Your team focuses on customers, ShrinkHalt AI + human reviewers handle loss prevention.
Skeleton‑based detection runs on affordable edge devices. Flagged events are verified by remote loss‑prevention specialists. Zero distraction for floor staff.
Retail shrinkage keeps growing, but most surveillance footage is never reviewed.
Store teams either ignore suspicious activity or waste hours watching cameras — time that should be spent on service and selling. Traditional AI alone generates too many false alarms.
$112B+ annual shrinkage
Globally, retail shrinkage exceeds $100 billion annually. The majority of incidents happen in plain sight, yet real‑time capture remains elusive with legacy methods.
Less than 2% of theft events flagged
Standard CCTV review is reactive and understaffed. By the time footage is reviewed, the opportunity for intervention has passed.
Associates lose hours on LP tasks
Manually tracking suspicious behaviour pulls staff away from the sales floor, hurting customer experience and revenue.
Automation built for loss prevention, not more screen time.
Our lightweight AI filters 95%+ of routine floor activity. Only potential theft events reach our remote human reviewers. Your team receives verified, actionable alerts.
Skeleton‑based AI detection
Compact Temporal Convolutional Network analyzes pose sequences (no raw video) to classify actions into four operational classes: Floor activity · Consumption · Concealment in bag · On‑body concealment. Runs entirely on edge devices.
Remote human validation
When the AI flags a potential incident, a trained remote loss‑prevention reviewer inspects the short clip. Only confirmed events generate an alert — eliminating false positives.
Real‑time verified alerts
Store staff receive discreet, verified notifications via mobile or store system. Because every alert is human‑confirmed, your team can respond with confidence.
Edge AI + remote reviewer pipeline
Video processing stays local on low‑cost edge hardware. When the model detects suspicious motion, relevant clips are securely sent to our remote review team for verification — no continuous cloud upload.
A different kind of loss prevention platform
🎯 Skeleton‑based, not pixel‑dependent
Robust to lighting changes, shadows, and background clutter. Tracks key body joints, extracting motion patterns rather than skin or objects.
🌀 View‑adaptive AI
Feature‑wise Linear Modulation conditions the model on real‑time viewpoint metadata, allowing adaptation to different camera placements without retraining.
✅ Hybrid AI + human accountability
The AI acts as a high‑precision attention filter; every critical decision involves a remote loss prevention expert. No unsafe escalations.
📦 Sub‑1M parameter edge model
Extremely lightweight TCN architecture runs on affordable edge devices. Pose extraction is the main workload — classifier adds negligible overhead.
Edge‑native architecture · 0.9661 weighted F1
The core AI is a Temporal Convolutional Network (sub‑1M parameters) with view‑adaptive conditioning, achieving 0.9661 weighted F1 on a challenging held‑out test set (27.5k real‑world pose sequences). Feature engineering uses skeleton‑based kinematics, enabling robust edge deployment on hardware like Jetson Orin Nano.
Four operational macro‑classes: Floor Activity, Consumption, Concealment in Bag, On‑Body Concealment. Optimized for loss prevention triage.
How ShrinkHalt delivers verified alerts
Read & detect
Existing cameras feed into an edge device that extracts pose data. The AI continuously analyzes motion patterns and flags suspicious actions.
AI → remote review queue
Only potential concealment or consumption events are sent to our remote loss prevention team. Non‑critical floor activity is discarded immediately.
Human verification → store alert
Reviewer validates the event within seconds. Upon confirmation, a real‑time alert (with anonymised clip or metadata) reaches your store staff discreetly.
Seamless fit for existing retail environments
Event‑driven clip transmission only when the AI detects suspicious activity — minimal bandwidth usage.