One Knowledge Base. Every AI Experience. Governance Baked In.
The Agentic Knowledge Layer: One indexed foundation, every AI experience, governance included automatically.
Key Benefits
One knowledge layer powers every AI experience you deploy
Access controls enforced at retrieval, not after the fact
Every new AI experience costs less than the last
What is the Agentic Knowledge Layer?
The Agentic Knowledge Layer is a four-component system that runs continuously underneath every AI experience your organization deploys—keeping knowledge current, retrieval precise, governance automatic and output quality measurable.
- Ingests: Connects to content sources and updates the knowledge layer in real time (documents, PDFs, video, audio and more) so every AI experience reflects what your organization knows today.
- Tunes: Configures 30+ retrieval strategies and 40+ supported LLMs per use case without reindexing or rearchitecting the underlying stack.
- Governs: Enforces access controls, tenant isolation and source attribution inherited automatically by every AI experience built from it at the knowledge layer.
- Evaluates: Continuously monitors groundedness, context relevance, and answer accuracy through REMi (RAG Evaluation Metrics Intelligence), surfacing quality regressions before users encounter them.
Whay DIY RAG Actually Costs You
Building your own retrieval-augmented generation (RAG) stack gives you control. It also gives you ingestion failures to debug, re-embedding jobs to schedule, retrieval tuning to maintain and an evaluation framework to build from scratch as permanent responsibilities, not finished work. And that's for one use case. Add a second, and you're not scaling infrastructure, you're duplicating it.
DIY RAG | Agentic Knowledge Layer | |
|---|---|---|
| New use case deployment | Rebuild ingestion, indexing and retrieval from scratch | Existing indexed foundation inherited automatically |
| LLM switching | Requires rework across application logic | Stack stays intact with single configuration change |
| Retrieval quality monitoring | Demands manual spot checking that doesn’t scale | REMi scores grounded and relevance continuously |
| Governance per deployment | Rebuilt from scratch every time | Role-based access control (RBAC) and source attribution inherited automatically |
| Cost as use cases multiply | Compounds—each new use case adds maintenance burden | Shared foundation absorbs new deployments so cost decreases |
Built for the Engineers Who Have to Make It Work in Production
Single-strategy retrieval misses context that matters. The Agentic Knowledge Layer indexes across semantic vectors, full text and knowledge graphs simultaneously so retrieval surfaces what is relevant, not just what matches a keyword.
Every AI use case has different retrieval requirements. Hybrid search, semantic chunking, metadata filtering and 27+ additional strategies are tunable per pipeline without modifying the underlying knowledge base.
Model deprecations and pricing changes shouldn't break your AI stack. With 40+ supported LLMs, you can swap models with a single configuration change; retrieval pipeline, index and evaluation setup remain completely intact.
Stale retrieval is a silent production failure. Sync Agents continuously monitor connected content sources and update the knowledge layer automatically with no manual reindexing jobs required or scheduled pipelines to maintain.
Most RAG quality issues are invisible until users notice them. REMi continuously scores groundedness, context relevance and answer accuracy across every deployment, giving engineering teams a measurable signal to diagnose and fix retrieval problems before they reach production.
Rebuilding access controls for every new AI agent compounds security risk with every deployment. RBAC, tenant isolation and source attribution are enforced at the knowledge layer and inherited automatically with no bespoke governance work required per use case.
Stop Maintaining RAG.
Start Deploying AI
Try the Agentic Knowledge Layer free for 14 days or talk to an engineer about your architecture.