RAG & Knowledge Retrieval
Grounds every AI response in an organization's own documents, records and policies.
What breaks without this
- General-purpose models cannot answer questions about internal policies, product documentation or case history.
- Without retrieval grounding, agents are prone to producing plausible but incorrect answers.
- Sensitive documents require access control that a naive search index does not enforce.
Provides the ingestion, indexing and retrieval infrastructure that lets agents answer from an organization's actual knowledge base rather than a model's general training data, with source citations attached to every answer.
What it does
Connectors for document repositories, wikis, ticketing systems, structured databases and file shares.
Configurable chunking and embedding pipelines with support for versioned re-indexing.
Hybrid retrieval — semantic and keyword — with re-ranking for precision on domain-specific terminology.
Source citation on every generated answer, linked back to the originating document and section.
Document-level and field-level access control enforced at retrieval time, not only at the source system.
From request to result
Connect
Source systems are connected via read-only connectors that respect existing permission structures.
Index
Content is chunked, embedded and indexed, with scheduled re-indexing to reflect updates.
Retrieve
A query triggers hybrid retrieval and re-ranking against the index, scoped to the requesting user's access.
Cite
The generating agent receives retrieved passages and returns an answer with citations back to source documents.
Built to be audited, not just used
- Retrieval never bypasses source-system permissions; a user cannot retrieve content they could not already access.
- Citations make every answer traceable to a specific document version.
- Stale or deprecated content can be excluded from retrieval without deleting the source record.
Fits existing infrastructure
Exposed to agents through the Model Context Protocol (MCP) for tool and data access, and Agent-to-Agent (A2A) messaging for multi-agent handoff — so third-party and custom agents can integrate without proprietary connectors.
Outcomes to expect
Qualitative, directional outcomes. We do not publish unverified performance figures — see the case studies section for engagement-specific, authorized results.
- Answers reflect current internal policy and documentation instead of generic model knowledge.
- Subject-matter experts spend less time answering the same repeat questions.
- Every AI-generated answer can be checked against its source, supporting review and correction.
Works alongside
Enterprise AI Gateway
A single, governed entry point for every model, prompt and AI service used across the organization.
Knowledge & DocumentsAI Knowledge Base
Keeps internal knowledge current, deduplicated and answerable in natural language.
Knowledge & DocumentsAI Search
Natural-language search across every connected internal system, with results ranked for relevance and permission.
Knowledge & DocumentsAI Document Intelligence
Extracts structured data and meaning from contracts, forms, invoices and unstructured documents.
Evaluate RAG & Knowledge Retrieval for your environment
Talk to an architect about integration into your existing stack, or request a scoped demo against a representative use case.