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Solution · Knowledge graph

Ontology-led knowledge graphs
for decision AI and semantic search

CognitiveGraph™ models your business as entities, relationships, and rules—then powers GraphRAG, semantic search, and governed agents on a shared semantic layer your teams can trust.

Meaning before models. Connect data to decisions, brick by brick.

Ontology modelingEntity graphsGraph RAGSemantic search
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Semantic architecture

Ontology layer

entities · relationships · business rules

Knowledge graph

property graph · lineage · versioning

Vector & semantic index

embeddings · hybrid retrieval · rerank

Agent grounding

GraphRAG · tool context · policy gates

Governance & catalog

stewardship · ACLs · audit trails

3x

Higher answer accuracy vs vector-only RAG on structured enterprise questions

1

Ontology and graph layer shared by search, analytics, and agents

100%

Traceable grounding paths from AI output to source entities and documents

Why vector-only RAG falls short

CognitiveGraph™ gives agents and search systems structured context—so answers reflect how your business actually works, not just similar text chunks.

  • Data lakes and warehouses store tables and files—but not the business meaning that connects customers, products, policies, and operations

  • Vector search retrieves similar documents without understanding entities, relationships, or permissible actions

  • Agents hallucinate or over-fetch because they lack a governed graph to traverse for grounded context

  • Each team builds its own entity definitions—fragmenting semantics across BI, CRM, ERP, and AI projects

  • Decision-makers cannot explain why an AI recommendation was made without lineage from ontology to source data

Solution capabilities

CognitiveGraph™ capabilities

Ontology-led knowledge graphs for GraphRAG, semantic search, and governed agents—one semantic layer for decision AI.

Ontology Modeling

Define the objects and rules your business runs on

Capture entities, attributes, relationships, and action surfaces in a versioned ontology—aligned to your domains, policies, and operational vocabulary.

  • Domain-driven entity and relationship design workshops
  • Business rules, constraints, and permitted actions as first-class objects
  • Versioning and change management for ontology evolution
  • Mapping from source systems, semantic models, and lakehouse tables
  • Stewardship workflows with data owners and subject-matter experts

One shared language for analytics, search, and agents—across teams and tools.

Entity & Relationship Graphs

Materialize ontology as a queryable knowledge graph

Build property graphs with lineage, identity resolution, and incremental sync from operational and analytical sources—ready for traversal, analytics, and agent tools.

  • Graph construction pipelines from OneLake, warehouses, and APIs
  • Entity resolution and golden-record strategies
  • Temporal and provenance metadata on nodes and edges
  • Graph analytics for path, community, and impact queries
  • APIs and graph query layers for applications and agents

Navigate complex relationships—from customer to contract to risk—in milliseconds.

Graph RAG & Agent Grounding

Hybrid retrieval that reasons over structure and text

Combine graph traversal with vector search so agents retrieve the right entities, neighbors, and documents—then cite sources and respect policy boundaries.

  • GraphRAG orchestration: traverse → retrieve → synthesize
  • Hybrid ranking across graph context and embedding similarity
  • Tool definitions scoped to ontology-permitted actions
  • Trace bundles linking prompts to graph paths and documents
  • Eval harnesses for grounding quality and hallucination rate

More accurate, explainable agent responses grounded in your domain model.

Semantic Search & Decision AI

Enterprise search and decision intelligence on one graph

Power semantic search, copilots, and decision workflows with unified indexing—filters, facets, and graph-aware ranking tuned to enterprise security and compliance.

  • Semantic and keyword hybrid search with graph-aware facets
  • Role-based visibility aligned to graph ACLs and data zones
  • Copilot and assistant experiences over governed graph context
  • Simulation and what-if scenarios on ontology-linked data
  • Dashboards connecting graph insights to executive KPIs

Turn fragmented data into decision-ready intelligence—not another chatbot silo.

Engagement roadmap

A phased path from ontology design to production GraphRAG—starting with one high-value domain and expanding with shared governance.

01

Domain & ontology

Weeks 1–4

  • · Use-case selection
  • · Entity modeling
  • · Stewardship model
02

Graph foundation

Weeks 3–8

  • · Ingest pipelines
  • · Identity resolution
  • · Graph QA
03

GraphRAG pilots

Weeks 6–12

  • · Hybrid retrieval
  • · Agent tools
  • · Eval & red-team
04

Scale & govern

Parallel rollout

  • · New domains
  • · Search & copilots
  • · Lineage & audit

Ready to build with CognitiveBricks?

Book a strategy session with our architects to map your agentic AI roadmap, platform foundation, and first production use case.

Book a strategy session