The problems we solve

• Generic ChatGPT isn't a product — Real value comes from domain data, workflow integration, and trust.
• RAG done badly hallucinates — Chunking, retrieval, and evals decide whether AI is useful or dangerous.
• Cost and latency at scale — Naive prompts cost 10x what optimized ones do.

What's included

• Retrieval-augmented generation (RAG) — Pinecone, Weaviate, pgvector, with hybrid search and re-ranking.
• AI agents & tool-use — Function-calling, planners, and verifiable outputs.
• Fine-tuning & evals — OpenAI, Anthropic, open-weight Llama, with eval harnesses.
• AI-native UX — Streaming, citations, edit-on-the-fly, and trust signals.

Outcomes you can measure

• Production AI in 8–12 weeks
• Measurable accuracy benchmarks
• Optimized $/request
• Defensible moat via data + UX

Technology stack

TypeScript · Python · OpenAI · Anthropic · Postgres + pgvector · LangChain · Inngest

Typical timeline

8–16 weeks to a production-ready MVP. Fixed-scope discovery in week 1, working software every 2 weeks.