How do people use TrustGraph?
TrustGraph is used wherever AI agents need accurate, structured knowledge — and where you need to trust the answers they give.
Enterprise knowledge and operations
Organisations use TrustGraph to unify fragmented knowledge — wikis, documents, tickets, databases — into a single queryable knowledge graph. Agents can answer questions that span multiple systems and follow relationships across the organisation: “What’s the impact of deprecating Service X on Customer Y?”
Internal assistants built on TrustGraph understand org structure, systems, projects, and ownership — not just text snippets. They traverse relationships (teams → services → incidents → SLAs) to give richer, more actionable answers than standard enterprise search.
Security, risk, and compliance
In security operations, TrustGraph connects users, hosts, alerts, and threat intelligence into unified threat graphs. Analysts ask natural language questions about security posture and get answers grounded in actual data relationships.
For compliance, regulations, policies, controls, and evidence are modelled as a graph. When regulations change, agents can identify which controls and assets are affected — automatically.
Finance, strategy, and research
Financial teams use TrustGraph for M&A analysis, competitive intelligence, and strategic planning — domains where understanding relationships between entities matters more than finding similar text. Research teams turn papers, patents, and lab notes into knowledge graphs that reveal non-obvious connections across projects.
Multi-tenant platforms
SaaS vendors embed TrustGraph as the knowledge layer for their own products, with per-tenant knowledge cores and strict isolation. Native multi-tenancy means each customer gets their own knowledge space with zero cross-contamination.
How TrustGraph compares
| Capability | Standard enterprise search | TrustGraph |
|---|---|---|
| Core architecture | Search indexing over documents | Context orchestration via hypergraph |
| Context depth | Document retrieval and vector similarity | Hyper-relational context: n-ary relationships capturing true enterprise events |
| Context management | Basic RBAC tied to SSO | Workspaces, Collections, and Context Cores: modular, isolated, reusable context units |
| Agent orchestration | Basic Q&A or simple LLM chains | HyperFlows: complex, chained agentic workflows with step-level LLM and graph config |
| Traceability | Logs of search queries | Real-time hypergraph traceability for all agent reasoning |
| Compute | API calls to proprietary LLMs | Open LLM stack: runs open models on Nvidia, AMD, or Intel hardware |
| Deployment | SaaS only | Self-hosted, BYOC, or SaaS |
The common thread
These use cases share a pattern: they need relationship-aware retrieval (not just text similarity), explainability (not just answers), and precision (not just recall). That’s what TrustGraph provides.
For detailed use case descriptions with example queries, see the full use cases page.
Next
Open source — what it means that TrustGraph is fully open source, and why that matters.