to become part of the Microsoft for Startups Founders Hub and recognized as a Microsoft Strategic Partner. Pushing forward our vision for Digital WorkersThis partnership reinforces our commitment to developing Accountable AI and Digital Workers that automate complex business processes. By tapping into Microsoft's extensive Azure infrastructure and specialized resources, Maisa gains powerful new capabilities to enhance the reliability, traceability, and performance of our AI technology. Strengthening Accountable AIThis collaboration with Microsoft supports our goal of building trustworthy, transparent AI solutions. We continue working to advance AI systems that companies can rely on to automate processes, delegating to accountable Digital Workers. --- - Published: 2025-04-01 - Modified: 2026-03-19 - URL: https://maisa.ai/agentic-insights/black-box-ai/ Artificial Intelligence is transforming critical decisions that affect businesses and people's lives, from approving loans and hiring candidates to medical diagnoses. Yet, many AI systems operate as "black boxes," providing outcomes without revealing how they were reached. This raises a fundamental question: how can we trust decisions made by systems whose reasoning we can't clearly understand? AI models learn from vast amounts of data, predicting outcomes without transparent, step-by-step logic. While their capabilities are impressive, this hidden reasoning creates uncertainty and potential risks. For businesses, relying on AI systems whose decisions are opaque can lead to serious accountability issues. If an AI makes a critical decision, how can companies confidently explain or justify it to employees, customers, or regulators? Addressing this trust gap isn't merely about compliance, it's about confidence and clarity in decision-making processes that shape real lives and business outcomes. Why is AI opaque? AI systems differ fundamentally from traditional software, which relies on clearly defined rules. Instead, AI learns directly from vast datasets. These models don’t have explicit instructions or human-understandable logic guiding their decisions. At their core, AI models use billions of interconnected parameters to convert inputs into outputs through complex mathematical calculations. This method is inherently probabilistic, meaning decisions are based on statistical patterns, not logical reasoning. With billions of these parameters adjusting simultaneously, tracking exactly how or why a specific output was produced becomes practically impossible. Unlike human decision-making, AI doesn't follow structured reasoning steps. It identifies correlations and patterns in data, predicting outcomes without explicit explanations. This absence of clear reasoning pathways means that decisions from AI systems often appear arbitrary, opaque, and difficult to interpret or justify. The risks of black-box AI in business Businesses rely increasingly on AI to automate important tasks, yet the opacity of these systems presents clear practical challenges. False confidence and AI hallucinations A major risk of opaque AI is "hallucinations," where AI produces seemingly accurate but entirely incorrect information. These false positives arise when the AI fills knowledge gaps or handles unclear inputs. For example, customer support chatbots might confidently provide false policy details, leading directly to confusion and complaints. Accountability gaps Opaque AI creates accountability issues. Traditional software clearly logs every decision step, making errors easy to track and correct. Black-box AI systems don't offer this clarity. When decision-making relies on hidden AI processes, identifying the exact point of failure becomes difficult, slowing corrections and process improvements. Legal and compliance risks Businesses must increasingly explain automated decisions clearly due to regulations like GDPR. If an AI-driven system, such as a credit scoring tool, makes decisions without understandable reasoning, businesses risk facing regulatory actions, customer complaints, or legal disputes. Uncertainty working with internal data and knowledge Businesses typically want AI to incorporate their specialized data and internal expertise clearly. However, black-box AI models obscure how proprietary business information is actually used. Without clear visibility, enterprises can't confirm that internal knowledge is applied correctly, risking inaccurate outcomes or impractical recommendations. Explainable AI (XAI) methods Several methods within... --- - Published: 2024-12-25 - Modified: 2026-03-19 - URL: https://maisa.ai/agentic-insights/maisa-raises-pre-seed-round/ Back in March, we introduced the first version of the KPU, setting new benchmarks that surpassed leading models. Since then, our technology has advanced with the launch of the Vinci KPU, our team has grown, and we’ve welcomed our first customers. Yet, the core challenge in the AI market remains unchanged: a persistent lack of accountability, reliability and transparency in AI systems. Manu Romero & David Villalón The trust problem in AI Generative AI lacks accountability. Techniques like Chain-of-Thought reasoning, RAGs, and multiagent systems aim to address more sophisticated challenges but still rely on probabilistic predictions, not deterministic computations. AI is unlocking opportunities across countless domains, but the complex world of business demands greater accountability. Mission-critical tasks require not only answers but traceable, evidence-based processes to reach them. Without these, we risk hallucinations—fabricated outputs that render AI results unreliable. This lack of trustworthiness is why fewer than 6% of corporations use AI for anything beyond basic tasks like question-and-answer bots. What we are building We believe the solution isn’t in refining existing approaches but in creating a new kind of computing system—one that combines AI's creative problem-solving with the determinism of traditional computational systems. With Maisa, you can create bulletproof AI Agents. These are a new generation of Digital Workers that follow natural language instructions to achieve specific outcomes and goals, making intelligent and reliable automation a reality. With the best behind us We are fortunate to be backed by visionary investors committed to advancing our mission. Our pre-seed round brought in $5 million, led by NFX and joined by Village Global, the venture fund backed by Mark Zuckerberg, Eric Schmidt, and Jeff Bezos. This funding was further supported by Sequoia's Scout Fund ,and DeepMind PM Lukas Haas, and other angel investors. This funding enables us to continue developing our product and expanding our research initiatives. We’re also genuinely grateful for the recognition our work has received, including a recent feature in Forbes that highlights this important milestone for us. Maisa is going to be a major player in Agentic Process Automation (APA) helping businesses across the world transform their core, business-critical functions through AI. It will allow them to work faster, more efficiently and achieve new and radical ways of operating. Anna Piñol, NFX David and the Maisa team are building a transformative technology to turn AI agents into actual workers that are capable of reasoning through complex workflows. We're super thrilled to be a part of their journey and are very excited to see the new benchmarks and enterprise traction Max Kilberg, Village Global Come join us If you are interested in joining our mission of making AI accountable, visit our careers page --- - Published: 2024-11-26 - Modified: 2026-03-22 - URL: https://maisa.ai/agentic-insights/vinci-kpu/ Introduction On March 14, 2024, at Maisa AI, we announced our AI system to the world, enabling users to build AI/LLM-based solutions without worrying about the inherent issues of these models (such as hallucinations, being up-to-date, or context window constraints) thanks to our innovative architecture known as the Knowledge Processing Unit (KPU). In addition to user feedback, the benchmarks on which we evaluated our system demonstrated its power, achieving state-of-the-art results in several of them, such as MATH, GSM8k, DROP, and BBH— in some cases, clearly surpassing the top LLMs of the time. Vinci KPU Since March, we have been proactively addressing inference-time compute limitations and scalability requirements, paving the way for seamless integration with tools and continuous learning. Today, we are excited to announce that we have evolved the project we launched in March and are pleased to present the second version of our KPU, known as Vinci KPU. This version matches and even surpasses leading LLMs, such as the new Claude Sonnet 3. 5 and OpenAI’s o1, on challenging benchmarks like GPQA Diamond, MATH, HumanEval, and ProcBench. What’s new on the Vinci KPU (v2)? Before discussing the updates in v2, let’s do a quick recap of the v1 architecture. KPU OS Architecture Our architecture consists of three main components: the Reasoning Engine, which orchestrates the system's problem-solving capabilities; the Execution Engine, which processes and executes instructions; and the Virtual Context Window, which manages information flow and memory. In this second version, we've made significant improvements across all components: Reasoning Engine Improvement: We have enhanced the KPU kernel, furthering our commitment to positioning the LLM as the intelligent core of our OS Architecture. This advancement allows for more sophisticated reasoning and better orchestration of system components. Execution Engine Enhancements: We have successfully integrated cutting-edge test-time compute techniques and made the execution engine more robust, secure, and scalable. This ensures reliable performance while maintaining high-security standards for tool integration and external connections. Virtual Context Window Refinements: We have refined our Virtual Context Window through improved metadata creation and LLM-friendly indexing. This enhancement optimizes how information flows through the system and lays the groundwork for unlimited context and continuous learning capabilities. KPU Architecture Benefits What makes these results particularly significant is that they're achieved by our KPU OS, acting as a reasoning engine, which focuses on understanding the path to solutions rather than providing answers. As main benefits, we can highlight: Model Agnostic Architecture (Better base models, better performance) Full multi-step traceability: configurable observability: Debug mode, visual representation, et. al. Provides better human-in-the-loop and over-the-loop control. Mitigate, almost fully eliminates, hallucinations: While this approach minimizes AI-generated inaccuracies, it may still encounter issues like errors in tool execution, incorrect data sources, or suboptimal approaches to solving the problem. Lower Latency to resolve problems than other systems in the market. Cost-efficient (up to 40x times cheaper than RAG, reasoning engines and Large Reasoning Models). Fully flexible and customizable with out-of-the-box functionalities: Unstructured data management, tools integrations, data processing... Autonomous execution with self-recovery/self-healing. It... --- - Published: 2024-03-15 - Modified: 2026-03-20 - URL: https://maisa.ai/agentic-insights/hello-world/ Hello World In recent periods, the community has observed an almost exponential enhancement in the proficiency of Artificial Intelligence, notably in Large Language Models (LLMs) and Vision-Language Models (VLMs). The application of diverse pre-training methodologies to Transformer-based architectures utilizing extensive and superior quality datasets, followed by meticulous fine-tuning during both Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback/Reinforcement Learning with Augmented Imitation Feedback (RLHF/RLAIF) stages, has culminated in the development of models. These models not only achieve superior performance metrics across various benchmarks but also provide substantial utility in everyday applications for individuals, encompassing both conversational interfaces and API-driven services. These language models, based on that architecture, have several inherent problems that persist no matter how much they advance their reasoning capacity or the number of tokens they can work with. Hallucinations. When a query is given to an LLM, the veracity of the response cannot be 100% guaranteed, no matter how many billions of parameters the model in question has. This is due to the intrinsic token-generating nature of the model, which generates the most likely token, not necessarily a factual one . Context limit. Lately, more models are appearing that are capable of handling more tokens, but we must wonder: at what cost? The "Attention" mechanism of the Transformer Architecture has a quadratic spatio-temporal complexity. This implies that as the information sequence we wish to analyze grows, both the processing time and memory demand increase proportionally. Not to mention issues such as the well-known "Lost in the middle" problem , where models fail to retrieve key information if it is located in the middle of long contexts. Up-to-date. The pre-training phase of an LLM inherently limits the data up to a certain date. This affects the model's ability to provide current information. Asking about events after its training cutoff may lead to inaccurate responses unless external mechanisms are used. Limited capability to interact with the digital world. LLMs are fundamentally language-based systems and cannot natively interact with external services such as APIs, files, or software systems, which limits their ability to solve complex real-world problems. Architectural Overview The architecture we have named KPU (Knowledge Processing Unit) has the following main components: Reasoning Engine. It is the "brain" of the KPU, orchestrating a step-by-step plan to solve the user's task. It relies on an LLM or VLM and available tools. The LLM is plug-and-play and has been extensively tested with GPT-4 Turbo. Execution Engine. Receives commands from the Reasoning Engine, executes them, and sends the results back as feedback for re-planning. Virtual Context Window. Manages the flow of data and information between the Reasoning Engine and Execution Engine. It ensures that reasoning remains within the LLM context while data stays external, maximizing token efficiency. It also integrates external sources such as the internet, Wikipedia, and files. This system is inspired by Operating Systems, which orchestrate hardware and software components while abstracting complexity for the user. This decoupling between reasoning and execution allows the LLM to focus exclusively on... --- --- ## Industries - Published: 2026-02-17 - Modified: 2026-04-01 - URL: https://maisa.ai/insurance/ Digital Workers for Insurance Transforming insurers’ operations to deliver faster value to clients Maisa’s Digital Workers help insurers streamline processes, accelerate outcomes, and stay compliant—delivering trusted results across the entire policy lifecycle. Schedule a Demo Proven Impact Across Key Insurance Areas With Digital Workers Property & Casualty (P&C) Digital Workers manage high volume claims, policy updates, endorsements, inspections, and document review. This reduces cycle times, improves accuracy, and supports faster and fairer settlements for homeowners and commercial clients. Health Insurance Automate eligibility checks, preauthorizations, provider documentation intake, medical coding validation, and benefits administration. This streamlines complex workflows while maintaining regulatory and clinical compliance. Long Term Disability Support end to end case processing by interpreting medical reports, verifying documentation, assessing benefit eligibility, and coordinating ongoing reviews. This reduces backlogs and improves claimant experiences. Life Insurance Accelerate application processing, underwriting evidence review, policy issuance, and beneficiary changes through automated data extraction, risk rules, and compliance checks. This speeds up decisions with consistent accuracy. Auto Insurance Automate first notice of loss, repair estimates, documentation validation, subrogation, and fraud detection. This streamlines claims handling and delivers faster resolutions for policyholders. AI Agents in Insurance – Too Dangerous to Trust? Understand what AI agents must have to operate safely and compliantly in highly regulated industries like Insurance. By completing and submitting this form, you agree that Maisa may email or call you with product updates, educational resources, and other promotional information. To learn more about how Maisa uses your information, see our Privacy Policy. Driving Practical Results: A Closer Look at Digital Workers in Action Why Insurance institutions choose Maisa Trust & Auditability Maisa provides full transparency and traceability across underwriting, policy, and claims processes. Every action is logged and auditable, while built-in hallucination resistance ensures decisions are accurate, compliant, and trustworthy—giving insurers confidence in their most critical operations. Scalability by Business Teams Insurance teams themselves can deploy Digital Workers using natural language just like onboarding a new employee. This facilitates giving feedback, continuous learning and scalability across teams. Speeding adoption and scaling automation from claims to collections without heavy technical dependency. Fraud Prevention & Risk Management Detect anomalies in claims, billing, and underwriting workflows thanks to our Digital Workers’ traceability and explainability. Maisa strengthens fraud controls while reducing operational risk exposure across the insurance value chain. Onboard your first Digital Worker today Practical Agentic AI for the Insurance Industry Schedule a Demo --- - Published: 2026-02-17 - Modified: 2026-03-16 - URL: https://maisa.ai/engineering-infrastructure/ Digital Workers for Engineering & Infrastructure Enabling engineering and infrastructure teams to operate with intelligent, end-to-end automation. Maisa’s Digital Workers help engineering and infrastructure teams streamline complex operations, enhance safety and compliance, and deliver value across every project lifecycle stage—from design to execution to maintenance. Schedule a Demo Proven Impact Across Every Function With Digital Workers Project Planning & Design Digital Workers speed up planning by automating tasks such as document analysis, blueprint verification, model updates, compliance checks, and stakeholder reporting. This reduces manual review cycles, improves accuracy, and ensures faster alignment across engineering teams and contractors. Construction & Field Operations Maisa supports site execution by automating inspection workflows, progress tracking, materials verification, safety monitoring, and subcontractor documentation. Teams gain real-time visibility, faster decision-making, and consistent quality across on-site activities. Asset Management & Back-Office Operations Organizations enhance efficiency by automating asset documentation, maintenance reviews, procurement workflows, and administrative operations. Finance, HR, compliance, and IT functions become more streamlined, enabling teams to focus on strategic programs. Driving Practical Results: A Closer Look at Digital Workers in Action Why Engineering & Infrastructure Organizations choose Maisa Trust & Auditability Transparent and verifiable actions ensure regulatory alignment, while hallucination-resistant models deliver accurate and consistent outputs across safety-critical tasks. Scalability by Business Teams Natural language onboarding empowers engineering, operations, and PMO teams to deploy Digital Workers independently—accelerating adoption and impact across projects. Faster Operations Digital Workers streamline workflows across planning, construction, and asset management, enabling organizations to move from project initiation to cash realization in a significantly shorter period of time. Onboard your first Digital Worker today Unlock Efficiency in Engineering & Infrastructure with Maisa Schedule a Demo --- - Published: 2026-02-17 - Modified: 2026-04-01 - URL: https://maisa.ai/manufacturing/ Digital Workers for Manufacturing Transform manufacturing operations with end-to-end automation Maisa’s Digital Workers help manufacturers eliminate bottlenecks, improve accuracy, maintain compliance, and move faster from planning to production to delivery. Schedule a Demo Proven Impact Across Every Function With Digital Workers Sourcing and Procurement Automate supplier documents, purchase orders, contract checks, and delivery confirmations to improve accuracy and strengthen supplier performance. Supply Chain Management Streamline demand inputs, shipment verification, exception handling, and inventory checks for faster decisions and fewer disruptions. Production and Maintenance Process production logs, equipment data, inspections, and maintenance records to detect issues earlier and improve plant reliability. Customer and Field Service Automate order validation, service requests, warranty reviews, and technician documentation to enhance response times and service quality. Finance and Compliance Streamline invoice checks, cost tracking, audit preparation, and regulatory documentation to improve accuracy and maintain full traceability. Driving Practical Results: A Closer Look at Digital Workers in Action Why Leading Manufacturers choose Maisa Trust & Auditability Transparent and verifiable actions ensure complete traceability across production lines, supplier interactions, equipment data, and plant records, supporting strict regulatory and quality management requirements. Scalability by Business Teams Operations, quality, engineering, and supply chain teams can deploy Digital Workers using natural language, allowing plants to scale automation across sites and processes without dependence on IT or specialized automation teams. Faster Operations Digital Workers remove delays across sourcing, supply chain, production, quality, maintenance, and service workflows. Manufacturers move from planning to execution to delivery in a much shorter time, improving throughput and operational stability. Onboard your first Digital Worker today Unlock efficiency and elevate operational control across your manufacturing operations with Maisa. Schedule a Demo --- - Published: 2026-02-17 - Modified: 2026-03-17 - URL: https://maisa.ai/all-industries/ Digital Workers for ALL INDUSTRIES Digital Workers that automate end to end processes for every industry From finance and legal to sales and operations, Maisa automates the work that actually runs the enterprise. Schedule a Demo Proven Impact Across Core Business Functions Built to support the core functions every company depends on so teams move faster and operate at greater capacity. Finance & Accounting From invoice intake to reconciliation and reporting, Digital Workers automate end to end finance workflows. Manual bottlenecks in reconciliations, expense approvals, vendor payments, and close processes are eliminated. Finance teams gain time for analysis, forecasting, and strategic decision making. Legal, Compliance & Procurement From RFP intake and vendor evaluation to contract review, risk assessment, and audit readiness, Digital Workers support the full legal and procurement lifecycle. They gather requirements, assemble and distribute RFPs, analyze vendor responses, extract and review contract terms, flag risks and exceptions, and ensure documentation stays compliant without slowing down sourcing or legal teams. This creates a continuous and auditable workflow from vendor selection through contract execution. Human Resources From employee onboarding to policy administration and offboarding, Digital Workers handle HR processes that traditionally require constant manual coordination. They manage documentation, ensure policy compliance, trigger system updates, and support HR teams at scale without sacrificing accuracy or employee experience. End to End Automation in Action How Digital Workers automate the most manual and complex processes in production. Why Leading Organizations Choose Maisa How Digital Workers automate the most manual and complex processes in production. From Pilot to Production in 60 Days Maisa follows a proven 1, 3, 6 delivery model built for speed and reliability. In one day, a feasibility assessment confirms the process can run in production. In one week, a Digital Worker is ready for user acceptance testing. In three weeks, a pilot runs in live workflows. In six weeks, the solution is production ready with a trained team and governance in place. Automation by Business Owners Maisa enables business owners to design, deploy, and adapt Digital Workers themselves. Using natural language, teams can automate workflows without waiting on IT backlogs or external consultants. This keeps automation aligned with real operational needs as processes evolve. Security & Governance Maisa is built to protect sensitive data across systems and teams with strong security controls and clear governance. Role based access, audit visibility, and policy enforcement ensure Digital Workers operate safely within the boundaries your organization defines. Onboard your first Digital Worker today Ready to automate the most manual and complex processes in production Schedule a Demo --- - Published: 2026-02-16 - Modified: 2026-04-01 - URL: https://maisa.ai/banking-financial-services/ Digital Workers for Banking & Financial ServiceS Reinventing how financial institutions work to stay competitive in the AI era Maisa’s Digital Workers help banks and financial institutions streamline operations, enhance compliance, and deliver value at every customer touchpoint. Schedule a Demo Proven Impact Across Every Function With Digital Workers Consumer/Retail Banking Customer services are improved by automating processes like account origination, KYC/AML, loan processing, card fulfillment, or dispute handling. This cuts manual work and compliance risk, while speeding up onboarding and delivering a better customer experience in different channels. Institutional & Wealth Core activities in research, trading, corporate finance, and wealth management become more efficient with Maisa’s Digital Workers on the team. Support extends to trade finance, power of attorney, account servicing, clearing, fund reporting, equity research or regulatory checks, among other use cases. Enterprise Operations Internal efficiency is strengthened by automating shared services, supply chain, and global functions. Finance, HR, procurement, compliance, and IT operations become faster, more accurate, and more cost-effective, freeing teams to focus on strategic priorities. Are AI agents ready for Banking and Financial Services? Understand what AI agents must have to operate safely and compliantly in highly regulated industries. By completing and submitting this form, you agree that Maisa may email or call you with product updates, educational resources, and other promotional information. To learn more about how Maisa uses your information, see our Privacy Policy. Driving Practical Results: A Closer Look at Digital Workers in Action Consumer / Retail Banking Corporate and Investment Banking Global Operations Legal and Compliance Retail & Commercial Wealth and Investment Management All Why Leading Banks and Financial Institutions choose Maisa Trust & Auditability Transparent and verifiable actions ensure compliance, while built-in hallucination resistance delivers accurate, trustworthy outcomes. Scalability by Business Teams Natural language onboarding empowers business teams to deploy Digital Workers themselves—driving adoption and results across the organization. Security & Governance Built to protect sensitive financial data with enterprise-grade safeguards, strict governance controls, and industry-leading security practices. Onboard your first Digital Worker today Unlock Efficiency in Financial Services with Maisa Schedule a Demo --- --- ## Use cases - Published: 2026-01-22 - Modified: 2026-03-20 - URL: https://maisa.ai/banking-financial-services/equity-research-investment/ Using Maisa Studio, the firm’s Wealth and Investment Management team created a Digital Worker that processes broker research reports end to end. Built without coding and guided by natural language instructions, the worker extracts, interprets, and structures insights with precision and consistency. Data Extraction and Structuring The Digital Worker reads and parses text, tables, and charts from PDF reports regardless of layout or formatting. It identifies relevant sections and converts them into standardized, machine-readable fields ready for analysis. Insight Summarization Summarizes target prices, recommendations, earnings expectations, and qualitative commentary into concise, structured insights, aligned with the firm’s reporting formats and KPIs. Automated Output Generation Creates ready-to-use outputs such as Excel templates and narrative summaries, enabling analysts to review and share insights instantly across teams and systems. Scalability and Adaptability Scales seamlessly across brokers and research teams with minimal setup, allowing rapid expansion of coverage across asset classes and sectors without additional engineering effort. By integrating document intelligence with domain expertise, Maisa Digital Workers turned a manual research workflow into a scalable, automated intelligence system for investment analysis.   The Results Faster Coverage Research reports that once required extensive manual review are now processed in less than five minutes. Analyst Enablement Structured insights accelerate decision-making and allow analysts to focus on strategic analysis rather than administrative tasks. Foundation for Scale The automated process enables consistent and rapid processing of large volumes of broker research, creating a foundation for global research scalability. Path to Advanced Intelligence Establishes a roadmap for future AI-driven research synthesis, enabling cross-broker comparisons and multi-document insights at scale.   --- - Published: 2025-11-14 - Modified: 2026-03-20 - URL: https://maisa.ai/banking-financial-services/rfp-response-generation/ Using Maisa Studio, the bank’s commercial operations and procurement teams built a Maisa Digital Worker that now handles the RFP process from start to finish. Configured in natural language, the Digital Worker connects directly with internal libraries, ensuring that every response is accurate, up to date, and aligned with corporate standards. RFP Document Intake Uploads RFP documents, reads each section, and automatically identifies all questions and response requirements. Automated Draft Generation Creates the first draft of RFP responses by analyzing historical data, previous submissions, and approved documentation. Drafts are automatically formatted in the required structure for Word, PowerPoint, or Excel. Unified Content Library Establishes a single, global document library that provides every team with access to the same verified and current content. This ensures consistency and removes dependency on outdated local files. Collaborative Workflow Management Assigns questions and sections to different teams or subject matter experts, tracks progress in real time, and sends reminders for pending items. The Digital Worker maintains version control and visibility across the entire process. Smart Information Retrieval Searches through internal knowledge repositories and documentation libraries to locate the most relevant and compliant information for each response. Analytics and Alerts Provides insights into response progress, pending items, and status alerts for administrators. It also manages document versions and change control to maintain transparency across all submissions. Final Compilation and Delivery Once all sections are validated, Maisa automatically compiles and formats the completed RFP. It generates supporting materials such as executive summaries, value propositions, and presentations ready for leadership review. By centralizing content and automating response generation, Maisa transformed a fragmented process into a fast, auditable, and scalable RFP operation.   The Results Automated Generation RFPs are completed automatically using verified content, removing the need for manual assembly and review. Time Savings Response time has been reduced from several days to just hours, allowing teams to handle multiple RFPs simultaneously. Improved Accuracy Systematic search and validation ensure that every response is compliant, complete, and aligned with approved documentation. Resource Optimization Teams now focus on strategic initiatives and vendor management rather than repetitive content retrieval and formatting. --- ---