The AI-Ready Data Foundation for Cyber Security

Knowledge Grid transforms fragmented telemetry into structured, high-signal knowledge that improves analytics, supports anomaly detection, and helps AI systems reason more effectively.

AI performance is Dependent on COntext

Knowledge Grid adds a cognitive data layer between telemetry sources and downstream tools, making security data more usable by structuring additional context that can be used for detection, investigation, and intelligent workflows.

The Problem

Legacy data stacks were built for storage, search, dashboards, and human investigation. They were not designed to organize telemetry into machine-usable knowledge for AI-driven analysis.

The Cognitive Data Platform

A cognitive infrastructure that transforms raw security telemetry into high-signal, machine-ready knowledge.

Telemetry Synthesis

Automatically condenses massive log streams into compact, structured Knowledge Descriptions for rapid retrieval.

Temporal Reasoning

Maintains stateful awareness of behavior over time, allowing AI agents to differentiate between noise and true drift.

Relational Context

Maps hidden relationships across disparate data sources, empowering machine reasoning for root-cause analysis.

AI-Native

We are an AI native data platform, purpose built for machine data consumption, not a retrofitted data lake/warehouse.

How it works

Data Structure

Knowledge Grid organizes data through a set of purpose-built structures that capture what exists in the data, how elements relate, and what context gives them meaning. Together, they create an AI-ready representation optimized for search, detection, model development, and agentic workflows.

Artificial Intelligence

Empowering Agentic Workflows

Better Context for LLMs

Knowledge Grid transforms raw telemetry into AI-ready knowledge structures. This provides LLMs with high-signal context rather than noisy data, significantly reducing hallucinations and improving reasoning accuracy for security analysts.

Data Science Workbench

Knowledge Grid embeds advanced data science directly into the data layer—automatically transforming raw telemetry into structured, high-signal knowledge through feature engineering, summarization, and correlation. This enables real-time analytics, unsupervised anomaly detection, and AI-ready data that improves the accuracy and effectiveness of agentic AI and LLM-driven workflows.

Support for Agents

Knowledge Grid provides the differentiated relational foundation required for agentic workflows. Agents can navigate complex data grids to synthesize evidence, perform multi-step investigations, and execute machine reasoning at scale.

✓

A unified Temporal Data Grid scale-out architecture ensures consistent, high-fidelity context for safe agentic workflows.

Why Traditional Data Stacks Fall Short

Conventional telemetry stacks are optimized for ingestion and storage first. As data volumes grow, organizations are forced into expensive tradeoffs: retain less history, eliminate or index only selected fields, parse only what is already known, or pay for increasingly large compute pools to keep query performance acceptable.

The Cognitive Data Layer takes the opposite approach. It assumes that the main bottleneck is not only where data is stored, but how it is represented. By converting data into compact summaries and contextual structures at ingestion time, the platform shifts cost from repeated full-data searches to reusable knowledge objects.

Traditional Data Stacks

Knowledge Grid

Raw logs create massive storage overhead and search latency, making AI retrieval expensive and slow.

✓

Feature Summaries compress telemetry into compact, high-signal structures designed for instant AI context.

Static schemas cannot capture behavioral drift or evolving patterns, leading to stale model accuracy.

✓

Temporal Histograms profile behavior in real-time, providing AI with the baseline needed for drift detection.

Isolated data silos prevent deep relational reasoning, forcing agents to guess 'why' an event occurred.

✓

Knowledge Correlations link behavior across the grid, allowing models to explain relationships and prioritize action.

Legacy indexes break down at scale, resulting in fragmented context that triggers unreliable automation loops.

AI is only as powerful as the data behind it—Knowledge Grid transforms fragmented telemetry into structured, high-signal knowledge, creating the data foundation required for accurate analytics, effective anomaly detection, and reliable agentic AI.

Knowledge Not Data

AI is only as effective as the data it learns from.
Better data doesn’t just improve AI—it defines it.
Models are powerful, but data determines their accuracy.

AI Needs More than Data - It Needs Knowledge

Decision Making

AI can’t reason without context—and context comes from data.
Agentic AI needs structured knowledge, not raw data.

Quality & Outcomes

Clean, structured data drives trustworthy AI outcomes.
Poor data leads to confident but wrong decisions.