Give your LLM structured context it can actually use.
Pometry grounds every AI response in your context layer. Responses come fully traceable and auditable. No black box hallucinations.
What the LLM layer does.
Contextual answers
Ask questions in natural language. Get precise, evidenced answers grounded in your graph, not hallucinated from training data.
Works with any LLM
ChatGPT, Claude, Gemini, Llama or internal models. Any model with tool-calling support works out of the box. No custom integration required.
NeuroSymbolic retrieval
Symbolic graph traversal plus neural language models working together. Deterministic accuracy for facts; natural language flexibility for interface.
Full auditability
Regulators and compliance teams can inspect the exact graph path behind any AI-generated output. Every claim traces back to a node, edge, or time.
Temporal precision
Answers are time-scoped by default. Ask "as of Q3 2024" and get an answer grounded in that exact historical state.
Explainable reasoning
Models show their working at every step. Ask why it reached a conclusion and get the exact graph path, entities, and events that led there.
Simple answers. Serious power.
Give leaders live visibility without disruption. Ask complex questions, get simple answers. Under the hood Pometry's LLM layer combines symbolic graph traversal with neural language understanding. Every answer is grounded in verifiable graph facts, not document chunks.
Natural language → Graph query
The model translates your question into a precise graph traversal and runs it against the live temporal graph. The answer is deterministic and auditable.
Semantic search → Entity context
Vector similarity finds the most relevant entities and relationships. The graph layer retrieves their full temporal context, including history, connections, and change events.
Algorithm → Narrative
Tools for temporal algorithms (e.g. centrality, community detection, path analysis). Let the LLM translate the output into a plain-English executive narrative.
Pometry MCP.
Expose your context layer as a first-class tool for any model that supports the Model Context Protocol. No custom integration required.
Graph traversal as a tool call - models can call pometry.query() the same way they call any other tool.
Temporal parameters - every tool call accepts at and between filters natively.
Compatible with Claude, GPT-4, Llama, and more - any model with MCP tool support works out of the box.
tool: "pometry.query" parameters: { question: "Which teams have the highest dependency concentration risk as of Q4 2024?", at: "2024-12-31", output_format: "ranked_list", explain: true } response: { results: ["Payments Platform", ...], evidence: [{ node: "team:PP", score: 0.91, reason: "..." }], timestamp: "2024-12-31T00:00:00Z" }
Your data is ready.
Are you?
Request a demo or a two-week diagnostic on real data. We'll surface key risks, patterns and opportunities your current tooling cannot see.