Rescale Launches Data Intelligence to Unify Engineering Data and Accelerate R&D

October 28, 2025
Rescale has introduced Data Intelligence, a new extension of its digital engineering platform designed to unify simulation and R&D data for faster product development and AI-driven insights.

Rescale has launched Rescale Data Intelligence, an expansion of its digital engineering platform that unifies R&D data to improve efficiency and accelerate innovation, announced in a press release.

The new offering integrates modeling and simulation data into a unified data fabric that automatically captures metadata, synchronizes with external systems, and maintains a traceable digital thread. This structure enables engineers to access legacy and new data in one place, reducing onboarding time and preventing knowledge loss.

Rescale Data Intelligence also includes agentic automation features that orchestrate and automate workflows, converting data from legacy systems into actionable outcomes. The platform’s AI capabilities power tools like Rescale Assistant, which allows engineers to query data using natural language for cross-disciplinary analysis.

Additionally, the company introduced its AI Physics OS, which uses simulation data to train and validate proprietary AI surrogate models. This system supports faster physics calculations and enhances digital twin capabilities across product lifecycles. Rescale Data Intelligence is available now for organizations seeking to build unified R&D data foundations.

We hope you enjoyed this article.

Consider subscribing to one of our newsletters like Industrial AI Weekly or Daily AI Brief.

Also, consider following us on social media:

AI Brief AI Brief (X)

Subscribe to Industrial AI Weekly

The latest advancements in smart manufacturing, predictive maintenance, AI-driven quality control, supply chain & more.

AI Token Webinar

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more

You May Also Like