Eigen offers AI‑powered thermal vision software that captures real‑time infrared imagery of manufacturing lines and uses machine‑learning models to detect hidden defects such as voids, adhesion failures, and material inconsistencies. The platform also provides condition‑monitoring alerts for equipment overheating or weld anomalies, running on edge hardware with cloud‑based analytics to reduce scrap, rework, and unplanned downtime without extensive rule tuning.
Funding
$2.6M 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.
Founders
Product
Problem
Manufacturers often miss early-stage defects and equipment issues because traditional visual inspection cannot detect temperature-related anomalies, and conventional machine‑vision systems require constant rule tuning when processes or products vary.
Solution
Eigen provides AI‑driven thermal vision software that captures infrared imagery of products and equipment in real time and applies machine‑learning models to identify hidden defects such as voids, adhesion failures, and material inconsistencies. By analyzing thermal patterns, the system detects quality problems and condition deviations before they become visible to standard cameras, enabling earlier intervention. The solution runs on edge hardware and streams data to a cloud platform where analytics generate actionable alerts and process insights. Users can monitor production lines continuously, reduce scrap and rework, and minimize unplanned downtime without extensive manual rule configuration.
Target Audience
Primary customers are manufacturers of high‑volume plastic, metal, adhesive, and food‑beverage products seeking inline quality inspection and equipment condition monitoring, particularly in injection molding, thermoforming, blow molding, and welding operations.
Features
- Real‑time infrared imaging integrated with AI models that learn from process variation
- Automated detection of thermal signatures associated with defects in plastics, metals, adhesives, and food‑beverage products
- Condition‑monitoring mode that flags equipment overheating or weld anomalies as they develop
- Edge‑deployed inference with cloud‑based analytics for scalable deployment across multiple lines
- Adaptive algorithms that reduce the need for manual rule updates when product or process changes occur
- Dashboard visualizations and alerts that provide actionable insights for operators and engineers