Software & AI · Vendor selection · Written and maintained by Haink’s AI adoption team · Updated August 2026 · 8 min read
How to Choose an AI Development Company
The AI services market is full of teams that can build a demo. Far fewer can ship a system that is accurate, maintainable and trustworthy in production. The single best predictor is a production track record with measured outcomes — followed by evaluation discipline, honesty about AI's limits, senior engineers doing the actual work, and deployment flexibility. Use the checklist below to tell them apart.
Key takeaways
- Demand a production track record with measurable results, not just prototypes.
- Look for evaluation discipline — they measure accuracy and catch regressions.
- A good partner tells you when not to use AI.
- Confirm senior engineers do the work, not a junior staffing pyramid.
- Check deployment flexibility (cloud or on-premises) and that they think about total cost of ownership.
- Separate gating criteria from scored ones. Four things are pass or fail — you cannot average away a vendor who has never put this class of system into production.
- Test every number they quote. Ask which case study it is in and who measured it. Most of the statistics in this industry do not survive that question.
Written by a vendor, so read it that way. Haink sells AI development, which makes this page an interested document. The scoring sheet below is the one we would want a buyer to apply to us, including the criteria we would score badly on with some clients — small team, no 24/7 managed service, and we decline work we think should not be built. If a supplier hands you a selection guide with no disclosure of that kind, that is itself a data point.
The scoring sheet
Most vendor checklists average everything into one number, which quietly lets a good score on culture cancel out a failure on production experience. Split it instead.
Four gating criteria — pass or fail
A miss here ends the conversation regardless of how well the rest scores. These are not trade-offs.
| Gate | What passing looks like | Why it cannot be traded |
|---|---|---|
| Has shipped this class of system | At least one comparable system in live production that they still operate or supported through handover | Demo-to-production is where most of the difficulty is. A vendor who has not crossed it does not know what they do not know |
| Can describe how they measure quality | A concrete answer about evaluation sets, what they score and how regressions are caught — not “we test thoroughly” | Without measurement, every later conversation about quality is opinion against opinion |
| Will name who does the work | Named people, their seniority, and how much of their time you get | A senior pitch with junior delivery is the most common failure in this market and the hardest to detect afterwards |
| Can meet your deployment constraint | A real answer on residency, on-premises or air-gapped operation if you need it — before contract, not during | A constraint discovered in month three is a restart, not a change request |
Then score the rest, 0–3 each
| Criterion | 0 | 3 |
|---|---|---|
| Depth of production track record | One pilot | Several comparable systems, still running, with named outcomes |
| Evaluation discipline | “We test it” | Evaluation set built early, scored on every change, metrics reported separately |
| Understanding of your domain | Generic AI talk | Asks about your exceptions, your edge cases and who signs |
| Willingness to say no | Every idea is a great idea | Has told you which part not to build, and why |
| Integration experience | Standalone tools only | Has shipped into systems like yours, with the authentication and audit story |
| Cost transparency | “It depends” | Published prices or a fixed-price specification phase, and a run-cost model |
| Handover and operability | Only they can run it | Documentation, runbooks and a path for your team to take it over |
| References you can actually call | Logos on a slide | A customer who will take a call and answer awkward questions |
How to test the numbers they quote
This industry runs on statistics nobody sources. The test is one question, asked politely, and it separates vendors faster than any capability matrix: “which case study is that number in, and who measured it?”
| What you hear | What to ask | What a good answer sounds like |
|---|---|---|
| “85% of AI projects never reach production” | Where does that figure come from? | Honestly, it is folklore — a 2017 remark about big data projects. Here is our own ratio instead |
| “We cut manual review by 50%” | Which engagement, measured how, over what period? | A named case with the measurement method — or an admission that the number is indicative |
| “99% accuracy” | Per field or per document? On which documents? | Field-level, and here is what that implies per document at your field count |
| “It will pay back in six months” | Show me the arithmetic with your assumptions visible | A model you can change the inputs of, not a conclusion |
A vendor who quotes a number, cannot say where it came from, and does not appear troubled by that. It is not the wrong statistic that matters — everyone has repeated one. It is the absence of the instinct to check, because the same instinct is what catches a model quietly getting worse in production eight months from now.
What to look for
| Criterion | Why it matters |
|---|---|
| Production track record | Shipped systems with measured outcomes prove they can finish, not just prototype |
| Evaluation discipline | Measuring accuracy and catching regressions is what makes AI trustworthy |
| Honesty about AI's limits | Telling you when not to use AI signals judgment over hype |
| Senior engineers | Avoids junior staffing pyramids behind a senior pitch |
| Deployment flexibility | Cloud or on-premises, with a real answer on data privacy |
| Total cost of ownership | They plan for run cost and infrastructure, not just the build |
Questions worth asking
- Can you show production systems with measured results — and references?
- How do you evaluate accuracy and catch regressions before they reach users?
- When would you advise us not to use AI?
- Can this run privately on our own infrastructure if we need it to?
- Who exactly will do the work, and how senior are they?
- What does this cost to run at our volume, not just to build? A good answer contains arithmetic you can change the inputs of.
- What would make you tell us to stop this project halfway through? A vendor with no answer has never stopped one.
- If we take this in-house in two years, what do we get and what breaks? The answer tells you whether you are buying a system or a dependency.
Red flags
- Promising to train a custom foundation model when retrieval (RAG) would clearly do.
- No clear answer on how they measure quality or prevent regressions.
- Never pushing back on a weak or unnecessary use case.
- Ignoring the recurring cost of inference and infrastructure.
- A senior sales pitch, but junior engineers doing the delivery.
- Demos that can't be reproduced on your data.
- Numbers quoted without a source, and no visible discomfort when asked for one.
- A selection guide, capability deck or “neutral” comparison with no disclosure that they are a vendor.
Why the infrastructure question matters
AI systems run on hardware, and sizing it wrong is expensive in both directions — over-provisioned GPUs idle, under-provisioned ones throttle the product. The useful test is not whether a vendor sells hardware; it is whether they can answer “what will this cost to run at our volume?” with arithmetic rather than a shrug, and whether they know that the answer is usually to rent rather than buy.
A vendor pushing you toward owned infrastructure without asking about your sustained throughput is selling hardware, not sizing a workload — the break-even against a hosted API sits near 340 tokens per second maintained around the clock, and most workloads are nowhere near it. Ask them what their number is and how they got it; the working is in on-premises versus cloud LLM deployment.
Fixed scope vs iterative delivery
Be wary of large fixed-scope contracts for AI work, where the right design only becomes clear once you see real data and real usage. Strong partners favor a short discovery phase and iterative delivery — a narrow first use case that reaches working results in weeks — so you steer the roadmap with evidence and can stop or pivot cheaply.
Building AI software on your own infrastructure?
Model, pipeline and GPUs under one contract — tell us the use case and we'll scope it.
What to read next
- How much does custom AI cost? — so you can tell a realistic quote from an optimistic one
- How long does an AI project take? — and which parts of a schedule are the vendor's fault and which are yours
- Build vs buy at the stack level — decide which layers you are hiring for before you shortlist
- AI Solution Blueprint — a paid specification you own, implementable by any competent team including not us
Related Resources
- Software & AI Development Services
- MLOps: Getting Models to Production — what “production track record” actually requires
- Build vs Buy: The Strategic Decision
Frequently Asked Questions
How do I choose an AI development company?
Look for a production track record with measurable outcomes, evaluation discipline, honesty about when not to use AI, senior engineers doing the work, deployment flexibility (cloud or on-premises), and attention to total cost of ownership — not just the build.
What questions should I ask an AI development partner?
Ask for production systems with measured results and references, how they evaluate accuracy and catch regressions, when they would advise against AI, whether it can run privately, exactly who will do the work, and what it costs to run at your volume.
What are red flags in an AI vendor?
Promising to train a custom foundation model when retrieval would suffice, no plan to measure quality, never pushing back on weak use cases, ignoring inference cost, and a senior pitch with junior delivery.
Why does infrastructure matter when choosing an AI partner?
AI runs on hardware; wrong-sized infrastructure is costly. A partner who quotes right-sized GPUs with the software gives one accountable vendor and run cost matched to real throughput, and makes on-premises deployment a real option.
How should we score AI vendors?
Split gating criteria from scored ones rather than averaging everything. Four things are pass or fail: they have shipped this class of system into production, they can describe concretely how they measure quality and catch regressions, they will name who does the work and how senior they are, and they can meet your deployment constraint before contract rather than during. Score the remaining eight criteria zero to three each — track record depth, evaluation discipline, domain understanding, willingness to say no, integration experience, cost transparency, handover, and callable references. Above twenty of twenty-four, proceed; sixteen to nineteen, buy a paid specification phase first; below sixteen, keep looking. Any gating failure ends it at any score.
How do I check the statistics an AI vendor quotes?
Ask which case study the number is in and who measured it. Most of the figures in this market do not survive that question: the widely repeated claim that 85% of AI projects never reach production traces to a 2017 remark about big data projects, not to research on models. When you hear an accuracy figure, ask whether it is per field or per document, because those differ enormously — 99% per field is about 82% per document at twenty fields. What matters is less the wrong statistic, which everyone has repeated, than whether the vendor shows any instinct to check.
Should AI projects be fixed-scope or iterative?
Iterative is usually better for AI, because the right design emerges once you see real data and usage. Favor a short discovery phase and a narrow first use case over a large fixed-scope contract.
Sources and scope. This page is written by a supplier of AI development and hardware, and should be read as an interested document; the scoring sheet is the one we would want applied to us. Thresholds are judgement rather than research — the point of the instrument is that gating criteria are not averaged away, not that twenty is a magic number. The provenance of the 85% figure is discussed in MLOps: getting models to production; the field-versus-document accuracy arithmetic is in how AI document processing works.
Reviewed: August 2026.