AI Product Engineering
AI inside your actual product
I design and ship production AI features for startups and growing teams — agents, retrieval, voice, and multimodal flows with evals and observability.
$50k+ earned · 33 Contra hires · 5.0 rating · Production AI, Web & Mobile
Perspective
Most AI product failures are not model choice — they are missing product boundaries, tool permissions, evaluation, and release discipline.
I treat AI as a product surface: latency budgets, failure modes, human approval where needed, and measurable quality before users touch it.
Architecture I actually ship
- Agent and tool-calling layers with explicit allowlists and audit trails
- RAG / context pipelines grounded in your data — not prompt-only demos
- Voice and multimodal paths when the product needs them
- Evals, logging, and monitoring so regressions are visible
Related proof
- FoCoCo — Shipped App Store / Play product with voice logging, GPS context, and ongoing product engineering.
- The Vault Los Angeles — Built platform, app, seller portal, and admin — client planned ongoing retainer.
- menuRX — Flutter + native Swift/Kotlin with Gemini and Firebase — deterministic safeguards over raw LLM wraps.
- NutriLens AI — Nine verified completed engagements on a sustained AI/mobile build.
FAQ
Do we need fine-tuning on day one?
Usually no. We start from the product outcome, then choose retrieval, tools, or fine-tuning only when the constraint demands it.
Can you work inside an existing codebase?
Yes. Most AI product work is integration into a live web or mobile stack, not a greenfield chatbot.