Retrieval-Augmented Generation pipelines that ground LLMs in your organization's knowledge.

Ground Your AI in Facts

Hallucinations are not a model problem, they are an architecture problem. Retrieval-Augmented Generation (RAG) connects Large Language Models to your live, permissioned, up-to-date knowledge — so answers come with citations, not confabulations.

Our RAG Stack

• Ingestion & Chunking: Layout-aware parsing of PDFs, wikis, tickets, and databases; semantic chunking tuned per document type.
• Hybrid Retrieval: Dense embeddings plus BM25 keyword search, fused with reciprocal rank fusion and cross-encoder reranking.
• GraphRAG: Knowledge-graph-augmented retrieval for multi-hop questions that flat vector search cannot answer.
• Postgres + pgvector: We build on boring, reliable infrastructure — one database for vectors, metadata, and row-level access control.
• Agentic RAG: Query decomposition, self-correction loops, and tool-calling retrieval agents for complex research workflows.

Production Concerns, Handled

Access control inherited from your identity provider, evaluation with RAGAS-style metrics, latency budgets, and full observability of every retrieval decision.

Contact us for an enterprise knowledge assistant pilot.