The capabilities that make Institutional AI possible.

Four core features that give your AI infrastructure temporal depth, structural reasoning, compounding memory, and the traceability regulators require.

Live Context

Every entity. Every relationship. Updated live.

A dynamic temporal graph that captures every entity, relationship, and event across your organisation and keeps it live as your data changes. The foundation everything else is built on.

Context Model — Incoming Events Live
14:23:18.042 Entity Programme "Apollo" linked to Portfolio — 3 systems, 4 owners
14:23:18.119 Relation Bradford Spiers engaged with Programme Apollo (role: Exec Sponsor)
14:23:19.003 Event Budget update: £2.3M allocated to Programme Delta
14:23:19.441 Relation System "RTGS" connected to "AML Gateway" — latency: 23ms
14:23:20.112 Event Vendor "Accenture" status changed: ACTIVE → UNDER REVIEW
14:23:21.448 Entity New dependency resolved: 6 shared owners across 2 programmes
14:23:22.890 Query "Who owned risk on Programme Delta as of Q3 2024?" — answered in 210ms

Real-time ingestion

Events flow continuously into the model and are reflected immediately — no batch reprocessing, no delays.

Point-in-time queries

Query the exact state of your organisation at any historical moment, down to the millisecond. Then compare it with today.

Entity resolution

Data from multiple sources is reconciled into a single view of each entity, with full lineage preserved throughout.

Temporal history

Every change is stored as a timestamped edge. Replay, audit, or compare any period without rebuilding from scratch.

What is a context model? →
Hybrid Intelligence

AI that traces its reasoning, not just retrieves it.

NeuroSymbolic GraphRAG combines the pattern-recognition power of LLMs with the structural precision of temporal graph traversal. Answers that follow chains of relationships across time and cite every step.

Multi-hop reasoning

Follow chains of relationships across your organisation to answer questions that pure semantic search can't reach.

Traceable answers

Every response cites the exact nodes, edges, and timestamps used — not source documents, but live data.

Temporal context

Ask about any historical period and get answers grounded in what was actually true at that time.

LLM-agnostic

Works with any model that supports tool calling — Claude, GPT-4, Gemini, Llama, or your internal deployment.

NeuroSymbolic GraphRAG — Query Temporal Graph
Natural Language Query "Which vendors appeared in all three failed programme implementations in the past 18 months?" Traversing transformation_programme graph (Q1 2023 — present)... Identified 847 vendor relationships across 14 active programmes Filtering: delivery_status = FAILED, outcome_date in window Cross-referencing procurement and risk timestamps... 3 vendors with overlap across ≥2 failed programme edges
Answer "Vendor Accenture appeared in all three: engaged Q2 2023, Q4 2023, and Q1 2024. Overlap with escalated risk events: 94%. Shared personnel across programmes: 6." 3 nodes · 18 edges · 24 timestamps cited  ·  340ms
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Organisational Memory

Your organisation, learning from itself.

Most AI resets with every new query. Pometry's context model compounds over time — capturing structural patterns, preserving institutional knowledge, and making every new query richer than the last.

Pattern accumulation

Structural patterns that emerge over months or years are preserved and queryable — not lost to system resets or model context limits.

Cross-system unification

Data from disparate systems — project management, procurement, HR, finance — is unified into a single consistent view of your organisation's history.

Knowledge retention

When people leave, their relationships, decisions, and context remain in the system. Institutional memory doesn't walk out the door.

Compounding value

The longer Pometry runs, the richer the context it accumulates. Value grows with time. Every new event makes every past query more useful.

Explore the context model →
Regulator-Grade Trust

Every answer, fully provenance-traced.

Every output from Pometry's AI is traceable to the specific data that produced it. Full provenance, complete audit trails, and fine-grained access control baked in at the graph level.

Provenance

Full data lineage

Every AI answer cites the exact nodes, edges, and timestamps used — not documents, but live data points with complete lineage back to source.

Access

Role-based control

Granular permissions at the entity and relationship level. Define exactly what each user or system can query — at graph resolution, not document resolution.

Audit

Immutable history

The temporal log cannot be altered retroactively. An unimpeachable record for regulators, compliance teams, and internal audit.

Deploy

On-disk operation

Data never leaves your environment. Works in air-gapped or hybrid deployments with no data egress required. Already used by Tier 1 banks and government agencies.

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Learn more about Pometry's AI Infrastructure.

Book a call, view more information, or run a secure, two-week trial in your own environment.