We already covered general software agency vetting in our guide to hiring a development agency — NDAs, references, communication overlap. Enterprise AI projects need all of that, plus a more specific checklist most companies don't know to ask for.
What's Different About Vetting an AI Vendor
A general software project fails visibly — a broken feature, a missed deadline. A bad AI implementation often fails silently: it works fine in the demo, degrades on real data, and nobody notices until it's given a customer a wrong answer with total confidence. The vetting checklist needs to account for that failure mode specifically.
The Enterprise AI Vendor Checklist
- Ask exactly what the AI is grounded in. A vague "we use GPT-4o" answer isn't enough — ask whether it's grounded in your proprietary data via retrieval, fine-tuned, or just prompted generically. We cover what a real implementation requires in our LLM application development guide.
- Ask how they evaluate accuracy before and after launch. A vendor with no test suite of realistic queries and no plan for monitoring model drift over time is building something they can't tell you is working.
- Ask where your data actually goes. Which model provider, what's retained, whether any of it is used for further model training — this matters more for enterprise data than for a consumer-facing side project.
- Ask for a reference specifically on an AI project, not just general software development — AI implementation is a different skill from general engineering, and a strong general portfolio doesn't guarantee AI competence.
- Ask what happens when the model is wrong. A vendor with no answer for escalation paths and guardrails hasn't built anything you should trust with customer-facing decisions.
Red Flags Specific to AI Vendors
Vague answers about data handling, no mention of evaluation or testing methodology, a demo that only shows the happy path, and pressure to sign before you've seen how the system handles an edge case — these are the AI-specific version of the general agency red flags, and they matter more here because AI failures are harder to spot early.
If you're evaluating vendors for an enterprise AI project, reach out at info@digit.com.pk — happy to walk through exactly how we ground, evaluate, and monitor an AI implementation before you commit to anything.