[ Which problem first, and whether to build at all ]
A short engagement to work out where AI would actually pay in your business, what your data can support today, and what to buy rather than build. From a company that builds this software — so the recommendations are costed, not theoretical.
A short engagement to work out where AI would actually pay in your business, what your data can support today, and what to buy rather than build. From a company that builds this software — so the recommendations are costed, not theoretical.
Let us look at where your hours actually goEvery candidate scored on hours saved, data readiness and risk. The most impressive idea is rarely the one to do first.
Where an off-the-shelf tool covers eighty per cent for a fraction of a build, we say so. We would rather be the firm you come back to.
Most AI plans fail on data access, not on models. We check what exists, where it lives and what shape it is in before recommending anything.
You leave with build estimates, running-cost projections and a sequence — something a finance director can act on.
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 across your teams to find and rank the tasks where AI would pay, with the numbers behind each ranking rather than an opinion.
What data you hold, where it lives, how clean it is and what it can support today — the audit that decides whether any of the plan is possible this year.
An independent look at whether to build, buy or wait — including running the shortlist of tools against your actual requirements rather than their marketing.
Interviews with the people doing the repetitive work, not only with the people who commission it. The best candidates are almost never the ones on the original list.
What is recorded, where it sits, how clean it is, who owns it. This is where most ambitious plans quietly die, so we do it second rather than last.
Each opportunity rated on payback, data readiness, risk and effort. The scoring is shared and argued over — the ranking has to survive your team disagreeing with it.
For each shortlisted item: is there a tool that already does this, and what would it cost against a build. Often the answer is buy, and that is a good outcome.
Data residency, personal data, sector rules, and what a wrong answer would cost you. Some strong candidates fail here, and better now than after the build.
A dated plan starting with one proof of concept, with estimates and running costs, and what has to be true before the next item starts.
Most AI consulting produces a slide deck with a maturity model in it. That is not useful to a business with fifty staff and a specific problem. What is useful is a ranked list of the tasks in your company where a model would save real hours, an honest assessment of whether your data can support them, and a number next to each one.
We are a build company, which cuts both ways and we would rather say so. It means our estimates are real, because we would be the ones delivering them. It also means we have an interest in recommending a build — so the engagement is priced and scoped as advice, and recommending an off-the-shelf tool or doing nothing is a normal outcome.
The task that costs the most hours is usually invisible from the top. It shows up in twenty minutes of talking to whoever does it every day.
A perfect use case sitting on data nobody can extract is a next-year project. Knowing that early reshapes the whole plan.
For common problems the tools are mature and cheap. A build is for where your process is genuinely yours.
Capabilities that justified a custom build eighteen months ago are now features in tools you already pay for. The scan has to be redone each time, not remembered.
India's DPDP Act and sector rules have moved data handling from a late compliance check to something that shapes the architecture from the start.
The reliable wins are unglamorous: routing, extraction, drafting, triage. The transformative-sounding projects have a much worse hit rate.
Per-request pricing means an AI feature has a monthly bill that scales with usage. Plans that skipped that arithmetic have been unwound.
A focused opportunity assessment and data readiness review is typically ₹1.5–3.5 lakh over two to three weeks. It is priced as advice and delivered whether or not it leads to a build — including when the recommendation is to buy a tool or to do nothing this year.
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