How to choose an AI development company

The AI development market has more vendors than ever, and most of them can build a convincing demo. Knowing how to choose an AI development company is the difference between a system that works on real data and months of sunk cost on something that falls apart the first week in production. Here are the six checks we would run if we were on the buying side, the red flags, and the questions to bring to the first call.

1. Ask for production systems, not demos

Anyone can prompt a model and screen-record the result. The question is whether they have shipped AI that runs on real, messy data at real volume, and kept it running. Ask directly: what is in production right now, at what volume, at what accuracy, and what breaks?

When we built the document-processing engine for DataFabricX, it had to handle over 500,000 documents at around 90% data accuracy across inconsistent real-world layouts, not a curated test set. That number means something because of one detail: uncertain extractions are flagged for review instead of passed through. If a vendor cannot explain how their system handles the cases it is not sure about, their accuracy figure is meaningless.

2. Match their experience to your domain

AI for insurance claims is a different problem from AI for customer support or code generation. The data, the regulations and the cost of a wrong output all change what has to be built. Ask whether they have built for your industry or an adjacent one with similar compliance requirements, not just "AI in general".

Before we wrote AI code for Greenwin, an insurer, we had to understand policy lifecycles, claims routing, payout conditions and audit requirements. That context is why the AI layer fits how the business runs: it fills records from incoming documents and sends anything uncertain to a person. Manual data entry dropped by about 75% and processing got roughly 5x faster, without a new category of undetected errors.

3. Make them defend their stack choice

There is no single right approach. RAG for retrieval over documents. Fine-tuning for strict formats with enough labelled examples. Agents for multi-step workflows. Classic machine learning for tabular problems where an LLM is overkill. A vendor who proposes the same architecture for every problem is working from tooling, not judgement.

Ask: what models do you use, and when would you use something else? What use case would make you recommend against an LLM? Good teams answer immediately. Teams that are wrapping one provider's API get uncomfortable.

4. Ask about the exception path

AI is not right 100% of the time. The exception path is where projects succeed or fail in production, and most proposals never mention it. You want:

• A confidence score on every output so uncertain cases are identified before they cause damage.
• A human review queue for low-confidence cases, with a named owner.
• Logging that shows what is being flagged, so the system improves over time.
• Monitoring after launch, because inputs drift and edge cases keep arriving.

If a vendor describes the guardrail as a limitation instead of a feature, keep looking.

5. Find out who is actually on your project

Plenty of companies have a strong portfolio because senior engineers built it, and then staff your project with a junior team following a template. Ask who specifically will work on it and whether the people on the sales call are the people building. For most AI builds, two or three engineers who own the whole system beat a large team passing context around.

6. Get scope, price and post-launch support in writing

Models drift, edge cases surface, inputs change. Ask what handoff looks like, what support is included, and how out-of-scope changes are priced. Then get a fixed scope and timeline in writing before you commit. A company that will not commit to a number before signing is telling you something worth hearing.

Red flags

• No production references, only prototypes and internal tools.
• Accuracy claims without conditions: on what data, in what domain, with what failure rate on edge cases?
• No mention of evaluation, monitoring or exception handling after launch.
• Lock-in to one AI provider with no technical reason for it.
• A fixed quote against your brief as written, with no discovery phase. A good vendor will tell you where your assumptions about the data are wrong before quoting.

The questions to bring to the first call

• Show me a live production system you built, with a client I can speak to.
• Have you built for my industry or one with similar compliance needs?
• Who exactly is on my project, and what have they shipped?
• Walk me through the architecture you would propose. How are uncertain outputs handled?
• What is included after launch, and how are changes priced?
• Will I get a fixed scope and timeline in writing before I commit?

A company worth hiring answers all six without hesitation. Vague or deflected answers are your answer before you sign anything.