Beyond Snapshots: Uncover the True Story Your Evolving Data Tells
Most data systems show you what is. Pometry reveals how it became, and what it could be next. Dive into the power of our native temporal graph core to unlock unprecedented insights, causality, and predictive power from your most complex data.
Native Temporal Engine
Unlike simple timestamping, our core is designed to intrinsically model and manage data as it evolves, capturing a continuous, queryable history.
Rich Temporal Semantics
Use Event Graph semantics to analyse activity within a window, or Persistent Graph semantics to see the most current state of entities within a historical context.
Instant Time Travel
Freely travel between versions of your graph with millisecond speed. Access the state of your graph as it was one year ago, one month, or one second ago.
Your Catalyst for Real-Time Temporal Business Intelligence
Transforming Data into Actionable Insights
Trusted by Innovative Companies Worldwide
Native temporality means time is a fundamental, built-in dimension of our graph model and query engine. It's not just about storing timestamps; it's about efficiently storing, indexing, and querying the evolution of data, relationships, and properties as a continuous history, allowing for complex temporal analysis out-of-the-box.
Questions like: “What are the top 3 risks facing the organisation?”, “What is John Citizen going to do next?”, "How did this specific customer's relationship network evolve in the month before they churned?", "What sequence of supply chain events historically led to delays of this type?", "Show me all assets that changed their risk score due to new indirect connections forming in the last 24 hours.", "What was the state of this entire system just before a critical failure?"
Evolving predicates mean where you follow an edge on Monday and then Tuesday for the next one, then Wednesday for the next one. Or semantically, the ability to model and query how the nature or properties of relationships themselves change over time (e.g., a 'friend' relationship becoming a 'colleague' relationship, or the 'weight' of a connection varying). We support different semantic models like Event Graphs (sequences of occurrences) and Persistent Graphs (enduring entities/relationships that change).
Accessing any historical state of the graph is an O(1) operation, meaning it typically completes in milliseconds, regardless of how far back in time you go. There's no need to rebuild snapshots from logs or re-process historical data to query a past state.
Pometry incorporates many core benefits of a TSDB (efficient storage of time-stamped data, fast time-based queries) and adds the rich context of graph relationships. We will soon be rolling out a comprehensive suite of time-series specific algorithms, making it a powerful alternative or complement.
By analysing how patterns, behaviours, and relationships have evolved leading up to past outcomes, you can identify leading indicators and build more accurate predictive models. Understanding the sequence and timing of changes is crucial for forecasting future states.
Unlock Your Data's Potential: Time Reveals All
Stop analysing a static world. With Pometry's native temporal core, you can finally see, understand, and act on the critical dimension of change. Discover the patterns, causes, and predictive signals that drive your business forward.
