Discovery + MVP
AI product strategy
I turn a vague "we should add AI" request into a scoped feature - what model, what data, what it should never do.
SEC.05 - AI SYSTEMS
I build AI features that ship, not just prompts.
I design and integrate AI into real products - chat interfaces, retrieval pipelines, and automation - using production-grade APIs. Below is the actual stack running this site's assistant, and how I typically approach an AI feature end to end.
RAG + Context
LLM systems design
Prompt architecture, retrieval over real content, and evaluation - the plumbing behind a chatbot that's actually useful.
Speed + Scale
Low-latency inference
I route requests to fast inference providers like Groq when response time matters more than raw model size.
End-to-End Delivery
Product engineering
I ship the full feature - frontend, backend proxy, rate limiting, and error handling - not just an API call.
Google GeminiReasoning & context-aware replies
GroqLow-latency inference layer
Build Loop
1Problem framing
2Model + provider fit
3Prototype + eval
4Production deployment
AI Impact
Faster answers.
Fewer dead ends.
Real product value.
Whether it's a support chatbot, an internal tool, or a feature like the one below, I focus on picking the right model for the job - not the biggest one - and shipping it with the same rigor as any other production system.
LIVE DEMO - ASK ABOUT MY WORK
T
Thomas's Assistantanswers questions about experience & projects
This demo is wired to run on Google Gemini for reasoning and Groq for fast responses, with resume and project content as context - a lightweight RAG setup. In production this call runs through a backend proxy rather than the browser, to keep API keys private. Right now it is in demo mode with canned context matching.