AI Discovery Sprint in India: costs, delivery models and data rules

An AI discovery sprint in India costs ₹2,00,000 or $3,250 at Eazyware: a fixed price for ten working days, credited in full against the build that follows. Indian clients are invoiced in rupees with GST. The larger variable is not the fee but the delivery model you choose and where the DPDP Act allows your data to sit.

This article covers the Indian price points, the four delivery models available to a buyer here, the data rules that shape a sprint under the DPDP Act and sectoral regulators, and a practical way to judge a domestic partner against a global one.

What does an AI discovery sprint cost in India?

Ours is ₹2,00,000, fixed, for ten working days. The fee covers senior engineering and product time, benchmarking two or three models on your own task, a target architecture, a cost model per transaction and a written recommendation with an evaluation plan. It is credited against the engagement that follows, so a go decision costs you nothing extra and a no decision costs you ₹2,00,000 instead of a quarter.

The adjacent Indian price points matter when you are setting a budget. A broader AI product strategy engagement across a portfolio of use cases runs two to four weeks from ₹2,80,000. A three-week proof engagement that settles the hardest technical claim starts at ₹4,00,000. A six-week MVP starts at ₹17,60,000. Indian entities are invoiced in rupees with GST and international clients in dollars, and every starting figure is published in both currencies on the pricing page, and the broader market context is in how much it costs to build an AI product in India.

Be sceptical of an AI discovery sprint cost in India quoted per hour. Discovery has a defined output; if a partner cannot fix a price for ten days of it, they have not run enough of them to know what it takes.

Three things legitimately move an Indian quote: the number of use cases in scope, whether the sprint has to touch a legacy system with no API, and whether regulated data forces the work inside your environment. A single use case over a modern SaaS stack sits at the base price. Three use cases across a core banking system and a warehouse is a different engagement, and it should be quoted as one.

The four delivery models an Indian buyer can choose from

For most buyers the real choice is between the first two. A domestic studio gives you fixed-price certainty, senior people in the room rather than on the org chart, and someone who has integrated with Indian payment, GST and telephony stacks before. A global consultancy gives you procurement familiarity and a name a board recognises, at a fee that usually buys a larger deliverable than a ten-day sprint needs.

Global capability centres deserve a note of their own. If your group already runs a GCC in Bengaluru, Hyderabad or Pune, the marginal cost of an internal sprint looks like zero. It is not: it is the roadmap work those engineers are not doing, plus the loss of an outside view. The usual compromise is a paid sprint run jointly, with your GCC engineers in every session so the knowledge stays in the group.

Data rules: what shapes an Indian sprint

The DPDP Act 2023

The Digital Personal Data Protection Act 2023 governs personal data processing in India and is published by the Ministry of Electronics and Information Technology at meity.gov.in. For a discovery sprint the practical consequences are narrow and concrete: process the minimum personal data needed to answer the question, redact or tokenise before any extract leaves your systems, record the purpose, and know which processor sees what. We cover the operational detail in DPDP Act 2023 and AI.

Sectoral regulators sit on top

Banking, insurance and healthcare add their own layer. An NBFC or a bank has to consider the RBI's outsourcing expectations when a third party touches customer data, which usually means the sprint runs against redacted samples inside your environment rather than an export. Hospitals apply their own consent and retention rules to patient records. Neither blocks a sprint; both change the sequencing, because access approval becomes the critical path.

Where inference runs is a decision, not a default

A sprint should test at least one hosted model and one open-weight model that can run on infrastructure you control, so the residency conversation starts from measured numbers rather than preference. Some categories of data simply should not leave your network, and the trade-off between quality and control deserves an evidence base. Data residency explains the terms, and sovereign AI in India covers the strategic argument for enterprises.

Contracts, ownership and invoicing

Indian engagements should be straightforward on paper: an NDA signed before the first working session, GST-compliant invoicing in rupees for Indian entities, and explicit ownership of the output. You own the code, the prompts, the model choices, the infrastructure definitions and the documentation, whether or not you continue with the same partner. A discovery sprint that leaves the benchmark data on a vendor's laptop has given you a conclusion you cannot re-test.

How to judge an AI discovery sprint company in India

• Ask for a fixed price and a fixed date, and treat an hourly quote for discovery as a warning sign
• Ask who is actually in the room, by name and years, not by team size
• Ask what happens if the answer is no, and whether the fee is still credited elsewhere
• Ask to see an evaluation plan from a previous sprint, redacted, before you sign
• Ask where model inference will run during benchmarking, and on whose accounts
• Ask who owns the output: code, prompts, benchmark data and the golden question set should be yours
• Ask about GST invoicing and INR terms if you are an Indian entity, and USD terms if you are not

Working hours, and why they matter more than location

We are headquartered in Bengaluru and work across IST, UK and US East hours, which for an Indian client means the sprint runs in your working day with no handover lag. For a UK or US client it means four to six hours of genuine overlap and a written handover covering the rest. Our Bangalore location page sets out how the studio operates, and outsourcing AI development to India covers what has changed about the model itself.

Buyers searching for an AI discovery sprint in Bangalore are usually looking for two specific things: engineers who have shipped production AI against Indian data, and a studio close enough for a day of on-site work with the operations team. Both are reasonable requirements, and the second one matters more than it sounds, because watching the work being done in person changes the scope of nearly every sprint we run. Our AI development company in Bangalore page sets out the practice in more detail.

Location matters less than access. A sprint stalls when a data extract is waiting on a ticket, not when a team is three time zones away. The partner you want is the one who raises those tickets with you at NDA signature, not the one nearest your office.

What the ten days actually contain

Days one and two: watch the work as it is done today, and open whatever real data exists. Days three to five: build a small golden question set and benchmark candidate models against it, including at least one open-weight option. Days six to eight: size cost per transaction at three volumes, map the integration surfaces, and write the residency position. Days nine and ten: write the recommendation, the architecture and the evaluation plan, then present the decision to the sponsor.

When an Indian partner is the wrong choice

If your data cannot lawfully leave a specific jurisdiction and your contracts forbid a processor outside it, an offshore sprint is the wrong shape regardless of quality; run it with a local team or inside your own environment. If your existing master services agreement with a global firm already prices discovery at effectively zero as part of a larger programme, use it.

And if the question is genuinely small, such as whether retrieval search over one document set is feasible, a full sprint is more process than the decision deserves. We will say so and point you at a two-week build instead. An honest no is cheaper for both sides than a sprint sold for the sake of it.

What this looks like in practice

An NBFC needed to know whether KYC and loan onboarding documents could be processed privately, at an accuracy that left staff reviewing exceptions rather than every file. Discovery ran against redacted samples, established how many documents were poor-quality scans, and sized the exception queue before any production code existed. The system that followed is described in the KYC document intelligence case study, and the residency question was settled in the sprint rather than argued about during the build.

Related reading

The hidden costs of an AI discovery sprint lists what a quote leaves out, AI discovery sprint for startups versus enterprises covers how scope shifts with company size, and the engagement itself is described on the AI discovery sprint page.

In India the fee is the easy part; pick the partner on how early they open your data and how plainly they will tell you no.