RAG Systems
We build secure knowledge search layers that query enterprise databases, wikis, and documents to answer natural-language queries accurately.
Key Capabilities
- Semantic search embedding
- Real-time document sync
- Source-attribution citation
- No-hallucination guardrails
How do you guarantee security for our data?
Data is vectorized and stored in a private, encrypted VPC. Your private data is never used to train public LLMs.
System Stack Specs
Architecture ModelModular Integration
Tech Stack MasteredPinecone • pgvector • LlamaIndex • OpenAI
Service Interface Endpoints/api/v2/rag-systems/*
Operational Verification:All systems undergo automated load checks and strict schema isolation tests.