Introduced as a technology preview in late 2025, Tech Soft 3D has officially launched its HOOPS AI framework, aimed at integrating CAD data into machine learning (ML) pipelines.
The product is now generally available based on a successful beta program with over 30 companies. This announcement addresses a problem that has long been a complex task for engineers in manufacturing and related fields. CAD datasets are notoriously difficult to feed into modern ML systems in a reliable manner, but HOOPS AI takes that directly on your shoulders by handling data preparation and model experimentation.
Commenting on the announcement, Gavin Bridgeman, CTO of Tech Soft 3D, said, “The official launch of HOOPS AI marks an important step at Tech Soft 3D as we lead the effort to bring AI to engineering data.”
New features, faster development cycles
The full release adds two features that weren’t in the preview. Linux support is also provided along with existing Windows compatibility. This is important considering that most ML infrastructure runs on Linux.
In addition to this, it also provides CAD embedding, a feature that automatically captures semantic relationships within CAD data without the need for human labeling. Rather than being told what to look for, the system identifies patterns on its own, allowing models to recognize similar parts and understand design context.
Teams can run hundreds or thousands of model variations simultaneously, opening the door to tasks such as part classification, metadata enrichment, manufacturing feature discovery, similarity search, duplicate detection, and design reuse and optimization across large design libraries.
The ability to iterate at this scale is intended to compress development timelines, and Tech Soft 3D says small teams can shorten cycles from months to weeks.
The roadmap will expand access to Python with a particular focus on Product Manufacturing Information (PMI). The company also plans to support training on private organizational data, a notable gap in the current version that has so far only been demonstrated on public datasets.
According to Tech Soft 3D, the long-term goal is to capture specialized engineering knowledge embedded in CAD models and make it accessible to the entire team.
CAD-ML issues
Machine learning models require standardized and predictable input structures, whereas CAD files are inherently nonlinear and context-sensitive. HOOPS AI acts as a technology translation layer between these two ecosystems, standardizing complex geometries into machine-readable formats.
This allows developers to eliminate data ingestion bottlenecks that previously prevented automated analysis of large engineering datasets.
The challenges HOOPS AI is addressing are long-standing challenges in the industry. Gian Paolo Bassi, senior vice president at Dassault Systèmes, acknowledged that even on one of the major CAD platforms, AI capabilities remain fragmented, describing a collection of narrowly focused tools that handle specific tasks without consistent orchestration across workflows.
Separately, Dassault has been working on extracting embedded engineering knowledge from shapes and historical design decisions, but that goal is still in development. That both goals are still underway at a company of Dassault’s size highlights how structurally difficult the CAD-to-ML problem is and why a purpose-built framework specifically targeting it is a meaningful development.
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The featured image shows 3D geometry processed by the HOOPS AI framework. Image via Tech Soft 3D.
