Where AI actually pays
We walk your real workflows and rank them by what automation is worth against what it costs to build and maintain. The output names the two or three worth doing and, just as usefully, the ones that aren't.
AI consulting that ends in working software.
Most AI consulting stops at the recommendation. You get a maturity assessment, a roadmap, a slide about use cases, and an invoice — and the thing still isn't built. The gap between *knowing what to build* and *having it running* is where these engagements usually die, and it is the reason so many AI pilots never reach a second quarter.
We do both halves. The first conversation is about what is actually worth automating in your business and what isn't. Then we build it, ship it into production, and hand you something your team can run without us.
That combination is unusual. Most AI consulting firms are strategy houses that hand the build to someone else, and most development shops will build whatever you specify without telling you the specification is wrong. Neither is much use when you are two decisions away from spending real money on the wrong thing.
If the answer is that you don't need us, we will say so on the call. Plenty of what gets pitched as an AI project is a reporting problem, a process problem, or a job for a tool you already pay for.
Talk to Anupam →Fixed fee, scoped in writing before anything starts. No hourly billing, no verbal scope.
Scope
What an AI consulting engagement actually covers.
Built into production, not a pilot
A demo that works on clean data is not the job. Real inputs, real edge cases, real failure paths — with deterministic steps kept deterministic and models used for judgment, never for arithmetic or as the system of record.
Gates on anything irreversible
Money out, data deleted, a customer contacted, a limit changed — those pass a human gate. Every decision is logged with its inputs so it can be explained months later, in an audit or to a regulator.
Written before it starts
What we are building, what we are not, what it costs, and when it lands — agreed in writing before anything begins. AI implementation consulting goes wrong most often at the scope boundary, not in the code.
Yours to run
A written runbook, alert thresholds, and a tested rollback path. No dependency on us to keep it alive, and no seat licence for a wrapper we control.
What buyers ask before they book.
What does an AI consulting company actually do?
It depends entirely on the firm, which is why the category is confusing. Strategy houses assess readiness and produce a roadmap. Development shops build what you specify. AI consulting services that are worth paying for do both — decide what is worth building, then build it — because a recommendation you cannot execute is a report, and a build nobody questioned is an expensive guess.
How much do AI consulting services cost?
Scope sets the price, so we quote against a written scope rather than publishing a rate card. What we can tell you up front: fixed fee for that scope, milestone-billed as verified work lands, no hourly rate and no timesheets. If budget is the real constraint, say so on the call — that is a much faster route to an answer than a proposal.
How is this different from AI strategy consulting?
AI strategy consulting typically ends at the recommendation — a roadmap, a prioritised use-case list, a business case. Useful, and genuinely the right purchase when nobody has decided what to build yet. It becomes a problem when the strategy is delivered to a team with no capacity to execute it. We do the deciding as the first step of the building, not as a separate engagement with its own invoice.
Do you work with generative AI and LLMs specifically?
Yes — generative AI consulting is most of the current demand, and most of it is agents, retrieval and workflow automation rather than model training. The engineering question is almost never which model. It is what happens when the model is wrong: what it is allowed to touch, what gets logged, what needs a human before it executes, and how you roll it back.
What if AI is not the right answer for our problem?
Then we say so, and we would rather say it on a free call than three months into an engagement. A surprising share of what arrives described as an AI project is a reporting problem, a process problem, or a job for a tool you already pay for. Telling you that costs us a sale and saves you a quarter.
Who owns the code and the models?
You do, outright. IP assignment is signed before repository access, never after, and it covers anything produced with AI assistance. You get the runbook and the rollback path at handover, and nothing in the delivery depends on a licence we control.
How quickly can you start?
Once scope is signed and the deposit clears. We do not quote a start date before that, because a date given before scope exists is a date that moves. What we will not do is begin on a verbal agreement.
Bring the workflow. We'll tell you if it's worth automating.
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Fixed fee, scoped in writing before anything starts. No hourly billing, no verbal scope.
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