Powerful Features for Enterprise AI
Everything you need to build production-grade AI applications with reliable context
Multi-Source Knowledge Ingestion
Ingest and normalize content from Git repositories, Wikis (Confluence, Notion), file systems, APIs, and custom connectors. All sources converge into a single contextual model.
Version-Controlled Knowledge
Every document and chunk is versioned by default. No silent overwrites, no mixed contexts, full historical traceability. You can always rebuild the system from source.
Deterministic Context Retrieval
Two-step retrieval process: Weaviate finds relevant candidates, Postgres hydrates authoritative truth. Guarantees no stale chunks, no hallucinated context, no cross-version leakage.
Context Packs for AI Applications
Returns structured context packs that are source-aware, version-aware, metadata-rich, and deterministic. Exactly what serious AI systems require.
Truth Layer (Postgres)
Versioned documents, chunks, metadata, lineage—auditable and rebuildable from source. The single source of truth for all contextual data.
Memory Layer (Vector)
Fast semantic + hybrid retrieval—searchable, replaceable, and always hydrated from truth. Optimized for speed and scalability.
Shared Contextual Backbone
A single, versioned, organization-wide brain that all AI applications pull from. No duplication, no inconsistency, no fragmentation.
Production-Grade Infrastructure
Built for enterprise scale with proper separation of concerns, auditability, and rebuildability. Not a demo—production-ready RAG infrastructure.
How It Works
1. Ingestion
Connectors send content to the ingestion API. Content is normalized and chunked.
2. Storage
Versions stored in Postgres (truth), embeddings indexed in Weaviate (memory).
3. Retrieval
Applications call the search API. Context is assembled and returned as structured packs.
Ready to get started?
Start building production-grade AI applications today.