AI Product Engineering Advisor – Tarek Zaghloul

What the prototype-to-production gap actually looks like in AI product engineering

An AI prototype that works in a demo environment typically fails in production for predictable reasons: the infrastructure assumptions made during research do not hold at load, the model accuracy that looked strong on a clean evaluation dataset degrades on real user input, the latency that was acceptable in testing becomes a UX problem at scale, and the team – often research-heavy – does not have the production engineering depth to debug and fix these issues quickly. Companies that do not identify these gaps before production launch spend three to six months firefighting rather than improving the product. Tarek has seen this pattern consistently and advises on how to close the gaps before launch, not after.

What an AI product engineering advisory engagement with Tarek produces

Tarek typically runs a two-week architecture review as the entry point – evaluating the model pipeline, inference infrastructure, data handling, and team composition against the production requirements the product needs to meet. The output is a production-readiness assessment: a prioritized list of the gaps between current state and production readiness, with specific remediation recommendations for each. From there, he advises on a monthly basis through the production launch, providing input on architecture decisions, reviewing pull requests for production-critical components, and helping the team develop the engineering capabilities they need to own the system post-launch.

When AI product engineering advisory is the right resource – versus a full-time engineering hire

Advisory is right when the core engineering team has the capacity to implement improvements but lacks the specific AI production experience to know what to prioritize. A team of strong engineers who have not shipped AI to production will make different mistakes than a team with production AI experience – and those mistakes tend to be architectural, which are expensive to fix after the fact. Tarek provides the production AI experience the team does not yet have, in a format that transfers knowledge rather than creating dependency. A full-time AI engineering hire is right when the advisory gap has been closed and the team needs operational depth, not advisory input.