EyeLabX provides a clinically validated digital eye‑protection platform that uses real‑time eye‑tracking analytics to measure attention load and visual fatigue, delivering adaptive micro‑break prompts and visual adjustments without disrupting workflow.
Funding
Funding not disclosed
Founders
Product
Problem
Screen-based work and entertainment cause prolonged visual focus, leading to eye strain, reduced productivity, and long-term visual fatigue for individuals and organizations.
Solution
EyeLabX offers a clinically validated digital eye protection platform that continuously measures user attention and visual fatigue through eye‑tracking analytics. The system provides real‑time feedback and adaptive interventions—such as micro‑break prompts, visual adjustments, and personalized wellness recommendations—to mitigate strain without interrupting workflow. Developers can embed the technology directly into applications, devices, or enterprise software via lightweight SDKs, enabling seamless integration into existing digital environments. Organizations can deploy the solution at scale to monitor collective attention health, generate actionable insights, and improve overall productivity while supporting employee visual well‑being.
Target Audience
Primary customers are software developers, device manufacturers, and enterprises seeking to integrate eye‑care features into their products or workflows to support professional users and remote workforces.
Features
- Real-time eye‑tracking analytics that quantify attention load and visual fatigue metrics
- Adaptive micro‑break and visual adjustment recommendations based on measured strain levels
- Clinically validated algorithms ensuring accuracy and efficacy of eye‑protection interventions
- Developer-friendly SDKs and APIs for embedding the technology into apps, devices, and enterprise workflows
- Enterprise dashboard delivering aggregated wellness insights, compliance reporting, and productivity analytics
- Scalable deployment options ranging from individual consumer use to organization‑wide implementations