About Inherent
The contextual backbone between enterprise data and AI applications
Our Mission
Modern AI applications fail not because models are weak, but because context is fragmented, inconsistent, and unreliable.
Inherent solves this by acting as a single, versioned, organization-wide brain that:
- Ingests knowledge from multiple data sources
- Preserves truth, versions, and lineage
- Assembles deterministic context for AI apps
Every agent, copilot, workflow, or AI product in your stack pulls context from the same trusted system.
Why Inherent Exists
Most teams build AI systems like this:
- Each app has its own RAG pipeline
- Each agent has its own vector store
- Documents are duplicated, re-embedded, and silently overwritten
This works for demos. It collapses in production.
Inherent treats context as infrastructure, not an implementation detail.
It provides:
- A shared contextual backbone for all AI applications
- Deterministic document versions
- Rebuildable and auditable knowledge indexes
- Clean separation between truth and memory
Core Concept: Truth vs Memory
Inherent is built on a strict architectural separation:
Truth Layer
Must be correct, versioned, and auditable. Stored in Postgres with documents, versions, chunks, metadata, and lineage.
Memory Layer
Must be fast, searchable, and disposable. Stored in Weaviate with embeddings and hybrid semantic search.
Technology Stack
PostgreSQL
Contextual Truth Layer - Documents, versions, chunks, metadata
Weaviate
Contextual Memory Layer - Embeddings, hybrid semantic search
FastAPI
Context Access Layer - Ingestion, retrieval, context assembly
Ready to get started?
Build production-grade AI applications with reliable context.