AI Chatbots & RAG Systems — Claude API, Vector Search, pgvectorGabe Upton
I build custom AI assistants and chatbots powered by Claude API — trained on your data, grounded in your knowledge base, and deployed to production.
Not a ChatGPT wrapper. I build RAG systems with document ingestion, vector embeddings (pgvector), semantic search, structured data extraction, conversation memory, and multiple chat modes — each with distinct behavior and retrieval strategies.
My proof: I built The Mirror for Catalyst OS — a production AI coaching system with 5 chat modes, guided intake that builds user profiles, and a research pipeline that auto-generates 42+ evidence-based documents per week from Claude and PubMed. All responses grounded in a RAG knowledge base with 1536-dimension vector search.
If you need an AI that actually knows your content and gives reliable answers — not generic completions — that's what I build.
FAQs
laude (Anthropic) by default — it's the best for structured, reliable output. I also work with OpenAI models if your project requires it.
Yes. I build custom chat UIs or embed as a widget. Deployed on Vercel + Supabase with your branding.
Your documents are split into chunks, converted to vector embeddings, and stored in a database. When a user asks a question, the system finds the most relevant chunks by meaning and feeds them to Claude as context — so it answers from your data, not from its general training.
Contact for pricing
Duration1 week
Tags
Claude
LangChain
Next.js
Supabase
Service provided by
Gabe Upton San Antonio, USA
AI Chatbots & RAG Systems — Claude API, Vector Search, pgvectorGabe Upton
Contact for pricing
Duration1 week
Tags
Claude
LangChain
Next.js
Supabase
I build custom AI assistants and chatbots powered by Claude API — trained on your data, grounded in your knowledge base, and deployed to production.
Not a ChatGPT wrapper. I build RAG systems with document ingestion, vector embeddings (pgvector), semantic search, structured data extraction, conversation memory, and multiple chat modes — each with distinct behavior and retrieval strategies.
My proof: I built The Mirror for Catalyst OS — a production AI coaching system with 5 chat modes, guided intake that builds user profiles, and a research pipeline that auto-generates 42+ evidence-based documents per week from Claude and PubMed. All responses grounded in a RAG knowledge base with 1536-dimension vector search.
If you need an AI that actually knows your content and gives reliable answers — not generic completions — that's what I build.
FAQs
laude (Anthropic) by default — it's the best for structured, reliable output. I also work with OpenAI models if your project requires it.
Yes. I build custom chat UIs or embed as a widget. Deployed on Vercel + Supabase with your branding.
Your documents are split into chunks, converted to vector embeddings, and stored in a database. When a user asks a question, the system finds the most relevant chunks by meaning and feeds them to Claude as context — so it answers from your data, not from its general training.
Contact for pricing