Start with the type of problem
Match the partner to the work. A consumer mobile product, enterprise modernization program and AI automation system require different strengths even if all are called “app development.”
Ask how they reduce uncertainty
Strong teams do not simply accept a feature list. They identify risky workflows, integrations, data and product assumptions and propose ways to test them early.
Evaluate the portfolio as evidence
Ask what the team actually did, what constraints existed, how decisions changed and what was learned. Beautiful screenshots without project context prove design capability, not full product engineering.
Make ownership and access explicit
Confirm code repository access, design/source assets, infrastructure credentials, documentation, third-party accounts and intellectual-property terms before work begins.
Look for tradeoff literacy
Good engineers can explain when not to use microservices, native apps, AI agents or custom software. A partner that recommends every technology it sells is not doing product-specific reasoning.
Inspect the production model
Ask about testing, accessibility, security practices appropriate to the product, release, observability, incident handling, backups/recovery and handover.
Warning signs
Be cautious with guaranteed rankings or outcomes, estimates that hide assumptions, vague teams, inaccessible repositories, fake “proprietary” lock-in, portfolios with unverifiable claims, and proposals that never discuss failure states.