Datasaur

Datasaur rolled out a fresh new look and clarified how we can help with building Private LLMs and Agents. Ask for help: let me know if it makes sense or resonates! Trying to strike the right balance between high-level explanation of our offerings and some technical clarification on what makes our approach unique. https://datasaur.ai/ Our NLP labeling platform continues to be served at datasaur.ai/studio

🚀 Powering Smarter, Faster Data Labeling As we move past 2025, we’re excited to share new Datasaur features built to make data labeling more efficient, flexible, and intelligent. Whether you’re annotating for NLP, managing complex workflows, or building datasets for LLM training. These updates are designed to help teams work smarter and scale faster. 👉 Read what’s new: https://lnkd.in/gpvQQphA

Apple has thrown in the towel and decided to use Google's Gemini to power Apple Intelligence. Want to build your own <Org> Intelligence? Work with Datasaur to build atop powerful open source models and invest in intelligence you own.

From Agents to Autonomy: A Practical Framework for Agentic AI Agentic AI isn’t binary. Autonomy exists on a spectrum, and understanding where a system sits is critical to building AI that’s useful, safe, and reliable. Our latest post introduces five levels of Agentic AI, from deterministic task bots to fully autonomous problem solvers: providing teams a shared language to design, evaluate, and govern AI systems responsibly. 🔗 Read the full framework: https://lnkd.in/edEadTQU

From 50 hours to 5. Grey Group, a global creative agency, transformed their market-insights workflow using Datasaur—cutting classification time by 10× while improving consistency and accuracy. By replacing manual review with Datasaur's AI, their team: - Reduced audits from ~40–50 hours to under 5 - Eliminated manual classification of their data - Achieved efficiency gains while enjoying improved accuracy 📊 This case study breaks down how Datasaur AI made it possible . 👉 Read the full case study: https://lnkd.in/gna3zRGT

We just wrapped a busy stretch of conferences at Datasaur across tech, legal, banking, healthcare, and insurance. One thing kept coming up in conversations with people from each industry. The sentiment around AI trust has really changed since these same events last year. Last year, most conversations were cautious. People were still figuring out what AI could safely touch. Now the vibe is different. There’s a real sense of confidence. After months of InfoSec, third party audits, and vendor assessments, companies have learned how to safely enable enterprise versions of ChatGPT, Claude, and Gemini for about 70-80% of their workflows and tasks. The remaining 20-30% is where things stand still due to privacy and compliance. • Privileged client communications for law firms • Regulatory correspondence and audit findings in banking • Medical claims with patient identifiers in healthcare • Claims histories with VIN data in insurance These are just some of the high-utility, high-impact workflows that are not being ignored. They are being protected. The future of enterprise AI is not just Enterprise ChatGPT, Claude, or Gemini. It is Enterprise ChatGPT, Claude, or Gemini AND Private LLMs. The commercial enterprise options drive employee productivity and adoption. Private LLMs power the heavily compliant, nuanced projects that deliver highly measurable ROI. Together, they complete the puzzle of secure and responsible AI adoption. Organizations that recognize this shift early are already completing the puzzle. The rest will be playing catch-up. #AI #GenerativeAI #ResponsibleAI #AIForBusiness #EnterpriseAI

Datasaur is proud to be building a sovereign, private AI solution for Indonesia, the world's 4th most populous nation. The vast majority of training data for current LLMs are linguistically and culturally biased, and leveraging local, relevant data is important to democratizing access to this technology. "Korika Chat was developed through a collaboration between KORIKA (Indonesia AI Industry Research and Innovation Collaboration) and Datasaur AI, a global company specializing in the development of Large Language Model (LLM) platforms. Built on privacy-first principles and open-source architecture, KChat is ready to support various sectors, from state-owned enterprises (BUMN) and public institutions to MSMEs, in delivering efficient and inclusive digital services." https://lnkd.in/gN8trrQq

In our latest post, we define what a Private LLM truly means and why the distinction matters now more than ever. With OpenAI and Anthropic recently updating their data retention policies, it’s worth revisiting equally powerful options available to enterprises that need the power of modern LLMs without sending sensitive data to third parties. We also highlight how privacy is not a binary choice, but a spectrum. Each organization must calibrate its requirements across dimensions like data residency, governance, and deployment. Finally, we propose a practical framework for evaluating solutions, helping leaders cut through vague marketing claims and identify the right approach for their specific regulatory, security, and business needs. Read more here: https://lnkd.in/g766ypBU

The August LLM Scorecard is here — now featuring OpenAI's new open‑source model gpt-oss and Grok 4! We’ve just published our August 7, 2025 LLM Scorecard, ranking leading language models across Privacy, Quality, Cost, and Speed. Curious how the open model stacks up? Dive in and see the full comparison: https://lnkd.in/gC6gKZgx

July Feature Updates! We’ve been busy rolling out new tools to make labeling faster, smarter, and more intuitive. ✅ Labeling Agent: Configure multiple LLMs to label your data, with consensus 🔍 Smarter Search: Instantly find what you need in complex datasets ✏️ Editable Rows: Make quick in-line changes without switching views Read the full breakdown here: https://lnkd.in/gaCVCP-W #AI #DataLabeling #MachineLearning #NLP #LLMs #ML #Datasaur