Hestus provides Sketch Helper, an autocomplete engine for CAD that predicts a designer’s next intent while sketching in 2D and offers one‑keystroke suggestions for constraints and dimensions. By analyzing current geometry and learning from prior designs, it reduces mouse clicks by up to fourfold and speeds sketch creation by up to 2.5×, helping mechanical engineers focus on design rather than repetitive CAD tasks.
Funding
$1.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
3ORYFounders
Product
Problem
Mechanical engineers spend a large portion of their time performing repetitive, manual actions in CAD tools—assigning constraints, adding dimensions, and navigating menus—to turn concepts into manufacturable designs. This “click-work” is time‑consuming, error‑prone, and reduces the time available for actual design thinking.
Solution
Hestus offers an autocomplete engine for CAD called Sketch Helper that predicts a designer’s next intent while sketching in 2D. The system analyzes the current geometry, proposes the most likely next constraint or dimension, and displays the suggestion as a red overlay. Engineers can accept the recommendation with a single keystroke, eliminating multiple clicks and reducing the risk of mistakes. By learning from prior design patterns, the tool accelerates sketch creation up to 2.5× and cuts the number of required interactions by roughly fourfold. The approach is planned to extend to full 3D modeling, assemblies, and design‑for‑manufacturing workflows.
Target Audience
Primary users are mechanical engineers and CAD designers who create part sketches and need faster, more reliable constraint and dimension placement, as well as engineering teams seeking to streamline concept‑to‑manufacturing workflows.
Features
- Real‑time intent prediction that suggests the next logical CAD operation based on the current sketch geometry
- Visual preview of suggested constraints or dimensions as an overlay before acceptance
- One‑keystroke acceptance to apply the suggestion instantly, minimizing mouse clicks
- Machine‑learning model trained on engineering design data to improve suggestion relevance over time
- Initial focus on 2D sketching with a roadmap to support 3D part modeling, assemblies, and DFM assistance