THE COGNITIVE DATA LAYER
Your AI Shouldn't Have to Reconstruct Meaning From Raw Data.
Knowledge Grid creates the missing layer between telemetry and machine reasoning — transforming raw data at ingest into structured, temporal and context-rich knowledge that models and agents can use repeatedly.
WHY A NEW DATA LAYER IS NEEDED
We Made the Models AI-Native.
But, We Never Did the Same for the Data.
01 — THE DATA PROBLEM
Telemetry wasn't built for AI
Security data is fragmented, noisy and optimized primarily for storage, search and human investigation.
02 — THE CONTEXT PROBLEM
Meaning gets reconstructed too late
Relationships, behavior, history and environmental context are repeatedly assembled after ingestion.
03 — THE REASONING PROBLEM
AI starts every question with too little knowledge
Models and agents spend compute and tokens rediscovering what the underlying data layer should already know.
What the Cognitive Data Layer actually creates
From Data to Durable Knowledge
Persistent Environmental Context
Entities
Who and what exists?
Relationships
How are they connected?
Behavior
What is normal?
Temporal State
What changed?
Findings & Memory
What have we already learned?
The Cognitive Data Layer changes how raw data is represented—organizing context, relationships, behavior, and time into reusable knowledge structures at ingest. The result is a persistent, continuously evolving foundation that gives analytics, LLMs, and agents the context they need to reason more accurately without rebuilding meaning from scratch.
AI capability moved faster than the data underneath it
The last 24 months of progress in AI and LLMs have been staggering. We now have models that can reason, plan, and simulate complex logic. But these models are only as effective as the data they consume. While the AI layer evolved at light speed, the data layer remained stuck in the past—reliant on raw, flat, disconnected telemetry.
Reconstructing Context
Today’s security operations are drowning in this raw data. Analysts and AI agents spend the majority of their time trying to reconstruct context: what happened before this alert? How does this IP relate to that user? Is this behavior normal for this specific system? Because the data is stored in its rawest form, machines have to do the heavy lifting of "knowing" your environment every time they run a query.
Transformation at Ingest
Knowledge Grid changes this by shifting the intelligence upstream. We believe that context shouldn't be a post-processing task; it should be an inherent property of the data itself. By transforming telemetry into knowledge at the point of ingest, we bridge the gap between petabytes of raw signals and the high-level reasoning required for autonomous security.
THE COGNITIVE DATA LAYER
The Missing Data Layer Between Raw Telemetry and AI Reasoning
Knowledge Grid transforms raw, fragmented telemetry at ingest into time-aware, context-rich, reusable knowledge — so analytics, LLMs and agents can reason from what your environment already knows instead of reconstructing context from raw data every time.
Better Data Quality
Structure and enrich data before AI consumes it.
Persistent Context
Preserve entities, relationships, behavior and change over time.
Machine-Ready Knowledge
Give models and agents compact knowledge structures built for reasoning.
Works alongside your existing SIEM, data lake and security stack. No rip-and-replace required.