VRAI offers the HEAT platform, which captures detailed interaction data from immersive VR training—such as gaze tracking and event logs—and converts it into actionable performance insights. The system provides automatic after‑action reviews, personalized metrics, and adaptive learning recommendations via intuitive dashboards, helping defense, security, emergency services, and academic organizations improve training effectiveness and reduce repeat sessions.
Funding
$4.4M 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.
1OFounders
Product
Problem
Simulation-based training generates large amounts of performance data, but organizations often lack tools to collect, analyze, and turn that data into actionable feedback for trainees. Without systematic insight, training programs cannot efficiently identify skill gaps or personalize learning pathways, limiting improvements in human performance.
Solution
VRAI’s HEAT platform captures detailed interaction data from immersive VR training scenarios, including gaze tracking and event logs, and processes it with analytics algorithms to produce clear after‑action reviews. The system highlights what users saw, missed, and how they responded, delivering personalized performance metrics and adaptive learning recommendations. Insights are presented through intuitive dashboards that can be accessed by trainers and decision‑makers to refine curricula and monitor skill development over time. By centralizing simulation data, HEAT enables organizations to continuously improve training effectiveness while reducing the need for repeat sessions.
Target Audience
Primary customers are defense, security, and emergency‑services agencies, as well as corporate and academic institutions that run VR‑based simulation training and require data‑driven performance assessment.
Features
- Integrated VR environment with built‑in gaze‑tracking to record where trainees focus during scenarios
- Automatic after‑action review that quantifies detections, misses, and response times for each training event
- Data aggregation and analytics engine that transforms raw simulation logs into actionable performance scores
- Adaptive learning recommendations that tailor subsequent training modules to individual skill gaps
- Web‑based dashboard for trainers to visualize cohort performance, track progress, and export reports
- Cloud‑hosted architecture supporting scalable data storage and multi‑site access for distributed training programs