A context graph is a graph of entities, relationships and situational metadata—who, what, when, why and how—optimised for AI consumption. It goes beyond a static knowledge graph by encoding decision traces, lineage and permissions so LLMs, RAG and agents retrieve precise, explainable context for a task.

Why context graphs matter now

Generative AI struggles when it lacks context: which version of a document is authoritative, how systems connect, who owns a process, and what changed. That’s why leaders are naming context graphs as the next enterprise data platform—so assistants and agents can reason over a live map of your work.

Research backs the shift. GraphRAG builds a knowledge/relationship graph from your corpus, adds community-level summaries, then uses that structure at query time to retrieve and stitch answers more robustly than vector-only RAG.

What a context graph is (and isn’t)

• Knowledge graph: represents entities and relationships (“what is connected to what”).

• Context graph: adds operational and temporal context—lineage, policies, ownership, time, decisions, and relevance signals—engineered for LLM use (token efficiency, provenance, ranking). This makes model answers traceable and task-aware.

Vendors implementing this pattern show similar ingredients: connectors to work apps, identity/permissions, object linking (people ↔ docs ↔ tickets ↔ systems), and personalisation so each user sees what’s relevant.

How context graphs supercharge AI

1. Better retrieval for RAG: Graph structure narrows to the right nodes/paths before fetching passages—improving precision and reducing hallucination.

2. Explainability & provenance: Edges carry source, owner, and time; answers cite where facts came from.

3. Agent reliability: Agents can traverse relationships (“customer → contract → risk flags”) to plan actions safely. Recent Neo4j examples show end-to-end, decision-style workflows powered by context graphs.

4. Personalisation & security: Identity-aware graphs tailor results per user without leaking data across boundaries.

Practical examples

• Customer support: Fetch policy, latest incident, product tier and renewal date to draft an answer with citations and required approval steps. (Graph-guided retrieval + permissions).

• Sales preparation: Traverse “account → stakeholders → open issues → reference wins → decks” to auto-prepare a call brief. (Contextual knowledge graph + ranking).

• Risk decisions: Neo4j’s walkthrough shows agents querying a context graph to assess a credit-limit increase—blending rules, history and relationships.

Implementation roadmap (60–90 days)

1) Scope the graph (week 1–2). Start with one journey (e.g., support resolution or sales prep). List core entities, relations and events you need at answer time. Use GraphRAG guidance to map corpus → nodes/edges → summaries.
2) Connect systems (week 2–4). Ingest identities, docs, tickets, CRM and wiki; preserve object IDs and permissions. Many enterprise search platforms (e.g., Glean) ship connectors and a company knowledge graph baseline you can extend.
3) Add operational context (week 3–6). Attach lineage, owners, timestamps, and policy tags; store provenance needed for audit and citations.
4) Optimise for LLMs (week 4–8). Generate community/cluster summaries, index graph neighborhoods, and pre-compute paths for common tasks (GraphRAG playbook).
5) Ship the assistant/agent (week 6–10). Have it traverse the graph before retrieval; log every hop and source in the response. Pilot with one team; measure precision, deflection, time-to-answer and citation coverage.

Tooling options (pick what fits your stack)

• Enterprise search/AI platforms that already build a knowledge/context graph across your apps for personalised answers and agents.

• Graph databases (e.g., Neo4j) when you want full control over schema, traversal and analytics; helpful for agentic workflows.

• Open methods like GraphRAG to turn raw text into a graph + summaries for stronger retrieval.

Governance and success metrics

Treat the context graph as critical data infra. Track: answer precision/recall, citation rate, dwell on sources, incident rate, and time-to-resolve. Keep provenance, access controls and change logs intact to support audits and trust.

FAQs

Q1. What’s the difference between a context graph and a knowledge graph?
A knowledge graph models entities and relationships; a context graph adds lineage, permissions, time and decision traces, engineered so LLMs can retrieve and explain answers reliably.

Q2. How do context graphs improve RAG?
They narrow retrieval to the most relevant neighbourhood in your corpus—often with pre-built community summaries—so answers are more precise and easier to cite.

Q3. Where should we start?
Pick one journey (support or sales), ingest core systems with permissions, attach provenance and timestamps, then pilot an assistant that traverses the graph before retrieval. Measure precision, time-to-answer and citation coverage.