[ Find out in three weeks, not three quarters ]
A short, fixed-scope build that answers one question: would AI actually beat how your team does this today? Scored on your data, against your current baseline, with a recommendation that is allowed to be no.
A short, fixed-scope build that answers one question: would AI actually beat how your team does this today? Scored on your data, against your current baseline, with a recommendation that is allowed to be no.
Bring one question and three weeksTwo to three weeks, one question, an agreed definition of success written before we start. Scope creep is what turns a proof of concept into a project with no end.
We measure how your team does the task today — time and accuracy — so the comparison is against reality rather than against nothing.
Including the messy records, the scanned documents and the edge cases. A pilot that only ran on clean data has told you very little.
Finding out in three weeks that this problem does not need a model saves the six months and the budget. We will write that down plainly.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Two to three weeks against one clearly stated question on your own data, ending in a scorecard and a recommendation you can take to a board.
What the task costs you today in hours and errors, and what it would cost run by a model at your real volume — the two numbers the decision actually turns on.
The lasting deliverable: a scored set of real cases with accepted answers. Every later build, model change and prompt tweak is measured against it.
Not "can we use AI" but something answerable: can it classify these enquiries as accurately as our team, at under this cost. Written down and signed off before day one.
Time per item, error rate, cost. Without this number there is nothing to beat and every result can be argued either way.
A held-out set of real examples with the answer your team would accept. This is the actual deliverable — it outlives the prototype and every later build is measured on it.
The smallest system that could answer the question. No integrations, no polish, no admin panel. A week of building, not a month.
Run the test set, record where it fails and why, and project the cost at your real volume. Failures are the information; successes are the easy part.
Build it, change the approach, or stop — with the numbers behind whichever it is, and a scoped estimate if it is build.
Most organisations do not need an AI strategy. They need to know whether one specific, expensive, repetitive task can be done better by a model — and they need to know it cheaply, before anyone signs off a programme. That is what a proof of concept is for, and it works only if it is narrow enough to finish and honest enough to fail.
We keep the scope to one question and three weeks, measure against how the work is done today, and hand over the evaluation set regardless of the outcome. If the answer is no, you have spent a small amount to avoid a large one. If it is yes, you have the scorecard the production build will be judged on.
Answerable, measurable, and agreed before we start. A proof of concept with several goals proves nothing about any of them.
Not to perfection. The bar is your team on an ordinary Tuesday, and that bar is often lower than people assume — or higher.
The prototype gets thrown away. The evaluation set is what makes every future decision about this problem measurable.
What took a quarter now takes days, which means a proof of concept can genuinely be allowed to fail. That changes what you should attempt.
Comparing three models is an afternoon. Getting a clean, representative sample of your own data out of your systems is where the real time goes.
With prices falling but volumes rising, the economics decide the architecture — which model handles which step, and what gets cached.
Teams remember the demo and lose the test set. The test set is the thing that stops the second version being worse than the first.
Typically ₹1.5–4 lakh for two to three weeks, fixed scope and fixed price. The range depends on how hard it is to get a usable sample of your data out of your systems, which is usually the larger part of the work. Techtaru Digital agrees the question and the success threshold in writing before starting.
Let’s talk about your ai proof of concept project. No obligation, just a conversation.
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