From What to Why: Supercharge Your Rag with Temporal Depth
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Introduction
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.
The Limits of Static RAG
Retrieval-Augmented Generation (RAG) has revolutionised AI by grounding LLMs in factual data. However, by treating data as a static snapshot, it misses the most critical element of intelligence: evolution. This"temporal blindness" leads to incomplete answers and a superficial understanding of reality, failing to grasp the narrative hidden in your data's history. Vector embeddings capture a point-in-time snapshot, but they can't tell you the story of how that state was reached. Without understanding the sequence of events, an AI cannot distinguish between correlation and causation, leading to flawed conclusions and making it impossible to answer questions about how an entity's profile has changed over time.
Empower AI with True Contextual Understanding
Model How Relationships Evolve. Go beyond static connections. Our platform natively models how relationships are created, change attributes, and dissolve over time, providing a rich, historically aware foundation for AI reasoning.
Understand the 'When' and 'How'. Pometry allows your RAG application to query the dynamics of your data. Ask not just who is connected, but how long they have been connected, how frequently they interact, and what sequence of events led to their current state.
Achieve Deeper AI Understanding. By grounding your LLM in a temporal graph, you move from simple fact retrieval to sophisticated narrative comprehension. This allows for more accurate summaries, better-informed predictions, and truly intelligent AI-driven insights.
Conclusion
Don't just augment reality. Augment its history. Give yourAI the power of temporal perception and move beyond static snapshots. Empower your RAG applications with the rich, evolving context they need to deliver truly intelligent, historically aware insights.

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