The Vertical Turn In Enterprise AI

As enterprises look beyond general-purpose AI, vertical foundational models are emerging as a cost-effective way to deliver specialised intelligence across payments, healthcare, agriculture, finance and other sectors securely at scale.

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Is enterprise LLM adoption in India going the SaaS way? Not even a decade ago, SaaS majors realised that India’s fragmented adoptive base for software products required dedicated vertical solutions. The same reality is now dawning on the AI ecosystem.

Vertical models like Razorpay’s Vulcan, Fractal’s healthcare reasoning models — as well as the establishment of its Indian business unit — and BharatGen’s domain-specific models for agriculture and the legal domain all point to a new direction. 

Earlier this year, Tech Mahindra unveiled an 8 Bn parameter Hindi-first LLM focused on education use cases, and designed for adaptive tutoring, particularly to address the structural English bias embedded in many global AI systems. This may not necessarily be an enterprise use-case, but it goes to show that vertical models are becoming relevant.  

Plenty more are likely to hit the market as software companies in particular look to capitalise on their data advantage.

It’s the clearest sign of a shift among software companies to venture into the AI model game. But it’s one thing to bank on vertical models, and another to sell to enterprises on a long-term basis. Enterprises will not completely abandon general-purpose LLMs and their agentic platforms, so the key will be to make the value amply clear. Once again, we are in the same place SaaS companies were a decade or so ago.

When ChatGPT Or Claude Are Not Enough

BharatGen CEO Rishi Bal says LLMs like ChatGPT, Claude, Gemini and DeepSeek are getting more and more powerful, but this is due to their versatility. This makes them over-engineered for a specific business problem, and in many cases, getting them to cater to specific needs ends up becoming expensive from a token point of view.

Vertical models are trained on the vocabulary, context, behaviour and logic of one domain, whether it is payments in the case of Razorpay or healthcare for Fractal.

Theoretically, these models should consume fewer tokens for the very particular needs of a merchant or a healthcare provider, or, in the case of BharatGen, agritech and legal services. The widely available popular LLMs can be tailored to these needs, but that means employing a team of LLM specialists in many cases or experimenting, which results in heavy token usage. Both are certainly more expensive options than deploying a vertical LLM.

That’s the pitch at least.

Vertical LLMs also solve the problem of access to AI. Smaller domain-specific models can be run efficiently on modest hardware, allowing startups and smaller enterprises to use AI without fine-tuning a frontier model or maintaining the infrastructure needed to serve one.

Under its Param series, for instance, BharatGen has built models including ParamAgri and ParamLegal, trained on Indian........

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