How do you vet a dedicated oroffshore team’s code quality, especially AI-generated code?

Why promises aren’t enough

Every team says their code is high-quality. What separates them is evidence and process, whether they can show you good work and describe a rigorous method for producing it. As a buyer, especially a non-technical one, you assess quality indirectly through these signals rather than by reading code yourself.

The four checks that work

• Review a code sample. Ask for a representative sample (or have a trusted technical person review it). Even non-experts can note whether it’s documented and organized versus chaotic.
• Ask about their process. How do they test? Who reviews code before it merges? What’s their CI and security setup? A confident, specific answer signals maturity; vagueness signals its absence.
• Check relevant references. Talk to past clients with similar projects. Ask specifically about code quality, maintainability, and what happened after handoff.
• Run a small paid trial. A one-to-two-week paid task shows you how they actually work, communicate, and deliver, far more reliably than any pitch. It’s the single most revealing step.

The AI-specific layer

If the team uses AI coding tools (most good ones now do), add these questions:

• Is every AI suggestion human-reviewed? The answer must be yes. AI drafts; a senior engineer decides.
• Do quality gates apply to AI code? Tests, security scans, and review should treat AI-generated code exactly like hand-written code.
• Can they explain what the AI produced? A team that understands its own AI-assisted code is safe; one that can’t explain it is a future maintenance problem.

Reading the trial

During the paid trial, watch for clear communication, code you or your reviewer find clean, sensible questions about your requirements, and honesty when something’s unclear. These behaviors predict the whole engagement. A team that does careful work on a small task will do careful work on a large one.