Enterprise AI needs enterprise context.
AI agents are only as good as the context they reason over. Pometry gives them a live, time-aware understanding of your organisation, so every answer is grounded, traceable, and current.
uplift in LLM accuracy with Pometry's context layer
Pometry analysis, 2026LLM memory benchmark accuracy achieved with Pometry (GraphRAG)
Hippocampus neural memory, 2026LLM accuracy drop on complex queries (with standard RAG)
MultiHop-RAG, Tang & Yang, 2024Agents are intelligent. They just don't know your organisation.
Without organisational memory, even the best models are guessing. They have language. They don't have history. And in regulated industries, that gap isn't a technical inconvenience. It's a liability.
No memory of what happened
Agents can't tell you why a decision was made six months ago, who was involved, or what the downstream effects were. That context exists in your systems. It's invisible to your AI.
Retrieval without relationships
Standard RAG pulls fragments of text ranked by similarity. It doesn't understand how entities connect, how those connections have evolved, or which reasoning paths are trustworthy.
Answers without provenance
If an agent can't show you where its answer came from, you can't trust it. In regulated industries, you can't use it. Explainability isn't optional. It's a deployment requirement.
From generative to grounded intelligence.
Organisational Memory
Agents understand how work has evolved, what decisions led to what outcomes, and how changes cascade. Not from snapshots. From the full temporal record of your organisation.
NeuroSymbolic Retrieval
Three parallel search paths (semantic, graph traversal, and exact match) run simultaneously. Works with any LLM.
Unprecedented Scale
3M edges processed per second. Sub-second queries across billions of relationships. Works with local and mini models as LLMs only need to navigate the context layer.
Full Auditability
Every answer or agent decision is grounded in verifiable data, traceable to specific events, decisions, and time points. Required for regulated industries. Built in from day one.
NeuroSymbolic retrieval. Three paths. One answer.
Conventional RAG systems search text. Pometry's system uses NeuroSymbolic GraphRAG: searching through structure, meaning, and time simultaneously.
Resource concentration in TEAM-007 (March 2024) propagated into 3 downstream delays by June 2024.
First detectable signal appeared 8 weeks before breach. Full decision tree available.
Embedding-based retrieval
Finds conceptually related entities across your knowledge graph, even when terminology varies.
Symbolic temporal reasoning
Follows relationships to identify causal chains and surface structural patterns invisible to vector search.
Deterministic lookup
Precise matching against structured data. No probabilistic guessing, no hallucination risk.
Full lineage on every answer
Every result traces back to specific events, decisions, and time points. Required for regulated industries. Built in from day one.
See what your agents have been missing.
Talk to us to find out how we work.
Explore the platform: LLM & MCP · Explorer · How we work