Solution

Your organization knows more than it can find.
Data Graphs changes that.

Critical knowledge is scattered across documents, databases, wikis, content systems, and people's heads. Data Graphs connects it all into a centralized, governed knowledge hub where both employees and AI agents can find precise answers instantly, not approximate search results.

Connected knowledge. Instant answers. Governed access.

The challenge

Siloed knowledge costs more than you think

Enterprise knowledge lives in dozens of systems: document management, wikis, CRM, ERP, project tools, email archives, content platforms. Each system knows its own slice, but none understands the connections between them. Employees spend hours searching across systems, reconstructing context that should already be connected.

Traditional intranets and enterprise search tools treat this as a search problem. But keyword matching across disconnected systems does not create understanding. What organizations need is a knowledge graph that connects entities, documents, people, and processes into a single queryable foundation, accessible through natural language and governed by the same permissions that protect the underlying data.

Why Data Graphs for knowledge management
🔗
Connected organizational knowledge
Integrate information from DAMs, MAMs, PIMs, documents, wikis, databases, and enterprise systems into a single knowledge graph where relationships are first-class citizens.
💬
Natural language access
Ask questions in plain language and get precise answers grounded in your actual data. Built-in GraphRAG combines structured graph queries with semantic search over documents and content.
🔍
Context-aware search
Go beyond keyword matching. Data Graphs connects knowledge assets with related content, surfacing results based on meaning and relationships, not just text overlap.
🤖
AI agents over your knowledge base
External AI agents and copilots access your knowledge hub natively via MCP services. Build department-specific copilots, customer-facing assistants, or automated workflows grounded in governed knowledge.
🛡
Governed, permission-aware
Role-based access control ensures users and AI agents only see what they are authorized to access. Full audit trails on every query. Knowledge stays governed at every touchpoint.
📄
Structured and unstructured, unified
Documents, PDFs, rich media, and structured data coexist in the same graph. AI-assisted workflows extract structure from unstructured sources, making everything searchable and queryable.
Use cases
🏢

Enterprise knowledge hub

Give employees a single place to find answers to questions that span departments, systems, and document types. A product manager asks "What customer feedback have we received about feature X in the last quarter?" and gets a precise answer drawn from CRM notes, support tickets, and survey data, not a list of search results to sift through.

🔍

Intelligent internal search

Replace fragmented search with context-aware discovery. When a user searches for a contract, they also see related assets: the counterparty, associated projects, key dates, linked communications, and relevant policy documents. The knowledge graph surfaces connections that keyword search cannot.

👥

Connected customer intelligence

Combine customer data, market research, sales interactions, and feedback into a unified view. Uncover relationships between purchase patterns, engagement signals, and support history to power targeted marketing, informed sales conversations, and better product decisions.

How it works

From scattered information to connected intelligence

01

Model your knowledge domain: the entities, document types, relationships, and vocabularies that matter to your organization

02

Ingest content from existing systems: documents, databases, wikis, CRM, ERP, content platforms, and archives

03

AI-assisted enrichment extracts structure from unstructured content, linking documents to entities and relationships in the graph

04

Employees ask natural language questions and get precise, cited answers grounded in the governed knowledge graph

05

AI agents and copilots connect via MCP to build department-specific assistants, customer-facing tools, and automated workflows

How Data Graphs compares

Beyond search, wikis, and intranets

Capability
Data Graphs
Enterprise search
Wikis & intranets
Document mgmt
Relationship-aware queries
Knowledge graph native
Keyword only
No
No
Natural language AI (GraphRAG)
Built-in
Basic
No
No
AI agent access (MCP)
Native
No
No
No
Structured + unstructured
Unified graph
Index only
Text only
Documents only
Governed access control
Full RBAC, audit trails
Partial
Basic
File-level
Cross-system connections
Graph-native linking
Federated index
Manual links
Folder hierarchy

Ready to unlock your organizational knowledge?

See how Data Graphs can turn scattered information into connected intelligence that both people and AI can use.