Engineering-Specific Accuracy: Unlike general AI, Leo uses a Large Mechanical Model (LMM) trained on 1M+ technical sources (standards, textbooks, datasheets). It also provides citations, so we don’t have to guess whether a material property or tolerance is correct. CAD & PDM Awareness: It understands your actual geometry. You can search your company’s internal library in natural language, or find standard vendor parts based on engineering intent rather than relying on part numbers. Traceable Math: It handles complex mechanical calculations (stress, thermal, fluid) and often shares the Python-based logic behind the result, which makes it easier to verify and include in technical reports. Efficiency Gains: It consistently saves a lot of time by automating repetitive documentation, part hunting, and early concept generation.

The parts search across our PDM system has been a game changer — engineers find components much faster instead of pinging each other constantly. It integrates seamlessly with our existing PLM setup, so it's not another orphaned tool. The technical Q&A feature pulls from real engineering standards with source citations, giving engineers confidence they're getting accurate, relevant answers. It has noticeably cut weekly meeting load and reduced the number of "can you find X spec" emails significantly. The ROI is clear when you consider how much time senior engineers were spending on retrieval tasks.

I like how Leo is convenient, quick, and accurate. It really boosts my productivity. For example, when I had to build a machine for my company, it helped me do it 10 times faster. The initial setup was also really easy, taking one day at most.