Ingestion & structure
Documents, CSV catalogs, and approved URLs are normalized into passages with source IDs, timestamps, and metadata. Source access and usage rights must be established first.
TECHNOLOGY / EVIDENCE BY DESIGN
A clear path from source to action.
Useful enterprise AI needs more than a model. It needs a knowledge foundation, an integration boundary, and a way to inspect the result.
REFERENCE ARCHITECTURE
Four layers. One reviewable workflow.
- 01
Approved sources
Documents · CSV · authorized URLs
- 02
Knowledge layer
Metadata · indexing · access rules
- 03
Retrieval & reasoning
Evidence · uncertainty · evaluation
- 04
Review & action
Human approval · scoped tools · feedback
Retrieval & grounding
Keyword and vector retrieval can work together. Ranking and evidence selection should be evaluated against representative questions, including questions that cannot be answered.
Permissions & governance
Access rules should be enforced before retrieval and tool execution. Authentication, retention, and audit requirements are defined for each deployment.
Evaluation & monitoring
Track answer quality, missing evidence, latency, and workflow failures. Log only the data approved for the deployment; review changes before expanding scope.
DEPLOYMENT CHOICES
Fit the environment you operate in.
Hosted, private-cloud, or customer-managed deployment can be assessed during discovery. Availability depends on the selected models, data stores, identity systems, and operational requirements.
Integration boundaries
APIs and MCP tools provide controlled connections to business systems. Credentials stay server-side; requests are validated, scoped, and approved where required.
No deployment model, certification, data residency, or service-level commitment is implied by this website.
YOUR KNOWLEDGE. YOUR NEXT LEAP.
What could AI do with
your product knowledge?
Tell us about your products, documents, and business workflows. We’ll help you scope a practical pilot.