Should I pick a company that only builds custom, or one that also runs its own product?

A company running its own product in production has to live with its own agent's failures every day — that pressure tends to produce more reliable engineering than a purely project-based shop that moves on after handoff.

Who should own the code after the project is finished?

You should, unless there's a specific reason otherwise. Get this in writing before the project starts — a developer that resists handing over source code or credentials is a warning sign, not a normal industry practice.

How do I evaluate a case study that claims strong results?

Look for a specific, named use case with a concrete before-and-after — hours saved, response time, booking rate — not a generic testimonial. Ask if you can speak to the client directly; a company confident in its results usually says yes.

What should I ask about post-launch support?

Ask exactly what's included (bug fixes, monitoring, minor tweaks) versus billed as new work, and what the response time commitment is if something breaks. Vague answers here predict a rough experience later.

Should pricing be fixed-bid or hourly?

Fixed-bid for a well-scoped, narrow first project protects you from runaway costs; hourly or retainer makes more sense for open-ended ongoing work. Be wary of hourly pricing on a poorly-defined first project — that's where costs balloon.

How important is industry-specific experience versus general AI expertise?

General AI/engineering competence matters more for the core build quality, but industry experience (understanding your compliance requirements, common workflows) speeds up scoping significantly and reduces costly misunderstandings.

What red flags suggest a company is inexperienced?

Vague answers about failure handling and edge cases, no examples of a genuinely multi-step agent (just chatbot demos), and reluctance to discuss what happens when the AI gets something wrong. Confident hand-waving on these questions is a real warning sign.

How long should a typical first project take, and what if they promise it faster?

A focused first agent typically takes 6-10 weeks including scoping, build, and testing. A company promising a fully custom, production-ready agent in a week or two is usually cutting corners on testing and edge-case handling.

Should I ask for references I can actually contact?

Yes, and follow up with them — ask specifically about post-launch reliability and how the company handled problems, not just whether the initial demo looked good.

What happens if the agent doesn't perform as promised after launch?

This should be addressed in the contract before you start — what recourse you have, whether there's a revision period included, and how disputes about performance are resolved. Get this in writing, not a verbal assurance.

Guide

How to choose an AI agent development company

Published September 1, 2026

Choose a company that can show you an AI agent it built actually running in production — ideally one it uses itself — backed by a case study with specific, measurable results, clear pricing, code ownership that transfers to you, and a defined support plan for after launch. If a company can't produce all five, ask why before signing anything.

1. Do they run their own product in production?

A team that has to keep its own agent working for its own customers every day has a different relationship to reliability than one that only ever builds for others and hands the keys over. We build and operate Botnira, our own AI receptionist product, alongside custom client work — which means the failure modes we design against in a custom build are ones we've already had to fix in something we run ourselves. It's a fair question to ask any developer: what have you built that you also operate?

2. Are the case studies specific, or generic?

"We helped a client improve efficiency" tells you nothing. Look for named use cases with concrete outcomes — response time, booked appointments, hours of manual work removed, error rate before and after. Our own case studies cover a dental practice, an auto shop, a driving school, a wellness clinic, and an immigration office automation platform — each one names the specific problem and what changed, not just a vague claim of success. If a company's case studies read like ad copy with no numbers, treat that as a real signal, not a formatting choice.

3. Is the pricing model honest about what's uncertain?

Be wary of anyone quoting a precise number before understanding your systems and scope — a build's cost is genuinely unknown until integrations and edge cases are scoped. What you want instead is a clear process: a scoping conversation, a range based on comparable projects, and a firm number once scope is locked. A company that's upfront about what drives its pricing is more trustworthy than one that gives you a headline number on the first call.

4. Who owns the code and the data?

Get this settled before work starts, in writing. You should own the code, the agent's configuration, and your own data by default — a company that wants to keep you locked into their infrastructure with no export path is optimizing for their retention, not your outcome. This matters even more for AI agents than typical software, since the agent is often deeply wired into how your business actually runs.

5. What happens after launch?

An AI agent isn't "done" at launch the way a static website is — real conversations surface behavior nobody anticipated, and your processes will change under it. Ask specifically: what's the support plan, what's included versus billed separately, and how fast do they respond when something breaks. A company with no answer to this question is telling you they haven't thought past the handoff.

The honest recommendation

Treat the first call as an evaluation of the company, not just the quote. Ask to see something they built that's actually live, ask a former or current client a direct question, and read the contract's IP clause before you read the price. The cheapest quote from a company that can't answer these questions usually costs more later.