RAG, embeddings, and hybrid retrieval — engineered into your app’s existing stack, not bolted on.
Your knowledge base, support docs, or product catalog are too big for users to navigate manually, but keyword search misses everything that uses different terminology.
Embedding-based semantic retrieval over your existing content, with hybrid keyword fallback for exact matches.
Generic chatbot vendors hand you a black-box assistant that can’t see your specific data or respect your access controls.
We build the retrieval layer inside your stack — your data, your auth, your latency budget. (Seeking Alpha pattern)
You’ve tried RAG once, got disappointing accuracy, and don’t know if the next iteration will be different.
We’ve shipped it across financial content (Seeking Alpha), clinical analytics (MDClone), HR/IT support (Mercan) — patterns transfer.
Not a black-box chatbot — a retrieval layer that respects your data, your auth, and your latency budget.
A high-impact strike team that diagnoses, architects, and ships. We bring the ML engineers, infra, and domain expertise needed to deliver measurable lift within weeks.
A dedicated research partnership where we co-develop proprietary models alongside your team — from initial hypothesis through production deployment.
A controlled experimentation environment for validating strategies before they touch production. Test against realistic system dynamics and quantify impact upfront.
No pitch decks, no generic demos — just a technical conversation about your data and your goals.