SeekOut MCP

The problem

An AI product can look convincing long before it is dependable. The harder work starts when it meets actual users, permissions, incomplete context, ambiguous requests, different assistant behaviors, and failures that do not repeat cleanly.

My role

I led the engineering work on SeekOut MCP from a customer-backed idea through public launch and stayed hands-on throughout. My scope crossed product shaping, architecture, implementation, evaluation, authentication, reliability, deployment, and the last-mile work required to publish the ChatGPT app.

The product decision

The product gives assistants a grounded way to work with recruiting search, profiles, talent-market questions, and recruiter context. The most important design goal was not making a polished demo. It was making the result understandable and useful when the request was messy.

What I learned

Keep the team close to real user behavior, make quality measurable, and treat the awkward edges as product work rather than cleanup.