One context layer, many use cases.
See how organisations are using Pometry to transform AI outcomes today.
The warning signs are in your data. You just can't see them.
of transformation programmes fail
Standish Group, 2024spent annually on digital transformation
IDC Worldwide Digital Transformation Spending Guide, 2022of work time is reclaimable with Pometry
Pometry analysis, 2025Transformation programmes are failing at scale.
Patterns that signal failure are already in your data, but they're invisible to Gantt charts, BI dashboards, and status reports.
No temporal visibility
Existing tools capture snapshots. They can't see how dependencies are evolving, when bottlenecks formed, or which decisions caused downstream delays.
Reporting lags reality
By the time issues surface in status reports, they're weeks old. The root cause has compounded into something far harder to unwind.
Complexity is invisible
At enterprise scale, no single team holds the complete picture. Resource concentration, priority dilution, and systemic failures hide in plain sight.
"If the signs were visible six months ago, why didn't we act before the risk became a crisis?"Global Bank CIO
Decision-grade context. In weeks, not months.
Temporal Intelligence
Your data has a history. Pometry models the full chronological context: every change, connection and pattern, not just a snapshot of today.
Natural Language Interface
Leaders ask questions in plain English. Pometry returns answers with full provenance, with every finding traceable to source data. Works with any LLM.
Actionable Diagnostics
Identify structural risks, quantify savings, and deliver concrete recommendations in a single engagement.
Zero-Friction Deployment
No data movement. Reads directly from Jira, GitHub, ServiceNow, data lakes or wherever else delivery data is stored.
in reclaimable value identified
- 28% of total work time reclaimable through systemic failure patterns
- Delivered first outputs in just 3 weeks, compared to competitor proposal of 6 months
- Required a single Forward-Deployed Engineer
Avoidable Cancellations
100,000s of person-days wasted on preventable work cancellations across the programme.
Incident Loops
SLA breaches consumed ~30% of total work time due to cross-team inefficiencies and closeable process loops.
Resource Concentration
⅓ of critical-path work was unknowingly assigned to oversubscribed staff.
Priority Dilution
Prioritisation methods had no bearing on execution sequencing. Pometry identified suitable resolutions.
Every number has a story. Make sure you can tell it.
in FCA enforcement fines in a single year
FCA Operating & Service Metrics, 2024–25spent by UK banks on regulatory reporting every year
Bank of England Future of Finance, 2019new COREP templates under Basel 3.1
Deloitte, Basel 3.1 analysis, 2024Your reports can answer "what". They can't answer "why".
Regulators, auditors, and boards are asking harder questions, not just what the number says, but how you arrived at it, and what it looked like six months ago. Most reporting stacks have no answer.
No provenance
A number changes. A regulator asks why. Tracing it back through source systems, transformations, and manual overrides is a weeks-long investigation. Often the full answer is never recovered.
History overwrites itself
Once a report is signed and filed, the underlying state that produced it is gone. You can't replay what the data looked like at signing without maintaining costly separate snapshots.
Lineage is design, not reality
Most lineage tools map column dependencies, showing what the pipeline was designed to do. They can't tell you what data actually flowed, what values changed, or why a specific output differed last Tuesday.
Complete traceability over time, every time.
No More Data Silos
Pometry builds a unified context model across all source systems. Lineage isn't trapped inside individual team tools or hand-offs. A single query spans your entire data estate.
Point-in-Time Replay
Reconstruct your data exactly as it existed at any prior date. Report on past state without maintaining separate historical snapshots or archive databases.
Row-level lineage
Pometry builds the lineage graph as data flows through your systems. No manual documentation. No maintenance. No drift between records and reality.
Natural Language Audit
Ask "why did this capital ratio change between Q2 and Q3?" in plain English. Get a complete, traceable answer with full source attribution, not a guess.
Catch financial crime before it compounds.
laundered across the world every year
UNODC, 2024of AML alerts are false positives
Unit21, 2025improvement in motif detection with Pometry
Pometry analysis, 2025Financial crime is designed to be invisible.
Criminals engineer complexity (layered structures, evolving tactics, and distributed networks) to stay ahead of detection. Most AML tools aren't built to keep up.
Networks form faster than you can map them
Manual analysis across thousands of data sources doesn't scale. A full investigation can take weeks. By then, the money has moved on.
Existing tools only see the present
Traditional graph databases give you a snapshot. Money laundering unfolds over weeks and months. Without temporal context, you're always looking at a frozen picture, never the pattern.
You can't know what you've missed
Rule-based systems find what they were built to look for, nothing more. Finding one syndicate doesn't mean you've found them all. Unknown unknowns present a real threat.
Every entity. Every connection. Over time.
Point-in-time reconstruction
Reconstruct your full entity network at any point in history. See relationships that have since dissolved, trace transactions backwards, and pinpoint exactly when suspicious behaviour first emerged.
Unknown network discovery
50+ graph algorithms run across your entire entity population, not just flagged accounts. Surface criminal networks and syndicates sharing common nodes that no alert rule would ever reach.
Automated typology matching
Scatter-gather, fan-out, mule networks, pairwise schemes - detected automatically without manual rule-writing. Finds known typologies and flags novel structures that match no existing alert.
Natural language investigation
Investigators ask questions in plain English. Pometry returns evidenced answers traceable to specific nodes, edges, and timestamps, not just a list of flagged transactions.
Smarter AI needs organisational context.
uplift in LLM accuracy with Pometry
Pometry analysis, 2026LLM memory benchmark accuracy with Pometry
Hippocampus neural memory, 2026Drop in LLM performance on complex queries without Pometry (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, but 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 but is 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 get a shared understanding of how work has evolved, what decisions led to what outcomes, and how changes cascade.
NeuroSymbolic Retrieval
Three parallel search paths (semantic, graph traversal, and exact match) run simultaneously. Works with any LLM.
Cost effective at 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 Pometry's context model.
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.
A complete, live view of every client.
annual revenue lost to data silo inefficiencies
IDC Market Researchof banker time spent in client dialogue vs. admin tasks
McKinsey & Company, Dec 2025clients acquired per banker per year without Pometry
McKinsey & Company, Mar 2023Client context is scattered across every system.
The data exists, but it’s spread across CRMs, transaction systems, comms logs, product databases and third party sources.
Siloed data, siloed view
Client records live across dozens of disconnected platforms. Relationship managers build their view manually (from memory, emails, and spreadsheets) and it's always incomplete.
Relationships are invisible
Who owns what. Which entity controls another. Which client is connected to a counterparty under stress. These connections only surface when something has already gone wrong.
Insight takes weeks to assemble
Manual analysis to produce a single client 360 takes weeks of analyst time. By the time the picture is ready, the window to act has often passed.
Every client. Every connection. As it happens.
Entity relationship mapping
Surface every connection: subsidiaries, beneficial owners, counterparties, shared directorships. The complete corporate network, not just the accounts your systems already know.
Change detection
Track how client relationships evolve in real time, so you can ensure RM coverage is optimised, new opportunities are identified, and emerging counterparty risks are flagged.
Natural language queries
Relationship managers ask questions in plain English. Pometry returns answers with full data provenance, with every insight traceable to source and ready for compliance review.
Analyst-grade intelligence in hours
Replace weeks of manual assembly with a query. Pometry delivers the depth of a full analyst engagement in hours, ready to act on, not just read.
You have data. You don't have context.
more revenue when bank services are personalised
McKinsey & Company, 2021in revenue shifting to personalisation leaders over the next five years
Boston Consulting Group, 2024ROI uplift on marketing spend with Pometry
Pometry analysis, 2025Insight lives in relationships, and how they evolve.
Your users leave a trail across every channel: logins, transactions, product interactions, support contacts. But without a way to connect that activity across systems and track how it changes over time, the trail disappears.
Segmentation built on static labels
Demographics tell you who someone is. Behaviour tells you what they do. Most organisations only have one of those, and they're making spend decisions based on it.
Decisions made on lagging indicators
By the time a report surfaces a trend, the moment to act has often passed. Marketing and product decisions need forward signal, not backward views.
No way to model "what if"
There's no mechanism to ask "if we invest X in segment Y, what's the expected return?", let alone measure it against a live model.
From fragmented data to decision intelligence.
Behavioural Segmentation
Personas built from how users actually behave, not who they are on paper. ~95% accuracy at scale, updated continuously as new data flows in.
What-If Scenario Modelling
Simulate the impact of investment decisions before committing spend, grounded in your live context model.
Temporal Pattern Detection
Change points, emerging trends, and evolving dynamics surfaced automatically. Segments recalibrate continuously as new data flows in.
Natural Language Queries
Pometry's LLM queries the graph directly. Every answer is grounded in verifiable data, traceable to specific users, interactions, and time points.