Empowering AI & Generative AI with True Temporal Graph Understanding

The recent OpenAI Cookbook on “Temporal Agents with Knowledge Graphs” has provided a brilliant blueprint for the next generation of AI: agents that don’t just answer questions, but reason over time, understand evolving contexts, and maintain a persistent, accurate memory. The cookbook perfectly outlines the what and the why – and the need to systematically update and validate a knowledge base, perform multi-hop retrieval, and resolve temporal conflicts.

Have you ever wondered when it’s better to cooperate versus when you should just look out for yourself? This is one of the oldest questions in human interaction, and a famous concept from game theory helps explain it perfectly: The Prisoner’s Dilemma.

If you’re exploring the world of data analytics, you’ve seen the power of graphs. By modelling your data as a network of entities (like people or products) and their relationships (like transactions or interactions), you unlock powerful insights that are easy for anyone to understand. But what if you’re only seeing a single frame from a feature-length film?

Graphs have attracted significant interest in recent years. In this blog, we dive deeper into the differences between the graph model vs the relational model to get a clearer view on when you might use one over the other.

We're thrilled to be featured in a recent article by The Alan Turing Institute that highlights the power of our temporal graph system, Raphtory, in overcoming the limits of traditional data analysis.

For every real threat, compliance teams are buried under an avalanche of false positives. This “alert fatigue” is more than just an annoyance; it’s a critical vulnerability and a massive operational drain. Investigators waste precious time chasing ghosts born from monitoring systems that lack context, while sophisticated, genuine threats risk being lost in the noise. The key to solving this isn’t more alerts, but more clarity.

For every real threat, compliance teams are buried under an avalanche of false positives. This “alert fatigue” is more than just an annoyance; it’s a critical vulnerability and a massive operational drain. Investigators waste precious time chasing ghosts born from monitoring systems that lack context, while sophisticated, genuine threats risk being lost in the noise. The key to solving this isn’t more alerts, but more clarity.

For an investigator, time is the most critical resource. Every minute spent waiting for a query to run is a minute a criminal has to cover their tracks. Unfortunately, legacy systems simply can’t keep up with the scale of modern data. The result is a slow, frustrating process where deep, exploratory analysis is impossible. To win the fight against financial crime, investigators need tools that operate at the speed of their own curiosity.

Modern financial criminals are choreographers of complex schemes. They don’t rely on a single, obvious action but on a carefully orchestrated sequence of events designed to appear legitimate in isolation. From smurfing and structuring to synthetic identity fraud, the true criminal intent is hidden in the pattern, not the individual steps. To defeat these adversaries, you must learn to read the grammar of their crimes by analysing the sequence of their actions over time.

Your most valuable insights are not hidden in a single database; they are scattered across dozens of disconnected systems. Customer data lives in one silo, transaction logs in another, and event streams in a third. For an AI application, trying to make sense of this fragmented landscape is like trying to read a story with every other page torn out. To unlock true intelligence, your AI needs a unified, historically aware foundation.

Semantic search was a monumental leap forward, allowing us to find information based on meaning, not just keywords. But it operates in a flat, timeless world. It can find what you’re looking for, but it often misses the deeper context of why it’s relevant now. True understanding, the kind that drives accurate decisions, requires a fourth dimension: time.

An AI application can have the most sophisticated reasoning capabilities in the world, but if it’s waiting for data, it’s useless. In today’s high-velocity environments, the value of an insight diminishes with every passing millisecond. The challenge is that as datasets become larger and more dynamic, the context retrieval process becomes a crippling bottleneck, leaving your AI feeling perpetually slow and a step behind reality.

Vector embeddings have given AI applications a phenomenal ability to understand semantic meaning at a single point in time. But this is like understanding the world through a series of disconnected photographs. While each photo is accurate, the story - the narrative of how things change and connect - is lost between the frames. For AI to achieve true contextual understanding, it needs to see the whole movie, not just the snapshots.

A traditional risk score is a snapshot in time. It’s a static assessment based on current data that becomes outdated the moment it’s calculated. In the fast-paced world of financial crime, this is a critical vulnerability. Bad actors can escalate from low-risk to high-risk in minutes, not months. To combat this, you need a dynamic risk scoring model - one that lives, breathes, and adapts in real-time as new data arrives and behaviours change.

Sophisticated criminals and sanctioned individuals are masters of concealment. They hide their influence behind complex webs of shellcompanies, trusts, and nominee directors, making it nearly impossible fortraditional KYC and due diligence checks to identify the Ultimate Beneficial Owner (UBO). To break through this wall of obfuscation, you need to go beyond static ownership charts and reconstruct the entire, evolving history of control and interaction.

The future of AI is collaborative. We are moving beyond monolithic models to sophisticated agentic frameworks – teams of specialised AIs working in concert to solve complex problems. But like any high-performing team, they need a single, reliable source of truth. Without a shared understanding of reality and its evolution over time, these AI teams, just like humans, descend into chaos, working with conflicting data and drawing contradictory conclusions.

Standard RAG applications are powerful, but they have a blind spot: time. They can retrieve what an entity is, but they can't understand its journey. Pometry’s temporal graph foundation empowers your AI to understand the crucial context of when and how connections form, change, and dissolve, transforming flat, static answers into rich, dynamic narratives.