// Cignal Engine

The Cignal Engine is a physics-based AI learning environment that combines hyper-realistic digital twins with high-fidelity sensor models to train AI systems for threat detection, security screening, and remote sensing applications.

Fast, Accurate Image Generation

The Cignal Engine is the high-performance image generator in OpenLane that generates high-fidelity X-ray, X-ray CT, and Millimeter Wave screening data.

Improve Detection

Improve the accuracy and coverage of AI-based detection systems.

Visualize Novel Threats

Analyze and explore detailed digital twins of real-world threats.

Adapt Faster

Develop models that reflect rapidly changing and emerging threats.

Critical Sectors

The Cignal Engine is designed to support the unique needs of critical sector enterprises.

Homeland Security

85% of security practitioners attribute increased cyber attacks to GenAI

-Deep Instinct 2023 report

Resources

Agentic AI in the Cignal Sandbox

In this video, Kenny introduces the Cignal Toolbox Node, providing in-browser language models the ability to execute JavaScript functionality to build agentic testing and workflow processes.

Building an AI Testing Workflow in Cignal Nodegraph

In this demonstration, Justin Tornetta shows how to use the Cignal Sandbox to build a simple testing workflow to find problems in AI systems.

Drag and Drop RAG: Instantly Improve any LLM

In this demonstration, Kenneth Brown shows how to use drag-and-drop RAG to improve LLM stability and performance.