Scalable Infrastructure

What this is

Most AI demos fail on the same thing. The model sounds great and the answers are wrong, and nobody can tell you why, because nothing was traced and nothing was grounded.

Scalable Infrastructure is the plumbing that fixes that. Retrieval over your actual content, embeddings and vector search you own, model APIs behind a boundary you control, and observability wired in from day one so you can see what the system did and why.

The bar I hold: a retrieval system that is useful knows when to refuse and shows you where the answer came from. Anything less is a chatbot that will eventually embarrass you.

What I’ve shipped

• Cast Sense, running in production. A source-grounded coaching product built on a curated source library, embeddings in pgvector, and live environmental data. It answers from sources, cites them, and declines when the sources are thin instead of inventing something plausible.
• The deployment stack, end to end. Next.js on Vercel, Supabase for Postgres and auth, pgvector for retrieval, model APIs behind server-side boundaries, Langfuse for tracing and evaluation. No hand-waving — I run this stack myself and pay for it myself.
• Fallback and caching behavior for live data. Third-party data sources go down. The product still has to answer, and it has to be clear about what it is working from when it does.

What I’d build for you

• A RAG pipeline over your documents, tickets, transcripts, or catalog — ingestion, chunking, embeddings, and retrieval you can inspect.
• A source library and quality-control process, so the thing your customers talk to is grounded in content you approved.
• Model API integration behind a server-side boundary, with cost controls and rate limiting that survive a traffic spike.
• Langfuse observability and an evaluation set, so quality is a number you can watch instead of a feeling.
• Deployment on Supabase and Vercel, with the fallback, caching, and refusal behavior that keeps it trustworthy when a data source disappears.