AI development company in India.

Most teams are treating that as a legal department problem. It is an architecture problem, and the difference shows up in cost. Three requirements are cheap to build in and expensive to add later.

Consent that is per-purpose and revocable. Not a checkbox at signup — a record of what this person agreed to, for what, when, and the ability to withdraw one purpose without withdrawing the rest. If your system decides what it may do by reading that record at request time, this is a week of work. If consent is implied by the existence of a row in a table, it is a rebuild.

Deletion that actually reaches everywhere. This is the one that catches AI systems specifically. A deletion request has to reach the primary database, the vector store, the cached embeddings, the conversation logs, the evaluation set someone built from production traffic, and any fine-tuning corpus. Teams routinely discover at audit that personal data they deleted two years ago is still influencing model outputs through an index nobody mapped.

A record you can produce on demand. What you process, why, on what basis, for how long, and who it goes to. Reconstructing this after the fact is archaeology; emitting it as you go is a logging decision.

Alongside DPDP sit two others worth designing for from the start. CERT-In requires certain cyber incidents to be reported within six hours of noticing them — which is an instrumentation requirement long before it is a legal one, because you cannot report in six hours what you find out about in a week. And the RBI's storage direction requires payment system data to be held only in India, which constrains where a model can be served from, not merely where a database sits.

None of this makes AI harder to build here. It makes it harder to build here carelessly, which is a different thing, and it is why we build the audit trail before the feature.

This is the part of building AI in India that is genuinely an advantage rather than a constraint, and it is chronically underused. A document-verification workflow that would need a scanning vendor and a manual review team elsewhere can pull a verified credential from DigiLocker. An onboarding flow that would take days of document chasing can use Account Aggregator consent to fetch exactly the financial data it needs, with the customer's explicit permission, in seconds.

The engineering that matters is at the seam. These rails return structured, authoritative data — which means the correct design is usually to let the deterministic system do the deterministic part and reserve the model for the genuinely ambiguous residue. Teams that route everything through a language model because the project is called an AI project end up with something slower, more expensive and less reliable than what they replaced.

We spent years building blockchain infrastructure before moving to AI, which is relevant here specifically: the instinct to make state exact, actions idempotent and every decision reconstructable is the right instinct when your system is touching a payment rail or an identity credential.

A customer opens in English, explains the actual problem in Hindi, names a place in Marathi and spells a surname that has been transliterated three different ways across your own records. Speech-to-text configured for a single locale does not fail loudly at each switch — it returns confidently wrong text, which then poisons every downstream step.

Retrieval has the same problem more quietly. A knowledge base built from real Indian business documents is already multilingual: English policy text, Hindi correspondence, proper nouns in three spellings. Embedding all of it with an English-first model produces a system that demos beautifully and fails on the queries that actually matter.

Handling this properly is the most reliable reason our voice agent work survives contact with production when somebody else's pilot did not.

We are going to be direct about this, because if you are an overseas buyer you have either been burned by it or been warned about it. The dominant Indian delivery model sells resources: a rate card, a bench, interchangeable engineers, and a project manager translating between you and people you never speak to. It works for well-specified maintenance work. It fails badly for AI, where the important decisions are made in the first three weeks by whoever understood the problem, and where an interchangeable engineer is a contradiction in terms.

What we do instead: the people who scope your engagement build it. No subcontracting, no bench, no substitution mid-project. Fixed-scope phases with defined exit criteria rather than an open-ended retainer, so the commercial structure does not reward stretching the work out. And a scoping conversation designed to talk you out of the project — if an off-the-shelf tool solves it, we will name the tool and take no fee.

That last one costs us projects. It is also why most of our work arrives by referral. The engagement models page sets out exactly how the phases are structured.

For regulated lenders and insurers, retrieval over documents where the requirement is never "make it clever" but "cite the source, refuse when unsure, and let me reconstruct the answer six months later".

For operations-heavy businesses, workflow automation — the same asset repackaged fourteen ways, approval chains living in email, extraction from documents where the accuracy bar is set by what a wrong field costs.

For customer-facing teams, agents and voice systems that hold state, take real actions and can be audited afterwards.

And increasingly, companies who want to be cited by assistants rather than merely ranked by search engines — answer engine optimisation alongside technical SEO. Measurable, or it does not count.

If you are in Mumbai and would rather meet in person, there is more about the office and the local practice on the Mumbai page.

How much does AI development cost in India?

Scoping diagnostics start in the low lakhs; production builds are a multiple of that depending on how many systems have to be integrated. Our engineer-hour rate is materially below a comparable London, New York or Singapore firm. We would rather you spend that difference on evaluation and observability than take it as a smaller invoice - the projects that fail are almost never the ones that were under-priced.

Who owns the code and the model artefacts?

You do, outright, including work from phases you paid for and chose not to continue. We do not retain licences to client work and we do not build on a proprietary internal framework that would trap you with us. You get the repository, the infrastructure definitions, the evaluation harness and the documentation.

Can you keep our data inside India?

Yes, and for several classes of client you have no choice. RBI's storage direction requires payment system data to be stored only in India, and sector regulators add their own constraints. We design for that from the first architecture conversation rather than discovering it at deployment: Indian cloud regions, models served from inside the country where the requirement is strict, and a documented position on exactly what crosses a border when an external model is called.

What does the DPDP Act mean for an AI system we are about to build?

The Rules were notified in November 2025 and the transition runs to full enforcement in 2027, with penalties up to Rs 250 crore for serious breaches. In engineering terms it means three things you cannot retrofit cheaply: consent recorded and revocable per purpose, deletion that actually reaches the vector store and the logs rather than just the primary database, and a record of processing you can produce on request. Building these in costs weeks; adding them to a live system costs months.

Do you subcontract the work?

No. The people who scope your engagement are the people who build it. This is the single most common failure mode of Indian outsourcing - a sales team wins the work, a delivery team who never spoke to you executes it, and the gap between the two is where the project quietly dies. Small senior teams, no bench, no substitutions mid-project.

How do time zones work with a client in the US or Europe?

IST gives a full working-day overlap with Europe, the Gulf, Singapore and Australia, and a partial one with US East - our afternoon is their morning. US West is genuinely awkward and we say so rather than promising a standup at 2am that will quietly stop happening in week three. We run asynchronous by default: written decisions, recorded walkthroughs, and a live call when a decision actually needs one.

Can you build systems that work in Indian languages?

Yes, and it is harder than it is usually scoped to be. Real Indian conversations code-switch mid-sentence, proper nouns get transliterated three different ways by three different people, and a knowledge base assembled from real business documents is multilingual whether or not anyone planned for it. We treat this as a design constraint from week one rather than a localisation task at the end.