Detection of suspicious concealment behaviors or unauthorized property damage.
Identifies the act of theft through behavioral analysis — not inventory gaps. Catches the concealment moment, not the missing item.
At Argu.ai, we pioneered the shift to Vision Agents. By combining Large Language Models (LLMs) with visual encoders, we built a system that understands the world the way humans do through context and language.
Concealment Detection
Recognize the behavioral signature of shoplifting — the glance, the item pickup, the concealment motion. Alert before the individual reaches the exit.
Confidence Scoring
Every alert comes with a confidence score. Review high-confidence events first. Customize thresholds to match your LP team's workflow.
Historical Pattern Analysis
Identify repeat offenders across visits using natural language search across archived footage.
These are the core principles that define how Argu approaches every deployment. Our solutions are built to work on your existing infrastructure, require no engineering overhead, and deliver measurable results from day one. Every feature is designed with one goal: giving your team the intelligence to act faster, with more confidence, and without adding complexity.
In-Act Detection
Identifies concealment behavior before the item exits the store
Confidence Scoring
Tiered alert priority reduces LP team review burden.
Pattern Matching
Identify repeat-visit patterns across historical footage.
In-Act Detection
Identifies the concealment behavior itself — not the missing item. Catches theft at the moment it happens.
Confidence Scoring
Every alert includes a confidence percentage. Review high-confidence events first. Reduce LP team workload.
Historical Pattern Matching
Search archived footage for repeat visit patterns using natural language queries.
No Exit Scanner Required
Detection happens at the aisle, not the door. More time to intervene before the individual leaves.
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