Foretellix Launches Data-Driven Toolchain to Accelerate Safe AI-Powered Autonomous Vehicle Development

Foretellix Boosts AI-Driven Autonomous Vehicle Development with Data-Driven Toolchain and Safety Evaluation

Foretellix, a leader in data automation for AI-powered autonomy, has expanded its Foretify platform to help Autonomous Vehicle (AV) developers cut development time by 50% and reduce costs by hundreds of millions of dollars. This update addresses critical challenges faced by AV teams as they push toward fully autonomous vehicles.

One major challenge is training AI engines efficiently by maximizing existing drive data and supplementing it with hyper-realistic synthetic data. AV developers must also ensure their systems are safe across all realistic driving scenarios. An independent, data-driven evaluation framework is essential for fleet operators, developers, and insurers to assess AV safety before public deployment.

Foretify: Training and Validation Powered by Real and Synthetic Data

Foretellix's Foretify toolchain automates the curation of training data by combining real-world driving miles with generated synthetic scenarios. It enhances sensor simulation data using NVIDIA Omniverse and Cosmos, creating highly realistic variations for large-scale AI training and testing.

This approach produces vast amounts of synthetic data, boosting the efficiency of achieving optimal operational safety within a defined Operational Design Domain (ODD) by a factor of ten. Foretify also offers a data-driven safety evaluation framework that analyzes coverage and performance across real and simulated data, providing independent evidence for system safety cases.

Backing and Industry Adoption

Foretellix is supported by top investors including Toyota's Woven Capital, Temasek, Volvo, and NVIDIA. The company's founders bring deep expertise from previous work on coverage-driven verification and validation tools at Verisity, Intel, and Cadence Design Systems.

Customers such as Torc (a Daimler Truck subsidiary), Volvo, Mazda, Woven by Toyota, and Nuro rely on Foretellix. Tilo Schwarz, VP of Engineering at Nuro Driver™, highlights Foretellix's role in validating large-scale scenarios for urban and highway environments. Similarly, Torc's CEO Peter Vaughan Schmidt emphasizes the importance of Foretellix's toolchain in rigorous scenario testing for their Level-4 autonomous trucking system.

Integrating NVIDIA Technology for Enhanced Simulation

Foretify recently integrated with NVIDIA Omniverse Blueprint for AV Simulation and the NVIDIA Cosmos Transfer World Foundation Model. This integration adds hyper-realistic sensor simulation to behavioral scenario modeling, speeding up AI training and validation for autonomous vehicles.

Zvi Greenstein, GM of Autonomous Vehicles Infrastructure at NVIDIA, explains that this combined toolchain enables scalable generation and evaluation of high-fidelity testing scenarios and training data, critical for safe AI-powered autonomy deployment.

The collaboration earned the Simulation Innovation of the Year award at the 2025 Automotive Testing Technology International Awards. Dr. Huw Davies from Coventry University points out that the ability to generate varied, high-quality synthetic data efficiently is key to addressing complex traffic scenarios and edge cases.

Conclusion

Foretellix’s Foretify platform offers AV developers a practical solution to train, validate, and evaluate AI-powered autonomous vehicle systems using a mix of real and synthetic data enhanced by advanced sensor simulation. This approach helps reduce development time and cost while providing independent safety evidence, accelerating the path to safe, scalable driverless mobility.

For IT and development professionals working on autonomous systems or AI training pipelines, Foretellix’s data-automation toolchain represents a significant step toward reliable and efficient AV development.

Learn more about AI training and development tools at Complete AI Training.