AI proof of concept development in India: costs, delivery models and data rules

In India an engineering-led AI proof of concept is bought as a fixed three-week programme rather than by the hour. Eazyware's ProofRun runs from ₹4,00,000 or $6,250 up to ₹6,80,000 or $10,500, invoiced in INR with GST for Indian clients. A ten-day discovery sprint at ₹2,00,000 or $3,250, credited to the next build, is the cheaper entry point.

What follows is the practical buyer's view: how the delivery models differ, what the DPDP Act and sector regulators actually require of a short experiment, where residency matters and where it does not, and how to compare an Indian partner with a global one without pretending the only difference is the rate card.

How Indian proof of concept engagements are priced

Two pricing shapes dominate. The first is fixed-price, fixed-date: an agreed scope, an agreed duration and an agreed set of deliverables, with anything outside it pushed to a later phase. The second is time and materials, billed per engineer per month, which is how most staff-augmentation firms and captive extensions work. For a three-week experiment, fixed price is almost always the right instrument, because the value is the measurement and the schedule is the constraint.

Eazyware's AI POC Sprint is a fixed three-week engagement that builds the riskiest slice of your system end to end, runs it on a meaningful sample of your own data, and measures accuracy, latency and cost against thresholds agreed before tuning begins. You receive a working repository, an evaluation report, a production readiness assessment and an updated build proposal. Starting figures for every programme are published on the pricing page; the case for buying this way is made in why fixed-price programs de-risk your first AI project.

Inference spend sits outside the fee and runs on your own provider accounts, which keeps keys and billing in your control. For a document-heavy proof of concept, that is usually tens to low hundreds of dollars over three weeks of iteration, and we configure budgets, routing and a spend dashboard so it does not surprise anyone. The broader Indian picture is in how much it costs to build an AI product in India.

Which delivery model fits an Indian proof of concept?

Pick the model by who carries the risk of the result, not by the headline rate. The table below compares the four arrangements Indian buyers actually choose between.

The platform vendor pilot deserves a caution. A free pilot is a sales motion, and it measures the platform on your data rather than measuring your problem. That is a legitimate thing to want, but it is not a proof of concept, and treating it as one is how a team ends up committed to a licence before anyone measured the hard case.

Data rules: what actually applies to a three-week experiment

India's Digital Personal Data Protection Act 2023 applies to personal data processed in digital form, and it does not exempt experiments. If your sample contains customer names, phone numbers, identity documents or health records, the Act applies to that sample exactly as it applies to production. The Ministry of Electronics and Information Technology maintains the data protection framework under which the Act and its rules sit, and it is the primary reference worth reading before you export anything.

You remain the data fiduciary whatever the engineering arrangement. A vendor is a data processor acting on your instructions, which means a signed data processing agreement, a named purpose, an agreed retention period and deletion at the end of the engagement are your obligations to arrange, not favours to request. The practical consequences are set out in DPDP Act 2023 and AI.

Four choices remove most of the friction from a short engagement.

• Redact before export. Replace names, phone numbers and account identifiers with stable pseudonyms. Extraction accuracy on a redacted document is almost always representative, and the approval conversation becomes trivial.
• Sample, do not dump. Two hundred well-chosen records including the awkward tail beat fifty thousand records of the happy path, and a smaller export is a smaller exposure.
• Decide residency deliberately. If your sector or board requires processing inside India, say so in week zero, because it changes which models are available and how the pipeline is deployed. Data residency is a design constraint, not a contract clause.
• Check sector rules on top of DPDP. RBI outsourcing expectations for regulated lenders, IRDAI guidance for insurers and hospital data practices each add requirements the Act alone does not cover.
• Agree deletion in writing. Named date, named systems, confirmation in the closing report. This is the single item most often forgotten when a proof of concept ends without a build.

Where residency genuinely matters, and where it does not

Residency matters when the data cannot leave India by regulation or by board policy, when your customers are regulated entities whose own auditors will ask, or when the content is so sensitive that a breach would be existential. In those cases a self-hosted or in-region deployment is the right constraint to design around from week one, and the trade is cost and model choice in exchange for control.

Residency matters far less when the sample is redacted, synthetic-adjacent or already public, or when the workload is short-lived and deleted at the end. Applying the strictest possible constraint to a three-week experiment often doubles its cost and lengthens its calendar for a risk that redaction already removed. Decide honestly which case you are in. Options for the stricter case are covered in sovereign AI in India.

Judging an Indian partner against a global one

Compare four things and ignore the rest: who the named engineers are, whether the firm will show you a redacted evaluation report from a previous engagement, what transfers to you at the end, and how the working day overlaps with yours. Eazyware is headquartered in Bengaluru and works across IST, UK and US East hours, with studio presence in New York and London, which in practice means a European client gets most of a working day of overlap and a US East client gets the morning. The local context is on the Bangalore page and the market shift is described in outsourcing AI development to India.

Commercially, Indian clients are invoiced in INR with GST and international clients in USD, and the rate difference against a US or UK firm is real but secondary. A cheaper proof of concept that measures the wrong slice is more expensive than a well-scoped one at any rate. Rate context across markets is in software development pricing in India versus the US.

When an Indian proof of concept is the wrong choice

It is the wrong choice when your data genuinely cannot cross a border and no in-region deployment is acceptable to your regulator. It is the wrong choice when the hard problem is organisational rather than technical, because no amount of measured accuracy fixes a process nobody has agreed to change. And it is the wrong choice when your team cannot free a domain expert for a few hours a week, because somebody has to adjudicate what a correct answer looks like and no vendor can do that for you.

There is also a timing case. If you cannot act on the result within a quarter because budget cycles or approvals sit elsewhere, the measurement will be stale by the time anyone reads it. Run discovery now and the proof of concept when the decision is live.

A worked example from Indian financial services

An NBFC needed to know whether document extraction would hold on the formats its KYC and loan onboarding teams actually received: scans of varying quality, regional-language identity documents and inconsistent bank statement layouts. The hard case was the messy tail, not the clean sample, so that is what was built and measured first, with processing kept inside the boundary the compliance team required. The engagement is described in the KYC document intelligence case study, and the wider sector view sits on the FinTech and BFSI page.

Related reading

AI proof of concept vs demo explains what you are actually buying, and from POC to production: the checklist covers the work that a passing result creates. If you want the scope decided before you commit to a measurement, a discovery sprint is ten days and is credited to the build that follows.

Buy the three weeks, the thresholds and the deletion date in writing, and geography stops being the interesting part of the decision.