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Software for Trustworthy AI

Meet Cignal Engine: a data-rich environment to safely exercise and test artificial intelligence models for security applications. 

Use Cases

The patented Cignal Engine* is designed to help scientists, engineers, and testers accelerate time-to-market, optimize performance, and reduce uncertainty for AI systems that rely on hard-to-acquire data

Graphic of a suitcase

Digital Twins

Create digital clones of people, objects, and scenarios without security friction

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Auto-Annotation

Generate 2D and 3D labels and annotations automatically for image datasets

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Data Management

Reduce space, cost, and delays with lightweight Cignal Engine Extracting Datasets (CEEDs) 

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Validation & Test

Verify safe and trustworthy AI using synthetic, multi-modal image streams

Cignal Engine

The Cignal Engine vision AI platform supports the full AI development lifecycle -

from initial concept, to testing, and final deployment.

Innovation Snapshot

Cignal Engine incorporates a number of innovations to improve the accuracy, performance, and safety of machine vision AI.

Infinite Objects

Infinite surface, shape, and material perturbation for continuous feature activation.

Cignal Echo

Create single-snapshot clones of imaging hardware for high-fidelity data generation.

CEED

Create massive datasets on-demand with Cignal Engine Extracting Datasets (CEEDs).

Full Spectrum Imagery 

X-ray, gamma ray, infrared, and millimeter wave are a few examples of the wavelengths found in advanced security, inspection, screening, and defense use cases.

Traditionally, acquiring training or test data for these spectra is time-consuming and expensive, and it can easily exhaust memory, compute, and storage budgets.

Cignal's innovative approach allows these non-visible datasets to be created, shared, and stored faster, with fewer limitations, and at a greatly reduced cost.

Services

Homeland security and national security projects are sensitive, complex, and need massive amounts of data.  Cignal specializes in designing and developing scalable, data-driven solutions to solve the most difficult challenges.

AI Strategy

Research, develop, and deploy performant AI systems.

AI Testing

Implement testing strategies that minimize risk.

Data Management

Design cost-effective, scalable, AI-centric architectures.

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