Buying AI services

How do I choose an AI consultancy?

Short answer

Choose an AI consultancy by demanding three things before you sign: a production system they built that is still running, a named business metric it moved, and the actual engineers who will do your work on the first call. Start with a small paid fixed-scope engagement — an assessment or a two-week proof of concept on your own data — rather than a long discovery phase, so you buy evidence instead of promises. The strongest predictor of failure is a firm that answers technical questions with case studies and staffs the build with people you never met.

Last updated August 14, 2026 · Bitfumes AI consultancy

Ten questions that separate builders from slide decks

Show me an AI system you built that is in production today. Who uses it and how often?
What business metric moved, and how did you measure it?
Which AI project did you talk a client out of, and why?
Will the people on this call write the code? If not, who will, and can I meet them this week?
How do you evaluate output quality — what does your test harness actually measure?
What does this cost to run per month at our volume, and how does that scale?
What happens to accuracy when our data changes? Who notices?
Do we own the code, the prompts, the evaluation set and the weights?
How do you handle our data — where does it go, what is retained, what is used for training?
What does the handover look like if we take it in-house in a year?

Red flags

A fixed 'AI transformation roadmap' produced before they have seen your data.
No mention of evaluation, only of models — a team that cannot measure quality cannot improve it.
Pressure toward a long discovery phase with no working software at the end.
Refusal to name the engineers, or a sales-engineer-then-swap staffing pattern.
Pricing that hides inference and infrastructure cost until after signature.
Claims of proprietary models that turn out to be a system prompt over a public API.

Green flags

They tell you which of your ideas will not pay back, unprompted.
They ask about your data quality before they ask about your budget.
They propose the smallest engagement that produces evidence.
They quote a cost-to-run, not just a cost-to-build.
Senior engineers, small team, no hand-offs between the people who sell and the people who ship.

How to structure the first engagement

Buy a fixed-scope assessment or a two-week proof of concept with a written deliverable, a clear success criterion agreed in advance, and no obligation to continue. You are not buying software yet — you are buying evidence about whether this firm can do your work and whether the use case pays back. If the assessment is good, the build decision makes itself; if it is not, you have lost weeks rather than quarters.

Bitfumes deliberately structures its first engagement this way: a $999 AI Opportunity Assessment, delivered personally with a written report, followed by an embedded build only if the numbers justify it.

Frequently asked

Should I hire an AI consultancy or build an in-house AI team?

Hire a consultancy to find and prove the first use case, then build in-house once you know which capability is worth owning permanently. Hiring senior AI engineers before you know what you are building typically costs 9–12 months and produces a team searching for a problem.

How long should an AI consulting engagement last?

The first engagement should be 1–4 weeks and end in a decision. Build engagements typically run 6–16 weeks to first production release, then continue as a retainer if the system needs ongoing evaluation and iteration.

What should be in an AI consulting contract?

Full IP ownership of code, prompts and evaluation sets; explicit data handling and retention terms including a no-training clause; named engineers; a defined success criterion; and an exit clause with a documented handover.

Do I need an AI consultancy that knows my industry?

Industry knowledge matters less than data and evaluation skill for most projects, with the exception of regulated domains — healthcare, finance and legal — where compliance experience meaningfully changes the design and is worth paying for.