SightGuard Vision
Transforms video and sensor inputs into structured physical events with timestamps, confidence, and model metadata.
AI-powered visual intelligence
SightGuard turns camera and sensor activity into explainable physical events, then connects them with business systems to reveal what needs attention.
One visual intelligence platform
SightGuard separates observation, context, risk, and human judgment, so teams can understand why something was flagged and what evidence supports the review.
Transforms video and sensor inputs into structured physical events with timestamps, confidence, and model metadata.
Processes activity close to the source, supporting privacy, offline operation, and future live camera connectivity.
Brings events, evidence, risk, investigations, rules, and audit history into one operational workspace.
Normalizes context from ERP, POS, WMS, access-control, and other digital systems without coupling the platform to one vendor.
See. Understand. Verify. Protect.
SightGuard is designed to connect observable facts with operational context, not to make unsupported claims about intent.
First solution ยท SightGuard Retail
The SightGuard Retail Early Access Program focuses on recorded retail CCTV and Odoo-exported data. The goal is to surface reviewable exceptions, not declare wrongdoing.
14:32:14BUSINESSCard payment completedMatched14:32:16CAMERADrawer openedTrigger14:32:31SYSTEMNo authorized cash eventExceptionResponsible by design
SightGuard keeps perception separate from conclusions and preserves the evidence, rule version, score, and review history behind every investigation.
Computer vision reports observable activity. Authorized investigators determine outcomes using evidence and policy.
The long-term edge architecture keeps continuous CCTV on customer infrastructure and sends selected evidence when needed.
Meaningful changes, rules, confidence, scores, reviewer actions, and timestamps remain auditable.
Early access
We are working with organizations that want to connect physical activity with operational context and build a practical, evidence-led workflow around the moments that need attention.