Production Path
Private RAG and LLM systems your team can trust in production.
We help teams move private-data AI systems beyond demos by designing retrieval, model access, evaluation, observability, access control, and operational handoff together.
Production RAG is more than a vector database.
Private-data architecture
We define where documents, prompts, embeddings, responses, metadata, and logs can live, then align deployment to those boundaries.
Retrieval design
We design ingestion, chunking, metadata filtering, reranking, query rewriting, and source attribution around real access patterns and failure modes.
Access control and auditability
We keep retrieval permissions aligned with user permissions and make answers, sources, and data exposure inspectable after launch.
Evaluation and quality control
We add practical evaluation loops for retrieval quality, answer quality, hallucination risk, regression checks, and production feedback.
Model and API strategy
We compare external APIs, private managed endpoints, model gateways, and self-hosted models against cost, latency, risk, and operational burden.
Operational handoff
We leave deployment notes, monitoring expectations, runbooks, and ownership boundaries so the system does not depend on tribal knowledge.
Need private RAG that can survive production?
Book a 30-minute AI Infrastructure Review. We'll map your data boundaries, platform risks, agent controls, and operating model before you commit to a build path.