Technology • Dyslexia
Privacy-first • Real-time • Full-stack
Product demo
Open on YouTube →
CaptureEye movement signals
AnalyzeAI feature extraction
ReportDecision-ready outputs
Architecture at a glance
| Layer | What it does | Investor value |
|---|---|---|
| Hardware | Flexible capture: standard cameras → ruggedized setups, calibration & quality checks | Lower deployment friction and wider addressable environments |
| Workflow | Browser-based task orchestration: standardized or custom protocols, session control | Repeatability across sites and faster rollouts |
| AI / Analytics | Feature extraction + pattern detection to generate real-time reports and monitoring | Actionable insights; supports continuous model improvement |
| Governance | Privacy-first design: access control, minimization mindset, secure storage | Enterprise readiness for sensitive data environments |
Token/blockchain integration is optional — the platform works fully without it.
Built to ship: scalable, privacy-first cognitive assessment infrastructure.
O’VISTA is designed as an end-to-end system: capture → orchestration → AI analytics → reporting, deployable from a single clinic to multi-site networks with consistent outputs.
Reading behavior simulator
Illustrative gaze pattern comparison on the same text (normal vs. dyslexia-like).
Normal reading
Eye movements typically progress line by line with brief fixations on words.
The gaze shows smoother forward motion and fewer regressions during fluent reading.
In guided tasks, this produces stable, repeatable metrics over time.
Dyslexia-like pattern
Eye movements typically progress line by line with brief fixations on words.
The gaze shows smoother forward motion and fewer regressions during fluent reading.
In guided tasks, this produces stable, repeatable metrics over time.
Note: This is a visual simulator for explanation only — it is not a medical representation or diagnosis.
Platform modules (expand)
Clean details for reviewers without crowding the page.
Hardware layer ▾
Device flexibilityStandard → ruggedized
CalibrationQuality checks
DeployabilityAccessible + scalable
Workflow & orchestration ▾
Browser tasksStandardized protocols
Custom experimentsWhen needed
Multi-site repeatabilityConsistent sessions
Analytics & AI ▾
Feature extractionFrom gaze behavior
Scoring & patternsDecision support
MonitoringProgress over time
Privacy & governance ▾
Access controlRole-based (deployment dependent)
MinimizationPractical defaults
Secure storageAudit-friendly design
Optional tokenization can be added later; it’s not required for platform functionality.
Integrations ▾
APIsEnterprise connectivity
Multi-siteStandardized reporting
ExtensibleNew modules over time
Questions about architecture?
We can share a deeper technical walkthrough on request.