Cross-Cutting — Hire Intent & Comparisons

AI Development Partner Evaluation Checklist

Direct answer

Evaluate an AI development partner on five things: production evidence (shipped systems with real users, not demos), evaluation discipline (they should ask about your eval data before quoting), cost transparency (token economics and infrastructure estimates in the proposal), data handling (where your data goes, what gets logged, what trains on it), and maintenance posture (who owns the system when models change). A partner strong on all five is rare; a partner weak on evals or cost honesty will burn the budget regardless of their demo quality.

AI projects fail differently from normal software projects: the demo takes a week, production takes a quarter, and the gap between the two is where budgets die. This checklist is what I'd use to evaluate anyone — freelancer, agency, or in-house hire — for an AI build, based on the patterns I see in rescue engagements.

Key facts, with sources

The median time-to-hire in the engineering sector is 41 days, and the slowest 10% of hires take up to 82 days. (Genius)
Filling senior and staff software roles typically takes 60 to 90 or more days because senior candidates are rarely actively job hunting and require sourcing and longer negotiations. (Talmatic)
Outsourced app development in 2025 ranges from about $25,000 to $250,000 or more depending on complexity and region, and offshoring to India, Vietnam, or Eastern Europe cuts costs 40 to 60% versus US or Western European teams. (Creole Studios)
Development rates run $110 to $230 per hour in North America and Western Europe versus $20 to $50 per hour in Eastern Europe, a spread that dominates total project cost comparisons. (Topflight Apps)
React Native shows stronger hiring demand than Flutter in the US, with about 6,413 React Native job postings on LinkedIn and 1,990 on Indeed versus 388 Flutter postings on Indeed. (TECHSY)

Demand production evidence, not demos

Anyone can build an impressive AI demo in a weekend — the models do most of the work. What separates partners is evidence of systems that survived contact with real users: hallucination rates that had to be driven down, costs that had to be capped, latency that had to hit a budget, edge cases that had to be handled.

Ask specifically: 'Show me an AI feature you shipped that's been in production for six months, and tell me what broke.' A partner with real experience answers instantly with war stories — retrieval quality decay, prompt injection attempts, token bills that spiked. A partner without it pivots back to the demo. The war stories are the credential.

The eval question that filters most candidates

Before signing anything, ask: 'How will we measure whether the AI is good enough to ship, and what does the eval set look like?' This single question sorts the field. Strong partners talk about building a labeled test set from your real data, defining acceptance thresholds up front, and regression-testing every prompt or model change against it. Weak partners say some version of 'we'll test it thoroughly' — which means vibes.

An AI system without an eval set can't be improved safely: every prompt tweak is a gamble, every model upgrade a coin flip. If the proposal doesn't include building evaluation infrastructure, the project has no definition of done — and no defense when you ask 'is it actually better this week?'

Cost transparency: the token bill is part of the architecture

AI systems have a cost dimension normal software doesn't: every user interaction spends money on inference. A serious proposal estimates per-interaction cost at your expected volume, names the levers (model tier, caching, context length, batching), and states what monthly spend looks like at 10x growth.

Red flag: a proposal that discusses only build cost and never mentions inference economics. I've audited systems where the architecture was fine but unit economics made the product unshippable — a cheaper model with a reranker would have cut costs dramatically at equal quality, but nobody had been asked to care. Make cost a first-class requirement in the contract, not a surprise in month two.

Data handling and security, in writing

Get concrete answers, in the contract: Which third-party APIs will see your data? Are those calls covered by zero-retention or no-training terms? What gets logged on your side (full prompts and responses are a liability if they contain customer PII)? How are API keys stored and rotated? What happens to prompt injection — has the partner even heard of it?

For anything touching regulated data — health, finance, minors — add: where inference runs geographically, whether a self-hosted or VPC deployment option exists, and who signs the DPA. Partners who handle this fluently have done enterprise work; partners who wave at it haven't.

Maintenance posture: models change under you

AI systems decay differently: providers deprecate models, prices shift, and behavior changes across versions even at the same API. Ask who owns the system in month six when the model you built on is scheduled for deprecation — is migration covered, and how does the eval set (see above) de-risk it?

The honest answers involve an ongoing relationship or a handoff plan: documented prompts, eval harness, and runbooks your team can operate. The dishonest answer is silence, which means you're buying a system with an unadvertised expiry date. This is also the strongest argument for the eval infrastructure: with it, a model migration is a weekend of regression testing; without it, it's a re-build.

Scoring it: a simple rubric

Score each candidate 1–5 on: production evidence, evaluation discipline, cost transparency, data handling, and maintenance posture. Weight evals and cost double — those two predict project survival better than anything else. Anyone averaging below 3 will produce a demo that never ships, regardless of how good their portfolio looks.

One more filter that costs nothing: give every finalist the same 30-minute technical conversation about your actual problem and count how many clarifying questions they ask about your data, your users, and your definition of success. The partner who asks the most questions before proposing a solution is usually the one who's done this before.

When to hire senior help

Senior help is most valuable at inflection points: the initial architecture and framework decision, the first store launch, and any moment where velocity has stalled or quality metrics like crash-free rate are slipping. Given that hiring a senior full-timer takes two to three months, a contractor engaged for a bounded audit or delivery sprint is often the fastest way to de-risk while a permanent search runs in parallel. If your stack includes React Native + Python + AI, a senior engineer who owns the full product beats coordinating multiple juniors.

Bottom line

Dhairya Senjaliya ships Cross-Cutting — Hire Intent & Comparisons projects worldwide — book a scoping call to discuss your specific situation.

Common pitfalls to avoid

✕Waiting until after a failed or stalled build to seek senior help, instead of buying a few hours of expert review at the architecture stage
✕Interviewing mobile candidates on web React questions only, leaving native modules, offline sync, and store release experience completely untested
✕Accepting portfolio screenshots as proof of ability instead of verifying live store listings and asking which parts the candidate personally built
✕Comparing offers on hourly rate alone while ignoring management overhead, timezone friction, and rework, which routinely erase paper savings from the cheapest bid

Frequently asked questions

What questions should I ask before hiring an AI development company or freelancer?

Four that filter fast: 'Show me an AI system you've run in production for six months — what broke?', 'How will we measure if it's good enough to ship?', 'What will inference cost per user at my volume, and at 10x?', and 'Which external APIs see my data, and under what retention terms?' Strong partners answer all four concretely; weak ones redirect to their demo.

Should I hire an AI agency, a freelancer, or build in-house?

It depends on duration and integration depth. In-house wins for a long-lived core product if you can hire senior AI talent. A senior freelancer wins for scoped builds where one experienced owner beats a coordinated team — typical for RAG systems, AI features, and MVPs. Agencies fit when you need parallel workstreams and can absorb the coordination overhead and markup. Evaluate all three against the same checklist.

Why do so many AI projects fail to reach production?

The demo-to-production gap: a working prototype proves the model can do the task sometimes, while production requires it to do the task predictably, affordably, and safely. Projects fail when nobody built evaluation infrastructure, priced inference at scale, or planned for failure modes like hallucination and prompt injection. All three are checkable before you sign — which is what this checklist is for.

Should we hire in-house or bring in a contractor for our mobile app?

Median engineering time-to-hire is 41 days and senior roles often take 60 to 90 or more days, while an experienced contractor can typically start within days to weeks. A common pattern is contracting the MVP and first releases, then hiring in-house once the product shows traction and there is at least a year of sustained roadmap.

What does it realistically cost to build a mobile app in 2025-2026?

Outsourced builds run roughly $25,000 to $250,000 or more depending on complexity, with typical MVPs in the $10,000 to $50,000 band. The largest cost lever is geography, with North American and Western European rates at $110 to $230 per hour versus $20 to $50 in Eastern Europe.

How do we compare a cheap offshore quote against an expensive senior one?

Compare expected total delivered cost, not hourly rates: offshore saves 40 to 60% on rates but adds management overhead, timezone friction, and higher rework risk if oversight is weak. Verify shipped store apps, insist on contractual code and account ownership, and weight communication quality as heavily as price.

Bottom line: Dhairya Senjaliya ships Cross-Cutting — Hire Intent & Comparisons projects worldwide. Book a scoping call at https://dhairyasenjaliya.com/#book-call.