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 and implementation 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.
Deciding what to build and getting it into production are two different jobs, and buying the first does not get you the second. You are left holding a document and a hiring problem.
What AI consulting is: deciding which parts of a business a model should touch, proving that on your own data, and putting the result into production. Most of the market sells the first two.
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.
AI consulting firms that are strategy houses hand the build to a partner. Development agencies have the opposite problem — they build whatever you specify, even when the specification is wrong.
Neither is much use when you are two decisions away from spending real money on the wrong thing.
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.
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Scope
What an AI consulting engagement actually covers.
Built into production, not a pilot
In AI implementation consulting, a demo that works on clean data is not the job.
Production is the malformed PDF, the duplicate record, the upstream API that returns 200 with an empty body, and the case nobody documented — 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.
Yours to run
A written runbook, alert thresholds and a tested rollback path. Your team keeps it alive.
What buyers ask before they book.
What does an AI consulting company actually do?
Two different jobs share one label. Strategy houses assess readiness and produce a roadmap; development agencies build what you specify.
When a firm sells AI consulting services, the question worth asking is which of those two it actually does, and what happens to the other half.
We do both halves ourselves, 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: a fixed fee for that scope, milestone-billed as verified work lands, never by the hour.
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?
Four checks settle it: whether anyone can state the rule the system would follow, whether the process changes every quarter, whether the data lives in three places and disagrees with itself, and whether a report would answer the question just as well.
A model on top of an undefined rule does not fix any of that. It makes the wrong answer arrive faster and with more confidence.
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?
The start date is set in the signed scope, and work begins as soon as the deposit clears.
Who sees my code?
Nobody before an NDA. Access is read-only until scope is signed, and you can revoke it at any time. The AI tools used on your code, and what each one retains, are listed on the governance page.
What are the exit terms?
Retainers end on 30 days written notice from either side, with no minimum term. Milestone billing means you never pay ahead of delivered work.
Bring the workflow. We build the part that pays.
Founder, Provingmark
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Thirty minutes, no charge.
Related: Generative AI consulting · AI consulting for small business · AI governance · Will AI replace consultants? · Hire vs engage a firm: a cost model · AI workflow automation · Staff augmentation · LLM & RAG integration · Custom software development · How we work