How to Choose an LLM Development Company: 10 Questions to Ask
Most LLM development companies demo well and deliver badly. These 10 questions separate teams that ship production AI from teams that ship prototypes.
Every software agency became an “LLM development company” sometime in the last two years. The websites look identical: the same GPT logos, the same “AI-powered transformation” language, the same wall of client badges. Yet the gap between teams that can ship a demo and teams that can ship production LLM systems is enormous — and you usually discover which one you hired three months and many dollars in.
These ten questions expose the difference in the first call. We answer them for ourselves at the end of each section, because a vendor that asks you to evaluate on these criteria should be willing to be evaluated on them.
1. “How will we measure whether the system is good enough?”
The single best filter. Weak vendors talk about features; strong vendors talk about evaluation — accuracy against ground truth, hallucination rates, latency budgets, cost per request. If a vendor can’t describe the evaluation suite they’ll build before the system, they’re planning to declare victory by demo.
Our answer: we define the eval suite with you in week one, and nothing ships without numbers against it.
2. “Can I see a production system, not a demo?”
Demos are easy; the last 20% — guardrails, edge cases, monitoring, cost control — is the actual job. Ask for case studies with production metrics that survived contact with real users: uptime, throughput, error rates over months.
Our answer: a deed-extraction system processing at 13× the manual throughput with zero critical errors, and fraud scoring that has run at 47 ms latency in production — with clients’ names on both.
3. “What happens when the model is wrong?”
Every LLM is sometimes wrong. Mature vendors have an architecture answer: confidence scoring, human-in-the-loop review queues for low-confidence outputs, fallbacks, audit trails. Vendors without an answer are shipping your reputation to an API.
4. “RAG, fine-tuning, or agents — and why?”
A useful test of honesty. The right answer starts with “it depends on your data and workload” and ends with a benchmark plan — not with whatever the vendor most enjoys building. Be suspicious of anyone who prescribes the architecture before seeing your documents.
5. “Who owns the code, the prompts and the data?”
You should. Everything — source, prompts, eval sets, fine-tuned weights where applicable — should land in your repositories, deployable in your cloud account, with your data never leaving your environment or being used to train anything else. Get it in writing.
6. “What does it cost to run, not just to build?”
LLM systems have a second price tag: inference. A good vendor gives you a cost-per-request model up front, designs for it (caching, routing smaller models where quality allows), and monitors it after launch. If operating cost never comes up in the sales conversation, it will come up on your cloud bill.
7. “How do you price — and who carries the risk of being wrong?”
Hourly billing puts estimation risk on you: every underestimate becomes your change order. Fixed-price-per-outcome puts it on the vendor. This is the fastest way to learn how confident a team really is in its own estimates.
Our answer: fixed prices, published on our website — a two-week Sprint from $2,000 to a working agent on your data, production builds quoted fixed before contract. If we underestimate, that’s our problem.
8. “Who exactly will do the work?”
Agencies routinely sell senior faces and staff juniors. Ask who is in the pod, what they’ve shipped, and how many other projects they’re on. Small senior teams outperform large mixed ones on LLM work, where judgment matters more than headcount — this is also where comparing against hiring in-house gets interesting.
9. “What does support look like after launch?”
Models drift, APIs change, prompts rot. A production LLM system without a maintenance plan degrades quietly. Ask what monitoring ships with the system, who watches it, and what a support retainer costs — before launch, not after the first incident.
10. “What have you decided not to build?”
The most senior answer in the industry is “you don’t need an LLM for this.” Vendors who have never talked a client out of an AI project will happily build you an expensive system where a regex would do. Ask for an example.
The short version
Take these ten questions into every call. A strong LLM development company will enjoy answering them; a weak one will steer you back to the demo. And notice the pattern behind them: evaluation, production evidence, ownership, running cost, aligned pricing, senior accountability. Teams that hold up on those six dimensions ship systems that survive contact with reality.
If you want to see how we answer all ten in the context of your project, book a free consultation — you’ll leave with a scoped, fixed price, or an honest “you don’t need us for this.”