The latest articles on DEV Community by Bitronix Technologies (@bitronix). https://dev.to/bitronix https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Forganization%2Fprofile_image%2F13507%2Fb4a2ff7e-4ab9-4b7f-96cc-f39007bd06b4.png https://dev.to/bitronix en Tausif Ahmed Sat, 30 May 2026 06:23:22 +0000 https://dev.to/bitronix/hiring-an-ai-development-company-ask-these-7-questions-first-hce https://dev.to/bitronix/hiring-an-ai-development-company-ask-these-7-questions-first-hce <h1> Hiring an AI Development Company? Ask These 7 Questions First </h1> <p>Most AI projects fail long before deployment.</p> <p>Not because the model is bad.</p> <p>Because teams skip the hard engineering questions.</p> <p>If you're evaluating an AI development company, ask these 7 questions first:</p> <h2> 1. How is data secured? </h2> <p>AI systems process sensitive business information.</p> <p>Ask:</p> <ul> <li>Where is data stored?</li> <li>Is encryption enabled at rest and in transit?</li> <li>Who has access to prompts, logs, and embeddings?</li> <li>Are enterprise security standards followed?</li> </ul> <p>Security should be designed in from day one.</p> <h2> 2. What observability exists? </h2> <p>You can't improve what you can't monitor.</p> <p>A production AI system should include:</p> <ul> <li>Request tracing</li> <li>Prompt/version tracking</li> <li>Latency monitoring</li> <li>Cost visibility</li> <li>Error reporting</li> </ul> <p>If nobody can explain what happened after a bad output — that's a problem.</p> <h2> 3. How do you handle model drift? </h2> <p>AI performance changes over time.</p> <p>Questions to ask:</p> <ul> <li>How are outputs evaluated?</li> <li>Is feedback collected?</li> <li>How are prompts/versioning managed?</li> <li>What happens when accuracy drops?</li> </ul> <p>Production systems need iteration loops.</p> <h2> 4. What happens during failure? </h2> <p>No system is perfect.</p> <p>Ask:</p> <ul> <li>Is there fallback logic?</li> <li>Human review?</li> <li>Retry handling?</li> <li>Graceful degradation?</li> </ul> <p>Failure handling matters more than demos.</p> <h2> 5. How is access controlled? </h2> <p>Enterprise AI systems require permissions.</p> <p>Examples:</p> <ul> <li>Role-based access</li> <li>API authentication</li> <li>Audit logs</li> <li>Team-level controls</li> </ul> <p>Not everyone should access everything.</p> <h2> 6. What compliance assumptions exist? </h2> <p>Especially important for regulated industries.</p> <p>Ask whether the system considers:</p> <ul> <li>GDPR</li> <li>SOC2</li> <li>HIPAA</li> <li>Financial or internal compliance rules</li> </ul> <p>Compliance cannot be an afterthought.</p> <h2> 7. Who owns the infrastructure? </h2> <p>Clarify ownership before signing anything.</p> <p>Ask:</p> <ul> <li>Who owns the source code?</li> <li>Cloud infrastructure?</li> <li>Models and prompts?</li> <li>Data pipelines?</li> </ul> <p>You should avoid vendor lock-in.</p> <p>AI success is rarely about flashy demos.</p> <p>It's about secure infrastructure, reliability, observability, and long-term maintainability.</p> <p>What question would you add to this list?</p> ai softwaredevelopment webdev blockchain