Practical notes from the engineers who build agents, copilots and AI products.
Buy the sprint when you need an outside benchmark, speed and someone accountable for the answer. Run it in-house when you already have an AI engineer with evaluation experience, unrestricted data access and ten uninterrupted days to give them. First-timers should buy; experienced teams should build.
Yes, an AI discovery sprint can be fully DPDP-compliant, because a well-run sprint needs almost no personal data. Work from redacted or synthetic samples, keep processing inside your own cloud account, set a retention date before day one, and log every access.
An AI discovery sprint in India costs ₹2,00,000 or $3,250 at Eazyware: fixed for ten working days and credited against the build that follows. Indian clients are invoiced in rupees with GST. The bigger variable is the delivery model you pick and where your data is allowed to sit.
Basic RAG demos well and fails in production for six specific reasons: poor parsing, one-size chunking, vector-only search, stale indexes, missing permissions and no way to measure relevance. Each has an engineering fix; here they are.
The return on AI proof of concept development is mostly avoided cost. A $6,250 or ₹4 lakh ProofRun that stops a build you would have abandoned saves the $26,500 or more you were about to commit. When it says proceed, the return arrives as scope certainty.
Three weeks, for a proof of concept that ends in a scored decision on one workflow. Eazyware runs ProofRun as fifteen working days from $6,250 or ₹4 lakh. Extra integrations add roughly a week each, and missing evaluation data adds a fortnight before you start.
AI proof of concept development runs in five moves: pick one workflow with a measurable outcome, assemble a labelled evaluation set, build the thinnest system that could pass it, score honestly, then decide. Eazyware runs this as a three-week ProofRun from $6,250 or ₹4 lakh.
The hidden costs of AI proof of concept development arrive after the build: inference spend, data extraction, evaluation set construction, your own team's hours, and the productionisation work a pass result creates. A fixed POC fee buys the experiment, not the ledger around it.
Ask an AI proof of concept development vendor five things: what the pass thresholds are, who builds the evaluation set, whose accounts pay for inference, who owns the code and prompts, and what happens on a fail. The answers separate people who have shipped from people who demo.