Move AI from pilot into production.
A living model of your decisions, context and know-how, kept current and queryable, so your teams and your agents work from what the organisation already knows, not from whatever a document happens to say.
Uplift in LLM accuracy with PometryPometry analysis, 2026
Drop in LLM performance on complex queries using standard RAGMultiHop-RAG, Tang & Yang, 2024
Agents are intelligent. They just don't know your organisation.
Without organisational memory, even the strongest models are guessing. They have language, but no history. In a regulated institution that gap is a liability.
No memory of what happened
An agent can't tell you why a decision was made six months ago, who was involved, or what it caused downstream. That context is in your systems and invisible to your AI.
Retrieval without relationships
Standard retrieval pulls fragments of text ranked by similarity. It doesn't know how entities connect, how those connections changed, or which reasoning path is trustworthy.
Answers without provenance
If an agent can't show where an answer came from, you can't trust it. In regulated industries you can't use it at all.
Answers your agents can show the working for.
Pometry gives your models a structured, temporal view of how the organisation works. It sits behind whichever LLM you already use, reached through the Model Context Protocol.
Shared organisational memory
Agents and people draw on the same record of how work evolved, which decisions led to which outcomes, and how a change cascaded.
Current by construction
The model updates as the underlying systems change, so knowledge doesn't need a curation process to stay accurate.
NeuroSymbolic retrieval
Semantic search, graph traversal and exact match run in parallel, so structure and meaning are searched together. Works with any model.
Efficient at scale
The retrieval work happens in the graph, so the language model handles composition rather than search. Local and smaller models remain viable.
Full auditability
Every answer and every agent action is grounded in verifiable data, traceable to specific events, decisions and points in time.
Time-scoped answers
Ask for the state as of a past quarter and the model returns that state, not today's answer wearing an old date.
It sits behind the model you have already chosen.
Pometry is not an assistant and does not replace your AI stack. It is the layer your existing models query when they need to know how the organisation works.
Reached as a tool call
Exposed through the Model Context Protocol, so any model with tool calling can use it. There is no custom integration to build per model.
Model agnostic
Works with commercial models, open-weight models and models hosted inside your own boundary. Swapping the model does not change the context layer.
Permissions travel with the query
An agent traverses only what its caller is entitled to see, controlled down to individual nodes, edges and time windows.
Experience Pometry in action
with a live demo.
Find out how you can close your visibility gap with unique, institutional intelligence.