How to vet an AI agency: a practical checklist

To vet an AI agency, evaluate five things before you sign: technical expertise that matches your problem, a verifiable delivery track record, compliant data handling, commercial clarity with a signed Statement of Work, and a legitimate, payment-protected engagement. This guide breaks down each one, with the questions to ask and the red flags to watch for.

1. Technical expertise that matches your problem

AI is broad — an agency strong in computer vision may be weak in LLM automation. Look for case studies in the same problem class as yours, and confirm the specific people who will do the work, not just the agency's best sales engineer.

  • Case studies in your problem class (automation, ML, data, cybersecurity)
  • Named practitioners with relevant backgrounds
  • A clear explanation of their approach in plain language

2. A verifiable delivery track record

Past delivery is the best predictor of future delivery. Ask for two references from projects similar to yours in scope and industry — and actually contact them.

  • At least two contactable client references
  • Evidence of projects shipped, not just started
  • Honest account of a project that went wrong and how they handled it

3. Compliance and responsible data handling

For European enterprises this is non-negotiable. The agency should understand GDPR obligations and the EU AI Act, and be able to explain how your data is stored, who can access it, and whether it is used for model training.

  • Clear GDPR-compliant data handling policy
  • Awareness of EU AI Act risk categories relevant to your use case
  • Defined security practices and access controls

4. Commercial clarity and contracts

Good agencies are transparent about price and scope. Insist on a written Statement of Work with defined milestones and acceptance criteria, so everyone agrees what 'done' means before work begins.

  • Transparent hourly rates or project estimates, no hidden fees
  • A signed Statement of Work with milestones
  • Defined acceptance criteria for each deliverable

5. Legitimacy and payment protection

Confirm the agency is a real, registered business — in the Netherlands this means a KVK registration. Wherever possible, pay through escrow so funds are only released when you approve each milestone, removing most of the financial risk.

  • Registered company (e.g. verified via KVK)
  • Willingness to use milestone-based, escrow-protected payment
  • No pressure for large upfront payments outside escrow

The fastest way to de-risk hiring an AI agency

Most of the checklist above can be short-circuited by hiring through a marketplace that verifies agencies for you and protects your payment. NorthBridge AI runs every agency through manual intake, portfolio review, and KVK verification, includes a signed Statement of Work with every project, holds payment in escrow until you approve each milestone, and provides independent mediators if a dispute arises.

Frequently asked questions

How do you vet an AI agency?

Vet an AI agency across five areas: technical expertise (relevant case studies and named team members), delivery track record (client references you can contact), compliance and data handling (GDPR, EU AI Act, security practices), commercial clarity (transparent pricing and a signed Statement of Work), and legitimacy (a registered company, e.g. verified via KVK). Prefer milestone-based, escrow-protected contracts so payment follows approved results.

What questions should I ask an AI agency before hiring?

Ask: Which team members will do the work and what is their background? Can you share two references from similar projects? How do you handle our data and model training under GDPR and the EU AI Act? What does success look like and how is each milestone defined? What happens if a milestone is rejected? Transparent, specific answers are a good sign; vague or evasive answers are a red flag.

What are red flags when hiring an AI agency?

Red flags include no verifiable case studies or references, unwillingness to name the people doing the work, no clear data-handling or compliance policy, pressure for large upfront payments outside escrow, vague scope with no milestone definitions, and reluctance to sign a Statement of Work.

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