The loudest story in artificial intelligence is a consumer story. Chatbots with hundreds of millions of users, app-store records, viral demos, and valuations that read like phone numbers. It is a story about eyeballs. But there is a quieter story unfolding in parallel — one about contracts, compliance officers, and software that never touches the public internet. Canada’s Cohere has spent the last few years betting almost entirely on that second story, and the wager is starting to look less like a niche and more like a blueprint.
For India’s emerging cohort of applied-AI and B2B builders, Cohere is worth studying not because it is the biggest name in the room, but because it is deliberately not trying to be. Its thesis — sell to enterprises, not eyeballs — is a strategic choice with real implications for where defensible value in AI actually accrues.
A deliberately different bet
While OpenAI, Anthropic and Google compete for consumer mindshare and the headlines that come with it, Cohere has positioned itself as the B2B alternative — an enterprise- and deployment-focused company rather than a maker of mass-market assistants. As BetaKit has reported, the company has built momentum precisely by refusing to fight on the consumer battlefield, leaning instead into a differentiated thesis aimed squarely at large organisations.
The wedge in that strategy is deployment. Most consumer AI lives in someone else’s cloud, accessed through an API, with your prompts and data flowing to a vendor’s servers. Cohere’s pitch inverts that: models that can run privately, on-premise, or inside a customer’s own virtual private cloud, so that sensitive data never leaves the organisation’s control. For a consumer, that distinction is invisible. For a bank, a hospital network, or a government department, it is frequently the entire purchasing decision.
This is also a bet on staying out of the spotlight. There is no daily viral moment in selling inference infrastructure to a telco. The momentum builds in procurement cycles and pilot expansions rather than trending screenshots — slower, less glamorous, and arguably more durable.
Why enterprise is defensible
The strategic appeal of the enterprise bet comes down to a simple question: what is hard to copy? Consumer chatbots, for all their reach, sit in a brutally competitive market where the underlying capability is increasingly commoditised and users can switch with a tap. Enterprise AI, done right, builds moats that are far harder to cross.
The first is data control and compliance. Regulated industries — finance, healthcare, defence, the public sector — operate under rules about where data lives, who can see it, and how it is audited. A vendor that can deploy models inside the customer’s own environment, conforming to data-residency and sovereignty requirements, is not just offering a feature. It is offering the only acceptable architecture for buyers who legally cannot send data to a third party. That requirement instantly narrows the field of viable competitors.
The second moat is workflow lock-in. Once an AI system is wired into a company’s internal documents, ticketing systems, knowledge bases and approval chains, ripping it out becomes expensive and risky. The switching cost is not the model — it is everything built around it. Investor commentary tracked by industry analysis (GrowthList, directional) makes the financial version of this argument: workflow-deep enterprise products with proprietary data tend to command materially higher multiples than thin consumer wrappers, precisely because they are sticky and hard to displace. The defensibility shows up in the valuation.
The third is regulated-industry demand itself. The organisations most constrained by compliance are often the ones with the deepest pockets and the most acute need for automation. They cannot use the off-the-shelf consumer tools, which means there is a structurally underserved market of buyers actively looking for a vendor who will meet them on their terms. That is a far more comfortable place to sell from than a free-tier price war.
The challenges
None of this makes the enterprise path easy. It comes with its own set of hard problems, some of them existential.
The first is distribution. The hyperscalers — Microsoft, Google, Amazon — already sit inside the IT stack of nearly every large enterprise on earth. They bundle AI into existing cloud contracts, offer it through familiar billing relationships, and reach the buyer through sales teams the customer already trusts. An independent enterprise-AI company has to win deals against incumbents who can effectively give the capability away as part of a larger package. Out-engineering the giants is not enough; you have to out-position them.
The second is capital and compute. Training and serving competitive models is enormously expensive, and the enterprise approach does not generate the viral consumer growth that justifies the largest funding rounds. Building privately deployable, customer-hosted systems can be more operationally complex than running one big centralised API. This is a capital-intensive business pursued without the consumer flywheel — a demanding combination.
The third is talent. The pool of researchers and engineers who can build frontier-grade models is small, globally mobile, and aggressively courted by companies offering enormous compensation and the gravitational pull of the most-watched labs. Recruiting and retaining that talent while staying disciplined about an unglamorous enterprise mission is a continuous challenge, not a solved problem.
The India read
For Indian founders, the Cohere story lands differently than it does in San Francisco — and arguably more usefully. India’s most credible AI opportunity has never realistically been to out-spend the frontier labs on a consumer chatbot. It has been to build applied, B2B systems that solve specific, valuable problems for specific, paying customers. That is exactly the lane Cohere has chosen.
A growing cohort of Indian applied-AI startups is converging on the same logic. Rather than competing for global consumer attention, they are building vertical tools for lending, insurance, logistics, healthcare administration and customer operations — domains where the value is in accuracy, integration and trust, not in raw model fame.
Enterprise and government make a natural beachhead for this approach in India. The public sector’s digital-public-infrastructure ambitions, combined with strict expectations around data localisation, create demand for AI that can be deployed within Indian environments and Indian rules. Large domestic enterprises — banks, conglomerates, IT-services firms — face the same compliance constraints that make private deployment a requirement rather than a preference. The buyers who most need a Cohere-style vendor exist in abundance here.
The deepest lesson, though, is about where to compete. The Cohere template suggests that the durable value often lies not in owning the foundation model, but in owning the workflow built on top of it. For Indian builders — many of whom will sensibly use open or third-party models rather than train their own — that is liberating. You do not have to win the model race to win the customer. You have to embed yourself so thoroughly in how an organisation actually works that you become impossible to remove.
That is the unglamorous middle Cohere has staked out: not the consumer headlines, not the pure research frontier, but the deployed, defensible, deeply-integrated layer where enterprises live. It will never trend. It may well prove to be where the money, and the moats, actually are. And for India’s B2B AI builders, it offers something rarer than hype — a strategy they can realistically execute.
