During a recent discussion between Sam Altman and Indian minister Ashwini Vaishnaw, the conversation centered around India’s role in the AI revolution. I think India has the unique opportunity to lead in AI. The next generation of AI products won’t just assist humans — they will replace them in operating systems entirely.

This is the most fundamental shift in software we have ever seen. It means that founders can now build for a ‘software + salary’ total addressable market (TAM), not just the traditional software TAM. This is a game-changer, as AI agents take over tasks that were previously performed by expensive knowledge workers.

At Together Fund, we have been tracking this shift closely, reviewing over 2,800 AI startup pitch decks over the last 2 years. We’ve invested in multiple companies that are capitalizing on this AI disruption — including Composio and Emergent, which are solving some of the hardest challenges in agentic AI.

Composio.dev: The Developer-Centric Integration Layer for AI Agents

Traditional AI agent integrations can take months; Composio’s platform reduces this to days, significantly enhancing developer efficiency & boosting AI Agent success rates from 40–50% to over 90%, ensuring dependable operations across applications. This is the reason why over 14,500 developers, including teams from Meta, Salesforce, and Cisco, have adopted Composio’s framework. They’re also working with large enterprises like Databricks, Datastax as well as innovative startups like 11x & Arcee.

One of the biggest challenges in agentic AI is integration — AI agents need to seamlessly interact with enterprise applications, tools, and APIs without human intervention. That’s where Composio comes in.

Founded by Soham Ganatra and Karan Vaidya, Composio is building a developer-centric integration platform that enables AI agents and large language models to connect with external applications out-of-the-box. The goal? To make integrating AI agents easy for AI-driven automation at enterprise scale.

Why does this matter? AI agents cannot operate in isolation. They need to fetch data, trigger workflows, and respond dynamically to external systems — all while maintaining accuracy and reliability. Composio is solving this by providing pre-built integrations, API orchestration, and real-time monitoring.

In many ways, Composio is to agentic AI what Twilio was to communication APIs — a foundational layer that will unlock an entire industry.

Emergent.sh: AI Coding Agent

Another key area where agentic AI is making an impact is in software engineering itself. Today, debugging is a time-consuming process that requires skilled engineers to analyze logs, reproduce errors, and manually write fixes.

Founded by Mukund Jha and Madhav Jha, Emergent is changing that. It is an AI-powered coding agent that is redefining software development by automating the entire software development lifecycle (SDLC) — from coding and debugging to testing, deployment, and maintenance.

Why Emergent Stands Out

• Beyond Code Completion — Unlike Co-Pilot and Devin, Emergent automates solutioning, debugging, testing, and deployment with human-like reasoning.
• State-of-the-Art Performance — Emergent’s AI models outperform competitors on industry benchmarks like SWE-Bench-Verified (consistently ranking in the top 10 since their launch early October last year w/ planned product launches performing significantly better
• Strong Market Traction — 5+ pilot programs starting in February, targeting application development, software maintenance, and testing use cases.
• Infrastructure Flexibility — Works across cloud, VPC, and local environments, ensuring scalability and security.

Emergent is competing in a space where even Cognition’s Devin, the world’s first AI software engineer, struggles — only 14% of GitHub issues are solved unassisted. The road to 100% accuracy is a long one, but Emergent’s approach is laser-focused on fine-tuning AI models for enterprise-grade reliability.

The impact? Faster software development cycles, fewer engineering bottlenecks, and reduced dependency on human developers for repetitive bug-fixing tasks.

Emergent consistently ranks among the top 10 in the SWE-Bench Verified benchmark, with internal models showing even stronger results in upcoming iterations.

The Path Ahead for Agentic AI

The bar for agentic AI is extremely high. These AI agents must not just work — they must work at 10x efficiency to justify replacing humans.

For founders building in this space, the biggest unlocks will come from solving:

✅ Seamless integrations
✅ Reliability and accuracy
✅ Infrastructure-level AI capabilities that power the next wave of autonomous software

India’s AI opportunity is not just about applications — it’s also about AI infrastructure or tooling. Just as AWS built the cloud backbone for the modern internet, Indian AI startups have the chance to build the foundational layers for agentic AI — whether it’s integration platforms like Composio, AI-powered coding assistants like Emergent, or other core enablers of the AI economy.

The real opportunity? Owning the AI stack. Not just consuming foundation models, but creating India’s own AI-first infra companies that shape how enterprises deploy and scale agentic AI. We have been early believers in these bold ideas resulting in these two investments at the seed stage.

That’s why I believe India is sitting on a $1 trillion opportunity — not just in AI applications, but in AI infrastructure itself. The future of AI is not just about building tools for humans — it’s about building the platforms that power the next generation of autonomous AI companies.

Are you building for this future? Let’s talk.

In the early 2000s, my world was the vibrant and uncertain realm of green coffee trading. Every day, as I navigated between the bustling trading floors of London and New York and the sprawling coffee farms of Vietnam and Thailand, a singular thought gnawed at me: Why was this industry, so rich in history and complexity, lagging so far behind in technological innovation?

The early mornings in dimly lit rooms, filled with the aroma of freshly roasted beans, contrasted sharply against the clunky, outdated systems we relied on. The industry was frozen in time, resistant to the digital wave sweeping other sectors. I was a young commodities trader, enthralled by the process yet frustrated by its inefficiencies.

This dissonance between the old and the new sparked an idea to revolutionise the commodities trading industry. Thus, Eka Software Solutions was born in 2004, fueled by a burning desire to bring technological sophistication to an arena steeped in tradition.

Gratitude and Growth

By early 2014, Eka was forging ahead, having reached $34 million in annual revenues with 30% EBITDA, two acquisitions under its belt in Canada and Australia, and raised significant capital from Silverlake, a major private equity player in the US. We were charging ahead on our vision to build a global leader in commodity trading software from India.

My bet on transformation versus tradition had paid off. Yet, amidst the success, I often think about the journey and the people who made it possible.

For starters, there has always been the unwavering belief of my first investor through this roller coaster ride, Mr Kirit Shah, Chairman of the GP Group, and the rest of Eka Software’s institutional board members Sandeep Singhal and Bryce Lee.

Over the past decade and a half, I am grateful to Eka Software’s management team for buying into my vision and running with it to grow into leadership roles. Eka’s head of HR and sustainability Shuchi Nijhawan, CTO Mumu Pande, Rahul Jain in Engineering, Amit Sureka in finance, Prachir Dhandhania and Gaurav Shah in product, Suryatej Sonawane and Vinni Malik in professional services, as well as Sumarani Sarkar in the crucial talent acquisition and development function — all of you have grown with Eka Software. Special thanks to early members Karthikeyan N, Shobhit Mathur ,Vinayak Mungurwadi, Sanjay Singla, Rick Nelson, Lynn Lattimer, Rajeev Warrier.

What makes me happiest as a founder is that many of the C-level executives who have been a key part of the smooth handover of Eka to STG Partners and the Quor Group this year, have muscle memory from the 2014 journey — of moving the software platform from licence to cloud, while managing the commodities downturn.

The Hard Decision to Leave

There are no two ways about it as a founder: Selling your company is an emotionally sapping decision, especially if — like me — you are a first-generation entrepreneur.

For starters, it is extremely difficult to create a sustainably running company from scratch. Eka Software has been profitable for most years, barring the three years when we took the product to the cloud from 2016 onward. Its profitability rose to high double digits since FY 2021. Plus, there’s substantial headroom for the profit margin to grow.

In such a context, it is natural for a founder to feel, “I can run the company forever,” because the topline and profits are growing. So why not continue to run Eka and build on it, especially with the technology moat we built in the industry?

Many a time, a business compounds simply because the founder perseveres in a business with high profitability. I have seen several family-run businesses do this beautifully — using profits from a flagship business to foray into new business lines and add new products. So, staying on to grow Eka was the first emotion I felt when we began to evaluate buyout offers two years ago.

But at another level, two unique thought processes in late 2022 made me look beyond Eka. First, the itch to jump into something completely new in the world of start-ups and venture capital. There are a lot of risks that come with changing your persona from an operator to an investor which calls for a coach’s persona.

I have been an angel investor, but with start-ups becoming part of India’s zeitgeist, I began to think about how I can be part of creating the infrastructure and building-blocks for start-ups.

More spectacularly, Generative AI happened. It was the ultimate driver of my decision — that I have to ride this AI wave with new eyes.

India is in a strategic position in the globe. Start-ups have become an important part of India’s economy. With AI’s ubiquity growing, I could leverage all my experience — from starting a company from scratch, building a global GTM and understanding technology — in value creation, and look beyond Eka.

This was the right time to exit.

No surprises, but my family, who have been with me through the Eka journey, were baffled. They wondered why I wanted to sell the business after years of toil. In India, there is an undercurrent that your children can inherit your business. And it’s very hard to create a company. So the discussion with my family about ‘What next!’ was vital. It wasn’t just an emotional decision for me.

Where family businesses have the capital and a lot of accumulated assets, I had done everything as an entrepreneur for the first time in my family. Money was not the biggest driver, though it is an important aspect of planning an exit. There was no question about retiring young. I am not done as an entrepreneur yet.

And what will happen to employees, especially those who have worked with Eka Software for more than 10 years? Will they be taken care of under the new owners? All these thought processes made for a roller-coaster of emotional and complex discussions with my family, myself and Eka Software’s employees.

Execution is the best bonding experience

In many ways, all of us at Eka Software experienced two arduous spells of challenges in the environment between 2016 and 2021. On both occasions, we rose to the myriad challenges before us. We grew.

My first hard call was to increase investments in technology between 2015 and 2019, when the average price of Brent Oil was $57 per barrel (bbl), compared to $91 bbl in early April. The picture was sombre even in the commodity and base metals markets. It was the toughest macro environment to innovate in, as we took Eka Software from a licensed product to cloud-based software offerings. It turned out to be timely.

By late 2019, the $2.5 billion CTRM (commodities trading and risk management) software market witnessed consolidation. In just three years, Aspect Enterprise Solutions, OpenLink Financial and Allegro Development Corporation got acquired by ION Group, a multi-billion dollar company. That’s also when Eka Software Solutions began to get buyout offers.

It isn’t so obvious in the technology industry, but a large chunk of the systems integration work in the CTRM universe is carried out by technology services companies. And there is still a lot of room for automation and product innovation. So, the demand for product companies like Eka was huge, and the doors were open for an exit. Until the world got hit by the COVID-19 pandemic.

The first wave was about survival before we stabilised the operations. Commodities trading is a high-touch business that involves a lot of travel for sales teams. Our customers are global companies like Cargill, Unilever, Transcanada, and Rio Tinto. When everything moved to Zoom, it was a fundamental shift to online conversations with CEOs of our top 50 customers. It gave us an opportunity to move towards higher profitability, as Eka Software’s SaaS flight was more organic than we initially anticipated.

In the commodities markets, entire supply chains got disrupted. As hard as that phase was, it sparked off the upcycle that the commodities market is currently in. (Knock on wood!)

Family and Future

By 2022, it was clear to the operator in me that Eka Software was ready for the new upcycle. What’s more, our engineering and management team had come of age. As I said earlier, the AI wave that began in October that year thanks to OpenAI spawned a whole new area of interest for the entrepreneur in me, and the Together Fund with Girish Mathrubootham had also taken wing in 2021.

For entrepreneurs, it is absolutely vital to have a strong anchor through a two-decade journey. My wife, Mitali Gupta, has been that anchor. As Steve Jobs once said, “You can’t connect the dots looking forward; you can only connect them looking backward.” Reflecting on my journey with Eka, it’s clear how each step, each decision, was part of a larger picture leading to new opportunities and horizons.

In the concluding part of this post, we will go into the mechanics and larger questions that come into play while completing a strategic acquisition.

By Manav Garg,
Cofounder & Managing Partner Together, Founder Eka Software

In the SaaS and AI world, two powerful frameworks guide a start-up’s growth journey, especially as it nears product-market fit: Product-Led Growth (PLG) and Enterprise Sales. Each framework has proven its value, but they play different roles in scaling a company. And as AI continues to revolutionise the SaaS landscape, both frameworks are more essential than ever, each addressing unique needs for scale, depth, and adaptability in a rapidly changing market.

PLG, a popular approach among Indian SaaS and AI founders, leverages a product’s self-serve, inbound appeal to attract thousands of users quickly and at a low cost. It’s a great way to build brand momentum, especially among small to mid-sized businesses (SMBs). But with AI-powered products becoming mainstream, founders must navigate more complex buyer demands that require a high-touch approach. To scale sustainably and meet the demands of AI-driven solutions, companies must leverage both PLG and enterprise sales for lasting growth.

1. Product-Led Growth (PLG) and AI: The Foundation for Rapid Scale

PLG is built around inbound sales and aims to attract customers by creating a product experience so engaging and valuable that users flock to it organically. It’s a model well-suited for small to mid-market customers (SMBs) due to its low friction and high scalability.

With AI tools, PLG companies can go further by automating personalised user experiences, optimising onboarding, and gathering insights into user behaviour. AI can streamline customer journeys and amplify inbound engagement, allowing SaaS companies to scale quickly with tailored support. Using tools like predictive analytics and AI-driven customer insights, PLG companies can gather data on user engagement and optimise the product experience accordingly.

For instance, Slack used PLG to achieve rapid growth globally, while Zoho and Freshworks became household names in the Indian SaaS space by capturing thousands of SMBs with their low-cost, high-value offerings. But while PLG can attract large numbers of users, it’s not the full picture for SaaS companies aiming to secure long-term, high-value contracts — especially with AI products that require customization and support. Relying solely on inbound demand can make it difficult to scale predictably, which is why enterprise sales is essential for SaaS companies looking to reach the next level.

2. The Value of Enterprise Sales in the AI Era

Enterprise sales focuses on high-value, long-term relationships with large accounts. This approach involves longer sales cycles, consultative selling, and a commitment to helping customers succeed over time. For AI-driven SaaS products, enterprise sales becomes even more critical, as companies must adapt to the unique data security, compliance, and integration requirements of each client.

Unlike the self-serve nature of PLG, AI products often require custom solutions and ongoing support to unlock their full potential for enterprise clients. Software companies like Eka and Icertis have demonstrated that enterprise sales is not just viable for Indian SaaS and AI — it’s critical for stable, predictable revenue. These companies focus on becoming trusted partners rather than simply vendors, which enables growth through upselling, cross-selling, and deep integration.

While PLG provides a strong foundation for inbound growth, enterprise sales allows SaaS and AI companies to become strategic partners for enterprises adopting AI. In this role, SaaS and AI providers help clients integrate AI-driven solutions, navigate compliance needs, and gain data insights that fuel growth.

3. Case Studies from Together fund portfolio: Start up companies focused on sales

Dhiwise: Scaling Beyond PLG with Enterprise AI Accounts

Dhiwise, a platform enabling developers to generate AI-powered code from designs, began its journey with a PLG approach. In its first few years, Dhiwise scaled to over 200,000 users by leveraging inbound demand, gaining insight into its ideal customer profile (ICP) and refining its product.

Recognizing a high-value opportunity within technology services, Dhiwise pivoted to focus on enterprise accounts with large ACV contracts, particularly within India’s $200-billion technology-services sector. AI drove demand for automated code generation, allowing Dhiwise to position its offering as a strategic asset. Had Dhiwise focused solely on winning PLG users, it might have missed this lucrative enterprise market. By integrating enterprise sales, Dhiwise broadened its revenue base and secured a predictable growth path.

RapidClaims: Winning Large AI-Driven Healthcare Clients

RapidClaims, a healthcare SaaS company using AI for medical coding, started with focus on enterprise sales — mid-sized hospitals (ACV $150,000) in the US as its target market. RapidClaims developed early muscle in sales: navigating complex processes to engage multiple stakeholders across departments, from the CTO to boards of directors, each with unique requirements for data security and accuracy

AI-powered coding attracted large healthcare providers interested in streamlining complex coding processes. Early references and early enterprise sales muscle helped RapidClaims aim to win $1,000,000 ACV contracts, boosting revenue and customer retention.

4. Building an Enterprise Sales Engine for AI-Driven SaaS Solutions: Practical Steps for Founders

For SaaS founders offering AI solutions, here’s how to build a sustainable enterprise sales engine that complements PLG:

Build the Sales Mindset

Every enterprise account has unique decision-makers and requirements. AI products add further complexity, as enterprise clients often require compliance with strict data regulations and customization for industry-specific needs. Your team needs to shift from simply selling a product to becoming a trusted advisor for clients.

Start with a team approach: founders, sales heads, and solutions leaders working together to understand and engage with enterprise clients. Hiring the right sales leaders early is critical, but timing is equally important. Bring them on too early, and you risk burning through capital; bring them on too late, and you miss growth opportunities.

Score Early Wins with Paid Pilots for AI Solutions

A pilot project is a great way to prove your product’s value in an enterprise setting. By encouraging enterprises to pay for the pilot, founders can better gauge the product’s potential impact. For AI solutions, pilots can also be designed to showcase the specific value AI offers in automating or enhancing workflows.

The pilot isn’t just about product validation — it’s also a chance to map the internal network of decision-makers, from budget holders to key influencers, as these dynamics can be more complex with AI-powered tools. This insight helps smooth the path for full-scale deployment and reduces the length of future sales cycles.

Prepare for Complex, Dynamic Sales Cycles

In enterprise sales, the decision-making process involves multiple stakeholders and complex budget approvals. Founders must be ready to navigate these dynamics, identifying who controls budgets, who makes final decisions, and who influences the purchase.

This complexity distinguishes large enterprises from SMBs, where all major decisions often reside with a single leader. Engaging with a complex structure requires patience, persistence, and consultative selling skills. By starting their enterprise sales engine early, founders can gain the experience needed to handle longer sales cycles and adapt AI tools to client-specific requirements.

Conclusion: Balancing Inbound and Enterprise Sales for Indian SaaS and AI Success

India’s SaaS and AI landscape has produced impressive outcomes from PLG-driven companies like Freshworks and Zoho, which successfully captured SMB markets. But the next wave of SaaS and AI giants will require a balance of PLG and enterprise sales to meet complex market needs.

Companies like Freshworks have shown that by blending both approaches, SaaS and AI companies can build scalable, stable revenue models. Founders who embrace both frameworks — leveraging AI to fuel PLG while driving enterprise sales for high-value accounts — will unlock long-term growth and global impact.

For Indian SaaS and AI founders ready to scale, enterprise sales offers an opportunity to secure high-value clients, expand market reach, and build stable revenue. While the journey may demand resilience, patience, and a readiness to hear “no” more often than “yes,” the payoff is worth it. It’s time for Indian SaaS and AI companies to embrace both inbound efficiency and enterprise depth to achieve sustained, global success in the AI era.

In the early days of building Eka Software, I found myself sitting across from prospective customers with no formal playbook in hand — just a relentless curiosity to understand their needs and a desire to sell. I had no choice but to rely on instinct and adaptability, skills I had picked up trading commodities across Southeast Asia and Europe.

What struck me back then — and still holds true today — is how easy it is for founders to assume that if you build a great product, customers will line up. But reality rarely plays out that way. Even the most groundbreaking technology needs a sales engine to push it into the world, create demand, and ensure it becomes a must-have rather than a nice-to-have.

Over the past few years, I’ve noticed that Indian SaaS founders are increasingly leaning into product-led growth (PLG) at the expense of building a robust sales machine. Some believe that with a great product, growth will happen organically. But even PLG stalwarts like Salesforce maintain massive sales operations — blending direct sales teams and partner ecosystems — to fuel growth.

With the rise of Gen AI, Indian start-ups have a unique opportunity to take centre stage. However, access to venture capital or a cutting-edge product alone won’t ensure success. Start-ups need a disciplined, repeatable sales strategy right from the start to unlock their full potential.

The Product Trap: Why PLG Isn’t Enough

Founders often think that focusing solely on the product will take them across the finish line. But PLG can only get you so far. A sustainable, scalable business needs a well-oiled sales engine driving revenue growth and customer acquisition.

In a study conducted by McKinsey for SaaSBooMi, the numbers told a revealing story: Indian SaaS companies were under-investing in sales and marketing by nearly 50% compared to their US counterparts. Worse, they started thinking about sales too late in the process.

Without a proper sales infrastructure, companies struggle to break through revenue ceilings. This challenge becomes even more critical as SaaS ventures mature into Gen AI opportunities, where rapid commercialization can make or break a business.

Three Steps to Build a Winning Sales Engine

1. Adopt Foundational Sales Principles for Success

For many Indian founders, particularly those with engineering backgrounds, sales can be a daunting journey. Unlike building a product — where processes are linear and outcomes predictable — sales is full of surprises, endless negotiations, and hearing ‘no’ far more often than ‘yes.’ At times, it can be all-consuming, requiring relentless persistence and emotional resilience. However, embracing these challenges early will make the process smoother over time.

Here are five key principles to help you build a winning sales engine:

1. Invest in Sales Early On
2. Get Your ICP (Ideal Customer Profile) Right
3. Understand Your Winning Position
4. Monitor and Manage Churn Early
5. Start Small, Then Scale

2. Build a Scalable and Repeatable Sales Machine

Once your foundational principles are in place, you can build a scalable and repeatable sales machine. Here’s a step-by-step roadmap, assuming a product with a $100K Annual Contract Value (ACV):

1. Set Your Revenue Goal:
2. Calculate Your Win Rate:
3. Map the Sales Funnel:
4. Break Down Monthly Targets:

What Your Sales Roadmap Looks Like

• Engage 3,340 prospects per month
• Generate 167 MQLs per month
• Convert 42 SQLs per month
• Close 8–9 deals monthly
• Hit $10M ARR by closing 100 deals

3. Leverage Tools, Data, and Churn Insights for Efficiency

A robust CRM system is essential to track every stage of your funnel and automate routine tasks. Data insights will help fine-tune the process — whether it’s optimising your MQL-to-SQL conversion rate, improving win rates, or addressing early signs of churn.

Here are key metrics to monitor and optimise:

• Sales Velocity: How quickly deals move through the pipeline
• Pipeline Coverage Ratio: Your pipeline should be 3x your sales target to account for conversion rates
• Deal Size: Track average deal sizes to ensure you’re engaging with the right customers
• Churn Rate: Monitor churn closely and build customer success teams that engage actively post-sale to ensure retention. A lower churn rate compounds growth, amplifying the impact of your sales engine.

Regular pipeline reviews are crucial. If deals are stalling at any stage, analyse why and take corrective action. Ensure your team practises pipeline hygiene — removing dead leads to avoid false optimism.

Conclusion: Build Momentum with Strategy and Discipline

A scalable sales machine is built on a foundation of early investments, clear ICPs, churn management, and a strong winning position. By starting small and refining your strategy, you create a repeatable, predictable process. Tools, data, and disciplined pipeline management ensure that your sales engine can grow with your business, no matter how fast you scale.

Sales isn’t a sprint — it’s a marathon. Indian founders — especially those from engineering backgrounds — must prepare for surprises, relentless negotiations, and hearing ‘no’ more often than ‘yes.’ Selling can be all-consuming, but it’s the foundation of scalable growth. With persistence and the right strategy, Indian SaaS and Gen AI start-ups can build growth engines that power long-term success.

Introduction: The AI Revolution’s Unexpected Epicenter

Picture this: A small team, on the brink of shutting down after a decade of struggle, suddenly skyrockets to a million users in just 84 days, the fastest growing productivity app of all time to hit the 1 million milestone.

This isn’t a tale from Silicon Valley — it’s the story of Presentations.AI, and it’s happening right here in Bengaluru, India.

As we approach the two-year anniversary of ChatGPT’s launch, a quiet revolution is brewing in the world’s largest democracy.

While tech giants battle over who can build the biggest AI model, India is writing a different story — one of focused innovation, deep expertise, and vertical domination in the AI space.

This is a story that validates Together Fund’s core thesis on AI — “India’s future in GenAI space is vertical”.

Countless opportunities lie in building AI-driven applications based on industry knowledge. We believe there are over 500+ untapped vertical opportunities for founders in India’s application layer, where domain know-how can be translated into world-class products.

Much like Presentations.AI.

The Presentations.AI Phenomenon: A Microcosm of India’s AI Strategy

In 2012, Sumanth Raghavendra embarked on a mission to simplify presentation creation. Fast forward to 2023, and his company, rebranded as Presentations.AI, is a testament to India’s unique approach to AI:

1. Domain Expertise: A decade of understanding the nuances of presentation narratives and design.
2. Carpe Diem — Seizing the Opportunity: Leveraging generative AI at the perfect moment.
3. Explosive Growth: From struggling startup to a million users in less than three months.

Sumanth’s words capture the essence of this transformation: “We had built this AI platform for years in a pre-GPT world. It was like trying to build a spaceship with stone tools. Then the generative AI tsunami hit, turning our headwind into a tailwind.”

This success story perfectly illustrates India’s edge in the Gen AI world: applications that draw from deep domain expertise.

The Shift from Foundational Models to Verticalization

While global tech giants like Google, Microsoft, and Meta race to build the largest foundational models, Indian founders are uniquely positioned to focus on verticalization: defining specific problems, understanding customer needs in niche markets, and executing effectively.

Building large language models (LLMs) requires vast resources. Even Sequoia Capital has pointed out that applications, especially AI-led verticals, are where the true value lies. The capital-intensive nature of foundational model development makes verticalization a smarter play for founders looking to innovate with fewer resources but greater focus on solving specific business problems.

Why Verticalization is India’s Trump Card

While the West focuses on building larger, more general AI models, India’s strength lies in its laser-focused approach to solving specific industry problems.

Here’s why this strategy is a game-changer:

1. Resource Efficiency: Building specialized AI requires less computing power and data than general models.
2. Quick Time-to-Market: Focused solutions can be developed and deployed faster.
3. Higher ROI: Solving specific problems leads to clearer value propositions and faster adoption with pragmatic business models and viable unit economics.

“In the AI gold rush, it’s not about who has the biggest shovel, but who knows exactly where to dig.”

India’s Legacy in Applications

India’s strength in application development is not new. For decades, we’ve built a reputation for world-class software, contributing significantly to the $254-billion IT services and business process management industry. This focus on applications stems from a unique historical context. During pre-liberalized India, strict regulations stifled the growth of hardware and infrastructure, but software development flourished as a liberating tool for innovation.

Companies like Zoho and Freshworks led the charge in creating an inbound motion SaaS model from India. When I founded Eka Software Solutions, we capitalized on my deep knowledge of commodity trading markets to build a specialized software solution. We didn’t get distracted by technology for technology’s sake; instead, we solved specific problems for commodity traders.

Similarly, in today’s Gen AI era, Indian founders have an incredible opportunity to do the same — build solutions rooted in domain expertise.

Today, we have over 6,500+ companies focused on various markets, building solutions for India and the world.

This legacy provides a strong foundation for India’s push into verticalized Gen AI applications.

Five Key Areas for Vertical Applications in the Gen AI Era

1. The Evolution from Co-Pilots to Agentic AI

Co-pilots were among the first AI tools to become mainstream, offering real-time suggestions and assistance. Now, we’re witnessing the rise of agentic AI — autonomous systems that independently perform tasks based on predefined goals. These AI agents are transforming user experiences, and any vertical-focused startup needs to consider how agentic AI can streamline workflows in their domain.

Example: Composio, backed by Together Fund, integrates AI agents with APIs to help enterprises automate complex processes. This type of AI-driven automation is becoming essential for businesses across various sectors.

Market Insight: According to a report by Grand View Research, the global intelligent virtual assistant market size was valued at USD 2.48 billion in 2022 and is estimated to grow at a compound annual growth rate (CAGR) of 24.3% until 2030.

2. Automation of Software Engineering

Generative AI isn’t just about flashy new applications; it’s about transforming how software is built. From code reviews to testing and even documentation, AI can now automate large portions of the software development lifecycle, cutting costs and speeding up time to market. For startups, embracing this automation can mean the difference between surviving and thriving

Example: Dhiwise, a startup in our portfolio, helps developers build web and mobile applications rapidly by automating the application development lifecycle and generating modular, reusable, and readable code for Flutter, React, and Node.js.

Market Insight: Gartner predicts that 75% of Enterprise Software Engineers Will Use AI Code Assistants by 2028.

3. AI-Led Services Firms

AI is also transforming traditional services firms into AI-led services providers. Traditional services firms are evolving into AI-led service providers, significantly enhancing efficiency and scalability.

Example: Hunar.ai, one of our portfolio companies, uses conversational bots for hiring frontline and non-IT workforce. Its AI-driven approach has reduced turnaround time and improved recruiter productivity. Imagine an employer branded, WhatsApp verified bot that drives employer branding and candidate engagement at the same time! That’s how Hunar.ai is looking to transform a traditional services activity like hiring.

Market Insight: The global AI in recruitment market is projected to reach $2.6 Billion by 2033, from $0.8 Billion in 2023, growing at a CAGR of 12.4%, according to Market Research Future.

4. Healthcare Automation and Productivity

AI is revolutionizing healthcare processes, from diagnosis to administrative tasks. AI-led automation is revolutionizing document-heavy industries, creating efficiencies that were previously the domain of outsourcing firms.

Example: RapidClaims is transforming healthcare revenue cycle management with AI. Its platform automates medical coding, improving billing accuracy and reducing time spent on manual tasks.

Market Insight: The global AI in healthcare market is expected to reach $194.4 billion by 2030, growing at a CAGR of 38.4% from 2022 to 2030, as per Grand View Research.

5. Security and Privacy in the Gen AI Era

As AI systems handle more sensitive data, robust security and privacy measures are becoming critical. All organizations, big and small, need to focus on protecting personally identifiable information (PII) and ensuring compliance with global privacy regulations. This is especially important as more countries introduce data protection laws. Companies must integrate strong compliance layers to protect both their businesses and their clients.

Examples:

• Our portfolio company, Privado is shifting privacy left to engineers, adding privacy checks in the development lifecycle. Its Privacy Code Scans help engineers and privacy teams get visibility into personal data usage by their products and apps. They can also monitor personal data flows, and detect privacy risks that exist in the code from data leakages.
• Another startup that we have backed, Protecto, provides data security as a service, offering intelligent tokenization to help enterprises gain user trust in their Gen AI-powered products.

Market Insight: The global AI in cybersecurity market is projected to reach $60.6 billion by 2028, growing at a CAGR of 21.9% from $22.4 billion in 2023 according to MarketsandMarkets.

The Global AI Chessboard: India’s Unique Position in the Global AI Landscape

While India may not be leading in the development of foundational models, its strength lies in applying AI to solve specific industry problems. This approach aligns well with India’s historical strengths in software development and domain expertise across various sectors.

Compared to other tech hubs, India’s advantage is clear:

India’s edge? A perfect storm of a vast pool of software talent, deep domain expertise, and a massive domestic market for testing and refinement.

Challenges and Opportunities

Despite the promising outlook, Indian startups in the Gen AI space face several challenges:

1. Access to high-quality training data
2. Funding constraints for AI research
3. Competition from global tech giants
4. Regulatory uncertainties

However, these challenges also present opportunities:

• Leveraging India’s vast and diverse population for data collection and testing
• Focusing on cost-effective, scalable solutions for nascent and emergent markets
• Collaboration with global tech firms for early access to advanced AI models in a “coopetition” model
• Proactively engaging with policymakers to shape favorable AI regulations

Looking Ahead: Other Emerging Trends and Predictions

1. Hyper-personalization: AI-driven personalization will extend beyond e-commerce to sectors like education, healthcare, and financial services.
2. Edge AI: As 5G rolls out across India, we’ll see more AI applications running on edge devices, enabling real-time decision making.
3. AI in Local Languages: Expect a surge in AI applications catering to India’s diverse linguistic landscape.
4. Green AI: As environmental concerns grow, there will be a focus on developing energy-efficient AI models and applications.
5. AI Governance Frameworks: India is likely to introduce comprehensive AI governance policies, balancing innovation with ethical considerations.

Your Roadmap to AI Success: A 5-Step Guide

1. Find Your Niche: Focus on a specific industry or problem where you have deep domain expertise or understand deeply
2. Think Big, Start Small: Begin with a focused application, but design for scalability.
3. Win Your Niche: Leverage your focused solution as a thin wedge to win in your niche.
4. Leverage Ecosystems: Collaborate with investors, hyperscalers, incubators, and larger firms to run as frugally as possible.
5. Expand Your Footprint: Stay updated through workshops, conferences, and online courses

Conclusion: The Time is Now

For the first time, AI has the power to both create and reason.

With companies like Alphabet, Microsoft, and Meta continually pushing the boundaries of LLMs, there is immense potential to build domain-specific applications on smaller, more cost-efficient models.

And this is where India’s real strength lies.

The principles of the value SaaS era still apply today: focus on solving customer problems, ensure a high NPS, and grow alongside your customers.

But in this new era, success will come from not just building technology but applying it effectively in verticals.

India stands at a unique crossroads in the global AI landscape. We have the talent, the market, and now, the momentum. By focusing on verticalized AI applications, we’re not just participating in the AI revolution — we’re shaping its future.

The question isn’t whether India will play a significant role in the AI era. The question is: Will you be part of this historic transformation?

The AI tsunami is here.

It’s time to ride the wave or risk being swept away.

What’s your move?

Lakshmi will head the San Francisco office and focus on early-stage investments in AI startups in the US India corridor

Together Fund, a leading venture capital firm with a unique founder and operator-led approach, is excited to announce the appointment of Lakshmi Shankar as General Partner. This strategic addition reinforces Together Fund’s commitment to SaaS and AI, and building a robust US-India bridge.

Lakshmi Shankar brings over 20 years of experience in building and scaling enterprise technology and consumer-internet products. As the former VP of Product Strategy at Google, Lakshmi was key in the growth of Google Search globally and the launch of the Search Generative Experience (now ‘AI Overview’), Google’s AI response to OpenAI’s ChatGPT, Microsoft’s New Bing, and Perplexity.

Before his tenure at Google, Lakshmi held pivotal roles at Twitter (now ‘X’), where he collaborated closely with Jack Dorsey, CEO of Twitter, and Kayvon Beykpour, CPO of Twitter. Lakshmi was also central to incubating new growth bets and monetization opportunities, and led several strategic acquisitions of start-ups to accelerate Twitter’s scaling efforts and strategic direction. He also advised global CEOs across various industries on growth strategies at EY and started his career as a Software Engineer at IBM, earning numerous patents in the Enterprise Cloud space. Lakshmi is a Stanford GSB Sloan Fellow, Imperial College Alumni and an IIT Madras Visiting Lecturer of AI & Entrepreneurship.

“Together Fund is at the forefront of AI and SaaS, and Lakshmi’s addition to our team underscores our commitment to staying ahead in this dynamic field,” said Manav Garg, Founder of Eka and Co-founder of Together Fund.

Girish Mathrubootham, Chairman of Freshworks (FRSH) and Co-founder of Together Fund, added, “Lakshmi’s presence in San Francisco enhances our mission of building a robust US-India bridge. His insights and connections in the Silicon Valley tech ecosystem will be invaluable as we support and scale startups across these key markets.”

“I am excited to join a team dedicated to empowering the next generation of innovators,” said Lakshmi Shankar. “Together Fund’s unique approach and focus on AI align perfectly with my vision for the future of technology.”

With Lakshmi Shankar on board, Together Fund is set to further solidify its leadership in AI, driving innovation and supporting startups that are transforming industries.

About Together Fund: Together Fund is a premier venture capital firm led by founders and operators, committed to empowering startups with the resources, expertise, and network they need to scale and succeed. With a strong focus on AI and SaaS, Together Fund partners with visionary entrepreneurs to drive transformative change.

Think about it. In the past two decades, India has produced very few exits in the software product industry. Cisco bought out cloud communications software venture IMImobile in 2020. Idera acquired data visualisation start-up FusionCharts a year later. Freshworks got publicly listed on the NASDAQ in 2021. But you have to cast your mind back to 2005 when Oracle bought out banking software venture i-flex Solutions, or when Sterling Commerce acquired supply chain software firm Yantra. Most acquisitions of product companies from India tend to be acquihire transactions for buyers.

From the outside, the time period of a start-up exit catches the common eye. By most accounts, it is described as a progressive — almost step by step — outcome for founders in presumably normal circumstances. So, the question of timing is what repeatedly surfaces.

Nothing can be farther from the truth.

The myth of perfect timing

If there is one thumb rule that has served me well, it is that trying to time the market perfectly is an exercise in futility. It is an elusive goal. We founders are far better off channelling those energies to build leverage by making our ventures profitable. That presents an array of options for all stakeholders concerned — most of all, you, as Founder and CEO.

Coffee farms in Vietnam — Growth of coffee cultivation in Vietnam in 2000s provided an edge

My early days in commodities trading taught me the pitfalls of chasing perfect timing. Managing large customers like Nestlè and Lavazza and keeping an eye on global coffee markets, I learned that unseen variables can drastically impact outcomes. For instance, coffee traders often sell high and buy low, but unforeseen events like Brazil’s devastating frost in 2021 can upend these strategies.

Instead of trying to predict the market, I focused on managing risks and maintaining liquidity. This approach allowed for flexibility and better decision-making, a principle that applies equally well to running a company.

Profitability as leverage

The same is true while running a company as well. As an entrepreneur, I took the experiences from my commodity trading days to heart. You can never find the perfect bottom or the perfect top. Instead of solving for timing, what the best founders do is ensure that the company gets profitable at scale. It is the single biggest metric, which gives founders the leverage they want.

So, capital allocation — as opposed to raising more capital — becomes super-important for your company. That position emanates from profitability. Raising capital is not an endless game. It was an option for founders in India for 12 years when the cost of capital was near zero. But that changed in May 2022, when the US Federal Reserve raised interest rates by 50 basis points — the largest increase in 15 years — to tackle inflation. Since July 2023, its key benchmark index has been at 5.25% to 5.50%. This has significantly impacted the capital markets globally.

When profitable, company founders can continue to invest in the business at the pace he or she wants. At the same time, if you have external investors, you can discuss the options of an exit with the board of the company and other investors. (More on this later.)

But first, as far as risks are concerned, you have to answer the fundamental question: do I want to exit or not?

If the answer is that you want to run the company for the longer term, the company’s profitability empowers you to provide an exit to external investors. This is your responsibility: to give them the option of an exit. You can explore the following options.

• Initial Public Offering (IPO): A public listing in India is a solid option for companies that are less than $100 million in revenue as RateGain Travel Technologies demonstrated in December 2021. A public listing in the United States’ stock exchanges requires you to meet a higher revenue threshold, as Freshworks did in September 2021. However, an IPO is not an exit for the founder/CEO.
• Debt to buy investors: The second option is to use debt markets, and buy out your investors. As long as you are fair to them, investors are always happy to discuss an exit.
• Strategic sale: One can evaluate between a strategic investor or financial investor. You can consider a part sale or full sale, where you bring in a new investor or a large private equity firm respectively. Even in a majority secondary buyout, you can sell part of your shares while retaining most of your share. Or you can, as I did, sell your entire stake to a strategic investor.

So, there are a variety of options for founders. Once you decide the option you want to take, the process takes over. But all these options become available based on how well you are solving for profitability.

Exit trade offs

My experience of an exit

In the case of Eka Software, the number of buyout offers began to grow in 2019, especially because Eka Software had strengthened its technology platform on cloud for commodity trading and risk management (CTRM).

We had built a strong management team and systems to address the market and changing environments, even as consolidation was rife in the CTRM software industry. The delegation to management was a vital milestone for Eka if I had to take a step back from day-to-day management in 2022, especially in operational matters of the business.

As a founder or co-founder, you have seen the venture grow from scratch. But as the company grows, you are mindful of the many interests at play when it comes to where to take your start-up. So a decision,like a strategic sale, needs to be taken in the light of these interest groups.

• Ensuring customer continuity

For customers, how robustly your management has designed the systems and processes becomes the best proof of continuity amid an ownership change. The market needs to know the company is ready to outlive the founder.

• Reassuring management team

For key management personnel, it is important to sensitise them about larger changes afoot, reassure them about the rationale and their significance to maintaining business as usual. You need to take them into confidence about what you’re thinking — and what’s in it for them,

• Engaging the board

For the board of the company, discussing your buyout offers and listening to their inputs is important for the strategic direction of your venture. Eka Software had large institutional investors and a family office. So, the feedback from each investor was unique, given their varying time horizons on investments. It called for introspection.

Let’s spend some time on the value of discussing options with the board of directors. This is because founders might feel hesitant to discuss the subject, as it may be perceived as a weak signal of his or her long-term aspirations for the company. There is the latent fear that you may get fired. But offers are an important theme for direct conversations (‘Why now!’ and ‘What next?’) with investors and board members from time to time.

As founders, decide what is directionally best for the company, so that value continues to accrue with or without you. Then, it’s about getting the buy-in of most — if not all — stakeholders.

If your business is in good health with a strong track record of using your technology dollar, there is little to fear about such discussions. Performance is the best signal of your ability and stamina as a founder. For example, Eka Software had raised $45 million over a decade and a half. As it was profitable in high double digits, the B2B venture could fuel the R&D and innovation pipeline between 2016 and 2020. We could also hire the best enterprise technology talent — and sales folk — in the market. For a B2C venture, the metrics could well be high growth, if not upfront profits.

I had been through the experiences of turning Eka profitable and turning it into a cloud product. So, the exit offers were an easier matter to discuss with the board, keeping in mind how investors also need an exit at some time. Be mindful that you are selling a company which is a living organisation. That empathy is imperative. It’s not a transactional matter.

The process : How it played out

We started talking to potential buyers two years ago. We soon discovered that buyers are unfamiliar with software product ventures that are built from India. In addition to this, I gauged what a potential buyer needs: readiness of finance operations, compliance around the globe, customer satisfaction, steady pipeline conversion, and above all a good gross margin and profitability profile.

Let’s face it: India is still building its brand as a software product nation, as opposed to the US where buyers and investors have a pool of more than 5,000+ revenue producing software product companies at any time. Large US companies use M&A effectively to grow. The M&A playbook for the product landscape in India is still developing.

As a result, Eka spent up to 18 months demonstrating its capabilities to run a software product company out of India. This is the effort that goes beyond the standard due diligence –- since 2022, it became a strategic priority for me as a founder.

In all, Eka Software spoke with 50 prospective buyers, including strategic buyers (enterprise technology companies). The list narrowed to three or four companies in 2023. By August last year, I decided who the buyer would be: STG Partners, as they had a three-year thesis on CTRM software. In 2022, the private equity firm had acquired Brady Technologies’ commodities business. STG Partner’s Quor Group thesis on CTRM was an ideal fit for Eka’s next stage of growth — it is a call my board backed me on.

The whole process took around 18 months. As a founder, if you want to sell the start-up you have built, you want the right buyer. With its management — and a world-class product and engineering team — Eka Software is ready to be the platform to consolidate CTRM industry. I am available as a board advisor to the Quor Group, as Eka becomes a strategic part of it.

Emotional detachment

Even as I prepared to move on from Eka Software last year, I realised the value of being bold in building a new identity around the next opportunity that excites you as a founder.

Founders often struggle to understand when is the right time to sell because your startup becomes your identity over the years. If you are running a profitable business, you can run it forever as long as you run the organisation well.

When the founder’s identity overlaps the company’s identity, the decision naturally becomes a highly emotional one. So, a key aspect of a successful exit is to detach yourself emotionally from the company at some point in time. It’s easier said than done. But this is really, really important, especially for founders from India because we tend to be more culturally rooted with the company we build, which makes the decision to move on harder.

Looking ahead

For nearly two decades, my identity was inextricably linked with Eka. But I now feel ready to start afresh in areas like A.I. In a sense, the Together Fund with Girish Mathrubootham and SaaSBoomi — the community mission of creating global software product companies from India — have given me platforms to apply my skills and experience for a larger and more meaningful cause. These have worked for me in terms of ‘what next?’.

At the same time, I am embracing the path of working with complete freedom on technology ideas that excite me — and then, build a strong and sustainable business around them. As an entrepreneur, it is liberating to begin anew with a primary focus on innovation, before arriving at the phase of managing the stakeholder expectations. We are all excited about the next wave. I will miss Eka, but I leave it knowing it’s in good hands.

By Manav Garg (Founder, Eka Software and Co-founder, Together Fund)

Formation of SaaSBoomi

At the time, we reflected on the strides that the Software as a Service (SaaS) community in India had taken over five years. We had met during the formation of SaaSBoomi in 2015.

SaaS founders like Girish, Krish Subramanian of Chargebee, Suresh Sambandam of Kissflow, Avinash Raghava and I were passionate to a fault about making India a ProductNation — a vision that India’s software products industry will emulate the success of its world-class IT services industry. That led us founders to form SaaSBoomi, a community of, by and for SaaS founders that is rooted in a pay-it-forward and giving back culture. Girish and I are part of the SaaSBoomi founding team and governing council.

SaaSBoomi has been vital in not only creating the playbook roundtables for SaaS but key pillars of scaling a SaaS company from India.

Angel investing and helping founders

By 2019, both of us had more than 100 angel investments — in ventures like Zenoti, Chargebee, Unbxd and Whatfix. And, SaaS had entered the start-up mainstream. Little wonder, many of our weekends were devoted to helping hundreds of founders.

In the SaaS ecosystem, there were four companies from India with annual recurring revenue (ARR) between $100 million and $500 million (Zoho, Freshworks, Icertis, Druva), at least another four ventures with ARR between $50 million and $100 million like BrowserStack, and 30+ companies between $10 million and $50 million. Overall, India had over 150 SaaS start-ups that were generating revenue of more than $1 million in 2019.

Girish would get busy making Freshworks ready for its initial public offering (IPO) — a seminal moment for India’s SaaS industry in 2021. I got occupied in scaling Eka cloud offering in a traditional vertical.

Birth of Together Fund

It was clear to us that the community needed a fund led by operators like us to spawn world-class companies from India. Like Olympic athletes needing consistent training and preparation, startups require the right elements at the right time and place to become category leaders or creators and scale to $1 billion in ARR.

Our thesis was clear: while there was plenty of capital in the ecosystem, no entity provided the necessary operational help to founders. Girish and I wondered if we could repeat the success of Freshworks and Eka Software, and multiply the impact. Could we build an institution, which can help entrepreneurs with different aspects of building a business?

By 2021, we decided the answer was an emphatic ‘Yes’, with the same nervousness and trepidation that we had experienced as founders on Day 1 of our respective start-ups.

That’s how Together Fund was born.

In 2021, we onboarded Shubham Gupta to join as the Founding Partner of Together. Shubham was a SaaS investor at Matrix Partners India. Further, we reached out to 150+ operators in India and Silicon Valley who would help our portfolio companies scale.

Together Fund’s unique approach

We have chosen to differentiate ourselves by leveraging our strengths as operators to help founders face unique growth challenges. We’ve experienced these challenges first-hand as founders and CEOs — from go-to-market strategies and building sales organisations in the US to achieving successful exits. This muscle memory stems from Freshworks’ IPO on NASDAQ in 2021 and my strategic sale of Eka Software earlier this year. For instance, there is tremendous value operators can add from their experience when it comes to building an AI strategy, scaling a US organisation, or how to create a win-win proposition for all stakeholders in planning a start-up exit.

Based on our experience with the first Together Fund, we identified three key stages where founders need the most help:

1. Getting to Product-Market Fit (PMF)
2. Early Revenue ($0.5 million to $2 million)
3. Scaling to $10 million and beyond

For each stage, the go-to-market (GTM) strategy is critical. Many SaaS founders have successfully built solid products and marketed them from India, achieving inbound and product-led growth. However, expanding to enterprise clients requires a deeper understanding of the market, discerning buyer personas, and building an effective sales engine.

• Getting to PMF: We help portfolio companies with design partners, refine their customer value propositions, create categories, and identify the right buyer personas. Founders often ask, “How do we scale our product-led growth function?” and “What are the key drivers of ramp time and sales productivity metrics?”
• Early Revenue: We advise on GTM experiments to identify scalable channels and assist in hiring GTM personnel. Key questions here include when to hire an overseas salesperson, how to structure commissions, and determining the cost of customer acquisition over time.
• Scaling to $10 million and beyond: Our support extends to organizational structuring, hiring leadership teams, personal scaling of the founder, and fundraising. A common challenge is hiring a ‘VP — Sales’ in the US. Many founders lack experience in this area, making it a significant decision to hire or build a sales team in the US.

Together Fund’s goal is to help create scalable companies that can gain significant market share globally. That’s how SaaS and AI from India will grow.

Creating Olympian Start-ups

Our journey from founders to operators-turned-investors has just begun. Girish and I have transitioned from our CEO roles at Freshworks and Eka to fully focus on nurturing the next generation of SaaS champions through Together Fund. We’ve successfully deployed our first fund and are now excited to launch our second, an AI fund, which promises to unlock even greater potential.

Our operator DNA equips us to solve the unique challenges that SaaS and AI founders face, from go-to-market strategies to building robust sales teams in the US. India’s SaaS and AI landscape is ripe with opportunity. With over 6,000 start-ups, 15 surpassing $100 million in ARR, and many more poised for rapid growth, the future is bright. Together Fund is committed to transforming these potential-filled ventures into global Olympian champions.

As we look ahead, we are more excited than ever to create and support world-class SaaS and AI companies from India. The global IT services market has already demonstrated India’s prowess, and now it’s time for our SaaS and AI industry to shine even brighter on the world stage. Join us on this journey. Share your thoughts in the comments, let us know which Indian Olympians you’re cheering for in Paris 2024, and stay tuned for updates on our progress with the second fund. Together, we can achieve remarkable heights.

Manav Garg

Exactly two years back to this very day, we announced the launch of Together — India’s first operator-led VC fund — with an initial corpus of $85 million to help India’s best SaaS entrepreneurs build, scale, and win together, establishing India as a true Product Nation.

Today, we are announcing the first close of Together Fund II, our $150 million second seed fund, focused on SaaS.AI (SaaS & AI). Our first close has attracted institutional investors from US and Asia. Our focus areas continue to span Gen AI, SaaS, enterprise software, developer tools, open source software, cloud-native infrastructure, and API-first businesses. Together Fund II will continue to focus on seed and series A investments with cheque sizes ranging from $1–5 million.

In just two years, Together Fund has engaged with over 1,700 startups and invested in 20 exceptional founding teams across large horizontal and vertical SaaS categories as well as those creating new categories. Together also has a deep portfolio in developer tools. The portfolio ranges from first-time founders to experienced entrepreneurs and includes startups from major hubs like Chennai and Bangalore to companies in Tier 2 cities like Surat. Six companies (Kula, Privado, RevenueHero, Spendflo, Spry, Toplyne) have successfully raised follow-on funding rounds from prominent global investors.

It is a testament to their progress that we have been able to close a new fund in such a short span of time, even in this harsh funding and macro environment.

We are grateful for the opportunity to partner with such high-quality teams and for having played a supporting role in their stellar success stories.

“The Together team is a true extension of Spendflo. All the way from sales/positioning to hiring, they help us get the right answers from the right experts. They truly have your back and give us the confidence to build and scale”, Siddharth, Co-founder of Spendflo.

“Together played a pivotal role in helping me build global healthcare SaaS from India. True to their name, they have helped me in every aspect of business, encompassing GTM, product strategy, and organisational development. Their commitment to supporting the founders is phenomenal. They have struck the right chord between business ground realities and investor expectations” — Brijraj, Founder of Spry Health.

We are at a liminal moment in history — a period of transformative transition where every business wakes up to the power of AI. Amid this paradigm shift, all of SaaS will transform to SaaS.AI — every startup leverages AI in a meaningful way to provide new solutions that are vastly superior to the status quo.

Similar to the sea changes seen previously in the mobile and cloud eras, the SaaS.AI era represents an inflection point where entire industries will get disrupted or created from scratch, opening up innumerable opportunities for ambitious startups to create iconic companies. Our purpose as a founder-first venture fund is to stand together with these startups and give wings to their dreams with capital, community, and operational know-how.

Over the last few months, we have witnessed first-hand the spectacular rise of Gen AI and the consequent cambrian explosion in AI-first companies. The new world will create breakthrough opportunities for startups to build AI-native solutions, form factors, business models, and even entirely new software categories. The pace of change is exponential and entire markets could be created and captured by early pioneers in a matter of weeks, if not months. It was immediately clear to us that these audacious founders would require bold but loyal investors with deep operating experience to assist them in guiding and accelerating their journey.

“In light of this once-in-a-generation SaaS.AI opportunity, we are extending our founder-first philosophy with our second fund, supporting founders from the inception stage of their journey,” said Girish Mathrubootham, founding partner of Together Fund. “We are excited to partner with these audacious founders who need bold and committed investors with extensive operating experience to guide and accelerate their journey.”

Over the last three months, Together Fund has already invested in three Gen AI companies in healthcare, developer infrastructure, and modern marketing stack.

“Together is truly the startup of funds. We’ve been privileged to partner with the Together team to build a large earnest SaaS business out of India. Everyone at Together has been a true partner in our journey, in both word and spirit.” Akshat, Founder of Workhack.

Building startups is hard in any age, but it is all the more daunting in an era of rapid change and transformation like the one we are currently in. We know it because we have done it in the past and are still doing it today! Together is the first and only VC fund in India led by founders. In parallel with being founding partners of Together, we run some of India’s most well-known SaaS startups. This makes us uniquely qualified to help you beyond just putting in capital. We offer our time and network to help guide you through the startup journey from building to scaling to winning. Actively being on the battlefield shepherding our own companies through the Gen AI waters allows us to have a ground-level and contemporary perspective on what works and what doesn’t, and we will share all our tribal knowledge with you to help you solve challenges with an informed opinion. We are the ideal VC partner for founders who believe this is more valuable than just capital.

We believe in serving as trusted partners to entrepreneurs at every stage, always striving to watch the founder’s back and act as their first port of call. We consider ourselves a full-stack VC, with our investors and founder amplification leaders helping with strategy, hiring, customer introductions, and follow-on financings. The earliest stage of company formation, when often all you have is a team and an idea, is one that has always excited our team of founders turned investors.

“Raising our second fund speaks to the remarkable progress of our existing portfolio and the technology ecosystem in India, even amidst challenging funding and macroeconomic conditions,” said Manav Garg, founding partner of Together Fund. “The bright spot ahead in the business landscape is AI, and we believe it’s becoming an increasingly indispensable asset for every enterprise to revolutionise existing solutions and deliver unprecedented value to customers.”

At Together Fund, we already have an informed opinion about how SaaS will transform into SaaS.AI in the age of Generative AI. SaaS. AI doesn’t imply that only AI-first or AI-native startups will succeed; it also provisions for companies to adopt AI meaningfully to radically change and improve all aspects of the development and delivery chain. We have a number of initiatives to help companies make this transition seamlessly — from regular AI mixers with deep discussions on AI products and best practices to our Foundation WhatsApp group for AI enthusiasts. All our companies will benefit from access to the Zone, our vast network of over 200 best-of-breed experts on AI and other strategic and tactical aspects, and a proven value-add to amplify impact by accelerating their journey from idea to escape velocity.

Looking forward to serving as early-stage partners to AI-first founders who are dreaming big and riding the Gen AI wave.

Let’s Capture the SaaS.AI Moment Together!

As the ChatGPT-fuelled AI zeitgeist sweeps through the world, how will it impact the world of SaaS? How will Indian SaaS startups adapt to this new era?

Where are the emergent markets? While this technology platform shift will throw up enormous opportunities, the traditional Indian SaaS playbook has to be adapted to the new AI order. Founders need to capitalize on this generational shift by adopting a first-principles foundational mindset coupled with rapid and bold execution.

The Age of AI

If there is one term that has captured the imagination of the entire world over the recent past, that term is undoubtedly “Generative AI”. While artificial intelligence and machine learning have been topics of tech research for decades, it is generative AI that has heralded the “Age of AI” — a transformational moment that is radically changing the way that software is created and consumed.

And nothing captures this “The Age of Artificial Intelligence” zeitgeist better than the rise of ChatGPT.

ChatGPT — AI’s iPhone Moment

It’s been about six months since ChatGPT was released as a consumer product. A lot has happened in a frighteningly short span of time. A hundred million people have used the product, it has made its way onto every software product roadmap, reignited the long-dormant search wars and arguably destabilized the most dominant company of the internet era, Google unleashed a flurry of investment dollars in an otherwise bearish funding environment and left pretty much every company grappling with how it will alter the fundamentals of their business and the future of work.

We are at the beginning of a tectonic platform shift in technology. If Apple’s iPhone launch was the epochal moment of the mobile revolution, the launch of ChatGPT is AI’s iPhone moment. There has never been anything to parallel the pace and momentum with which it is capturing the world’s imagination.

ChatGPT took 5 days from launch to get to 1 million users and is the quickest app in history to reach 100 million users. It is changing the way the world works, creates content and searches for online information. ChatGPT’s actual impact could possibly surpass even the biggest technology paradigm shifts such as the cloud and mobile in terms of scale, scope and speed. A recent report by Goldman Sachs predicted that generative AI could raise global GDP by as much as 7% over the next few years. To appreciate how huge that impact is, consider that the personal computer’s contribution to GDP was 0.006%. AI could potentially have the type of global impact that electricity and the steam engine had in terms of productivity changes.

AI — Hype or not?

To understand why this hype is justified, we need to pay attention to three non-obvious facets of ChatGPT’s rise.

First, ChatGPT’s main innovation is the user interface or rather the lack/simplicity of it. That might seem incongruous given the fact that there is arguably nothing new or innovative about a chat messaging UI as such and chatbots, both text-based as well as voice-based (Siri, Alexa etc) have been around for a while. But as we now see retrospectively, the combination of a simple universally-familiar user interface abstracting away the power of GPT contributed to ChatGPT’s massive adoption. Furthermore, it has set a benchmark for the ease of use and simplicity that users will increasingly expect from all their applications. It is changing end-user expectations around automation and app user experience

Secondly, the biggest indicator that AI, in general, is here to stay is the universal excitement and enthusiasm with which the world, in general, has adopted ChatGPT — not only is social media abuzz with interesting uses and applications of ChatGPT and the broad ecosystem that is rapidly coalescing around the core foundational models, every company from small startups to behemoths are actively exploring ways to incorporate AI into their offerings. Lock in step, the amount of funding that is looking to back generative AI companies has gone to stratospheric levels. Analysts at research firm PitchBook predict that venture investment in generative AI companies will easily be several times last year’s level of $4.5 billion.

Finally, there is no doubt that the success of ChatGPT establishes AI as an idea whose “time has come” — a perfect storm of market dynamics and technological progress. As the chart below illustrates, the underlying technology foundations have improved steadily for many years but have reached a tipping point in the last two years with a sharp increase of capabilities powered by progress in both algorithmic as well as compute models.

And to think, we are still just at the beginning of the age of AI — the rate of growth from this point could well be exponential over the next few years completely changing every facet of work and life. This seismic shift is a golden opportunity for nimble and ambitious companies to create iconic companies.

AI’s seismic shift

“There are decades when nothing happens, and there are weeks when decades happen.”

This famous quote is particularly applicable to the way in which AI is changing the world at the moment. While every technology wave opens up opportunities to create disruptive companies, the AI age is fundamentally different from previous waves like the mobile and the cloud.

How so?

While previous advances such as mobile and cloud computing followed an adoption curve that was limited to enthusiastic tinkerers and “nerds” in the early days, generative AI was useful right out of the gate for a much broader audience of “ordinary” people. The fact that the value of the answers from ChatGPT is instant and obvious and in many cases, astonishing enough to meet the high threshold for “advanced science that is indistinguishable from magic”. People across industries and job functions are tinkering with ChatGTP in myriad ways and sharing ways in which the tool has massively improved a specific job to be done. Everyone from school kids looking for homework help to grandmothers seeking cooking recipes is relishing the value of ChatGPT. There is possibly no precedent in terms of a technology advancement that has had so many relevant use cases for over a billion people within a few months of its release and equally importantly, for free.

While the first wave of generative AI applications resembles the mobile application landscape when the iPhone first came out in terms of being gimmicky and thin with unclear competitive differentiation and business models, the value of many of these applications is obvious and immediate. From writing creative marketing copy to generating homework to conjuring up stunning images from just a text prompt. Even though it is early days, these applications provide an intriguing glimpse into what the future may hold. Once you see a system produce detailed blog posts or complex code faster and with less effort than you could believe was possible, it’s hard to imagine going back to the “old” ways of how we used to work and live.

Additionally, there was a long-standing belief that AI will first automate manual and repetitive tasks such as data entry and other relatively simple tasks but robotics turned out to be harder than some parts of cognitive knowledge work. The spread of ChatGPT and generative AI has demonstrated that it will fundamentally transform every form of knowledge work starting top-down from “high-value” jobs such as software development, product management and marketing. The approximate nature of generative models makes them better than expected at creative work. Generative AI will fundamentally change every business activity from management consulting to movie production to customer support. PitchBook estimates the market for such AI applications in enterprise technology alone will rise to $98 billion in 2026. The remarkable advancements AI is going to make in the coming years with advancements in this field not growing linearly henceforth, but hockey-stick curves that will wipe out entire professions and careers and create entirely new categories of businesses and jobs. Early evidence of this shift is already visible. IBM announced that it will cut 7,800 jobs that will be automated by AI. Coca-Cola just released an advertisement that was created entirely by AI. So while there is hype around AI, the real-world impact and implications are already visible.

It is therefore essential to adopt a “first principles” approach towards parsing and understanding the opportunities ahead. The ability to adapt nimbly and take advantage of emergent opportunities will be key. Far-fetched scenarios that seemed like science fiction just a few months back have not merely entered the realm of the possible, they seem all but inevitable now. AI will play a key role in shaping not just how enterprises work but also how humans work with the software itself.

This shared epiphany has invigorated tech ecosystems all over the globe from the Bay Area to Bangalore. From startups to big tech, everyone is sprinting.

So where are the opportunities? Who has a good chance to win the race? Incumbents or startups?

To answer these questions, we need to first parse the overall landscape.

Parsing the AI Landscape

There are two broad layers in the AI ecosystem.

The first is the “Model” layer. This includes all the infrastructure needed to build the foundational layer of AI — it includes databases, networking, & compute.

The second is the “Application” layer. These applications learn and generate content, work, and emulate actions for many tasks.

This landscape map by Sequoia Capital provides a succinct overview of the categories and dimensions of the model and application layers.

One aspect that stands out in this map is that the model layer pits companies against all the major tech behemoths — Microsoft (OpenAI and Azure), Google (Bard. PaLM), Amazon (Sagemaker), and Facebook. This is not surprising given the nature and size of the opportunity before us — it is essentially an opportunity to become to the AI ecosystem what AWS (Amazon Web Services) became to the cloud. Occupying a central role in the foundational layer will enable the winner to basically extract an “AI tax” for every transaction built through these engines. This “AI Tax” will decrease over time as the costs around foundation models reduce with scale and improvement in base metal GPU performance — the likes of OpenAI have already dropped costs significantly in the last 6 months. This dynamic makes it even more difficult to establish a competitive moat in this layer — playing at this table requires billions of dollars in funding and decades-long gestation periods and is therefore likely to be outside the purview of all but a few well-funded startups (the likes of OpenAI, Cohere, Anthropic, Stability.ai) that have raised hundreds of millions of dollars in funding with the knowledge that the base models and hence the business model can potentially be a commoditization play with a race to the bottom.

The immediate opportunity for startups is therefore in the application layer where companies like Copy.ai, Jasper, Midjourney and Runway have established early leads and built meaningful businesses and brands. While it is tempting to believe that these companies are going to remain leaders in the future as well, it bears notice that many, if not most, of these companies are equivalent to the early “toy” apps like flashlights and cat animations that were popular in the early days of the mobile era. The real opportunity is in front of us.

The only question is how a startup should think about competing in this age of AI — how should they pick a market and domain, how can they compete against incumbents and other startups, and how can they build a competitive advantage or moat?

Startups vs Incumbents — The race to add AI

With every major technology platform shift, many legacy companies get disrupted as they get caught flat-footed and are slow to respond to changes around them. Traditionally, startups had one key advantage against incumbents — speed — the ability to move quickly and nimbly. However, in the case of gen AI, speed doesn’t seem to be an advantage for startups.

Why so?

Previous technology platform shifts such as mobile and cloud computing were innovations that required incumbents to make large-scale changes, not just to their technology stack but also to their business model. Re-architecting and transforming a desktop application to a web application or mobile app was a resource-heavy and time-intensive effort. However, generative AI can be added to an existing product in a matter of days by integrating a simple API call without changing its tech architecture. As a result, it comes as no surprise that Microsoft was able to integrate generative AI into Microsoft Office and their Edge browser within months of the release of ChatGPT. Other behemoths such as Google and Adobe have made similar integrations within their apps just as quickly. As have large startups such as Salesforce, Notion and Airtable. The bottom line is that incumbents are able to move just as fast as startups in the AI race.

Also unlike previous platform shifts, large companies are not ignoring or underestimating the disruptive potential of AI. Every large technology company is actively incorporating AI across their product and even in places where they may have been previously short-sighted.

Beyond this, incumbents have the advantage of distribution and data — two key weapons of the AI battle. Incumbents can leverage existing data on which they can train their models or build industry-specific models or even customer-specific models. Unlike the cloud or mobile revolutions where new channels had to be built from scratch, incumbents can leverage their existing GTM and distribution strengths to reach customers. For instance, Google has as many as nine products with more than a billion users each — they don’t need to reinvent the wheel to expose these vast audiences to AI capabilities within their products.

If this was a boxing match, it wouldn’t be wrong to say that the first round has definitely been won by incumbents.

But the battle is not yet lost for startups.

The AI opportunity for SaaS startups

While incumbents have an advantage in the first round, their victories might have come with strings attached.

Take Google for instance. In a world where content can be generated instantly and infinitely by AI, how will SEO change? When users can find answers to their questions by chatting with AI, will they even click on online advertisements — if not, how will Google’s formidable SEM advertising business change? While the answers to these questions are moot, there is no doubt that Google will have to radically transform and reassess their business model to defend its legacy revenue streams. It is a tricky balance as making the wrong move can cannibalize existing businesses while not making any change at all is a sure-shot recipe for disaster.

Similarly, take Microsoft. In a world where a single person can create as much content as ten people would previously, does a seat-based pricing model make sense? Also, while layering AI features on top of existing products is an easy win, will it be enough for them to protect their turf? It is one thing to have AI as a feature within a larger product but it is entirely another to rethink and reimagine products in an AI-native form to fully capitalize on the benefits of generative AI. Will the likes of Salesforce, SAP, Workday and other incumbents be bold enough to take such radical steps to completely overhaul their legacy products?

Given this context, there are numerous opportunities for SaaS startups to innovate and win.

Let’s explore some of these opportunities.

AI-native applications — niche today, large tomorrow

Early AI-native applications like Jasper.ai and Copy.ai were widely perceived as being niche apps, mere “thin wrappers” around OpenAI’s GPT models, that catered to a small audience of freelance marketers. It was just a matter of time before these apps would stagnate or be killed by larger apps that incorporate these marketing copy creation capabilities within a broader product. But contrary to these perceptions, these startups have grown and prospered reaching tens of millions of dollars in ARR and even unicorn-level billion-dollar valuations. They arguably achieved this the old-fashioned SaaS way — by offering a well-crafted product with a clear and valuable value proposition to a targeted audience. AI might have changed the landscape of SaaS in many ways but the core ingredients for success don’t seem to be too different from the old ways.

Other AI-native apps such as Midjourney and Runway have also carved out clear winning positions for themselves. Midjourney is an 11-member bootstrapped startup that defies many conventions — its image creation capabilities are available only through a single Discord channel but it has managed to attract millions of paying customers and is said to be well over $100 million in ARR. Similarly, Runway is a video generation app that has attracted attention and audiences — including Hollywood studios that have used their technology to win Oscars. Both these startups also demonstrate the value of building a loyal community beyond building a clearly-positioned brand.

AI-driven workflow automation

Workflow automation is not a new idea but the generative AI boom has reinvigorated the concept by breathing new life into the idea. AI-driven automation will enable entirely new capabilities in areas which were not possible with previous-generation technology. They will disrupt other workflows and make possible new ones that can potentially increase productivity by 10x or more. The advent of AutoGPT and ChatGPT plugins has enabled the creation of a new ecosystem of workflows and integrations that can now power cross-application communication and end-to-end business use cases.

Autonomous agents are programs, powered by AI, that when given an objective are able to create tasks for themselves, complete tasks, eliminate bottlenecks, reprioritize their task lists and loop by themselves until their objective is reached. They can range from specialized functions such as lead generation workflow or can take up a much broader role similar to all the tasks an SDR (sales development representative) would perform. AutoGPT has surpassed 100K stars on GitHub, making it the fastest-growing open source repository ever and several startups such as Inflection AI and Adept have emerged as early leaders in this space, attracting tens of millions of dollars in funding with ambitions to offer “intelligent omnipresent companions” to millions of users over the next few years. Langchain, a popular open-source framework for building with LLMs, has built integrations to agent projects like BabyAGI, CAMEL and AutoGPT and all these are available as loosely-coupled frameworks that startups can innovate on top of. While these systems are already powerful, the ultimate aim is to allow anyone to merely issue a simple instruction such as “Book me a flight from Bangalore to San Francisco and a hotel room for my stay” and have an army of personal autonomous agents perform the task for you. Imagine how many apps and platforms such a workflow will disrupt. While LLMs made us rethink everything from marketing to medical research to software development, and even what it means to be creative, agents will multiplex this impact, redefining how we interact with technology and with each other, opening up new vectors of possibilities and opportunities for nimble startups to leap-frog previous generations.

AI-powered dev tools and infrastructure

One of the industries that are likely to see the biggest impact from AI is the world of software development and programming itself. It might seem counterintuitive at first glance but in hindsight, it should not be surprising at all. A large corpus of open-source software code has been publicly available for LLMs like GPT-4 to ingest and index. This is why AI coding assistants like Github’s Copilot have proven themselves to be excellent augmentation tools for developers. By some estimates, Copilot is already automating 40–50% of code for many developers by abstracting away boilerplate code from manual development. We are facing a future where the output of software engineers is likely to increase ten-fold within the next decade — imagine a world where every developer is a 10X engineer! OpenAI’s ChatGPT can already pass Google’s exam for a high-level software engineer and the scores are continuously increasing.

In this brave new world, every layer of the dev tools and infrastructure stack is up for grabs. New AI layers will be added to the stack ranging from LLMOps and ML workflows to AI-driven software testing and automation. Given the impact of LLM costs on gross margins, every SaaS firm is likely to want to adopt frameworks and tools that will help control and optimize these new backend costs. ChatGPT’s Code Interpreter and ChatGPT plugin’s drop-in API architecture will add new layers around the capabilities of software solutions and the surrounding ecosystem.

The world of programming itself is likely to see major changes over the next decade with a lower bar to entry and a large set of hobby developers emerging — folks who don’t necessarily have extensive experience in coding but can leverage coding assistant tools to whip up single-use or temporary software solutions. Roles around data analysis and visualization are also likely to be democratized by AI.

AI and IT services

How will the emergence of generative AI impact the world of IT services, an area that India has been traditionally strong at? At one end of the spectrum, there is a belief that outsourcing will be negatively impacted by AI with automation replacing manual workers. While that may well be true to a limited extent, most large enterprise customers have concerns and considerations around security, privacy and reliability which might not be achievable with a hallucination-prone AI solution. On the contrary, at the other end of the spectrum, there are a number of new opportunities around consulting and digital transformation that will emerge around enterprises seeking to adopt AI.

These enterprise-level use cases are potentially a large business opportunity for services-led startups that can help customers to leverage existing data sets and develop specialized LLM or fine-tuned models. Many Indian companies, small and large, are already working on transformation projects and developing solutions in the AI and Intelligent Automation (IA) space, assisting enterprises to meet demand, improve efficiency, and implement smarter business models.

AI-driven vertical SaaS

Vertical SaaS is a nascent area that has seen tremendous growth over the past few years — these startups focus exclusively on a single industry or domain and build deep solutions tailored to their specific needs. Vertical SaaS startups such as Veeva have built large businesses adopting this approach. AI is likely to have a major impact on vertical SaaS — areas such as healthcare and industrial manufacturing will see major gains. Healthcare is seeing the emergence of vertical LLM service providers such as Hippocratic while manufacturing companies will benefit from generative AI’s strengths around supply chain planning and simulations.

In fact, there is not going to be any vertical Saas domain that is unlikely to be unaffected by the emergence of generative AI. Startups such as Harvey.ai and EvenUp are revolutionizing the legal industry by automating contract analysis, due diligence, litigation and regulatory compliance and augmenting analysis and synthesis that previously required manual labour. Similarly, models such as BloombergGPT are emerging in the financial services sector. Vertical saas players have an inbuilt advantage by virtue of having access to proprietary data that can be used to build fine-tuned models that offer better results compared to other competitors.

The end game for vertical SaaS is also likely to be very interesting — when software development costs trend towards zero, innovative startups can target and build for $10m TAMs instead of $100 million or $1 billion ones. SaaS solutions that were traditionally perceived as being subscale or VC-unfundable can deliver positive unit economics and efficient business models with very small teams.

Reimagining UX in a World of AI

While all of the above are specific opportunities, there is possibly a broader opportunity around UI (user interface) and UX (user experience) that could potentially change every SaaS application that exists today. Many, if not most, SaaS applications use the familiar paradigm of “forms” that help users view, interact with and update data from some backend data source. This common UX pattern can be seen across every major SaaS domain from CRM to support and from project management software to ERP.

These form-based interfaces might have reached their expiry dates.

ChatGPT is an early indicator of how AI will revolutionize UX. A chatbox where the user can just ask the system what he wants using natural language, textual or verbal, is a major step up from clunky forms. These affordances are much simpler and more powerful than manually entering data into a form. Imagine a world where every SaaS application is front-ended by a chat interface where you can just tell the application what you want it to do.

But this is just the beginning.

Generative AI will usher in a world of hyper-personalized UX and possibly even screenless UI where users interact with other modalities. In fact, LLMs can themselves self-generate the interface at run time as apps such as Perplexity.ai are demonstrating already. Others such as Humane are facilitating effortless navigation in unfamiliar environments along with providing personalized recommendations and seamless communication across all languages aided by the power of AI.

AI is fundamentally changing the mental models that we have built around SaaS UI and UX over the last two decades. Startups that win by innovating on UX in this new era of invisible interfaces and AI-enhanced experiences can emerge as big winners in the future.

The final word — “Think Fundamental, Think First Principles”

AI will fundamentally transform every form of work. Every company will be an AI company. Or risk getting disrupted by one.

The new world will create breakthrough opportunities for startups to build completely new AI-native solutions, new form factors, new business models and even entirely new software categories. Traditionally, Indian SaaS startups have adopted the “fast follower” approach — they benchmark against a successful US SaaS startup and offer a comparable solution at a lower cost but higher value. In the age of AI, this approach is not likely to work. The pace of change is exponential and entire markets could be created and captured by early pioneers in a matter of weeks if not months. These pioneers could even be incumbents who have recognized this generational opportunity and have bet the farm on AI being a critical component of every aspect of work and play in the near future. While there are going to be countless opportunities for new players, the key for Indian SaaS startups is going to be in terms of thinking from first principles to leverage AI to innovate in fundamentally different ways and execute quickly and boldly.

There has never been a better time to build a SaaS startup and especially so, from India.

Let’s go and win the age of AI.

By Manav Garg

SaaS in the Age of Generative AI was originally published in Together Fund on Medium, where people are continuing the conversation by highlighting and responding to this story.