Retail

Frugo Stores

24/7 Loss Prevention using existing cameras.

Frugo Stores Case Study

How Frugo Stores turned existing CCTV into real-time product concealment detection.

Frugo Stores deployed Ojo AI on its existing security cameras to detect possible product concealment in real time. Loss prevention can review the relevant video event, verify what happened, and decide how to respond without adding new camera hardware.

Retail loss prevention
Existing cameras
Concealment detection
Customer

Frugo Stores wanted faster visibility into shoplifting events without adding more live video monitoring.

Deployment

Ojo AI was added to existing CCTV to detect possible product concealment and surface video events for review.

Result

Loss prevention gained an exception-based workflow for reviewing possible concealment events faster.

Upload Frugo Stores case study PNG
High-priority alert
Existing cameras
Faster action
The challenge

Frugo Stores needed faster visibility into shoplifting without adding more live monitoring.

Traditional CCTV is reactive and time-consuming to review. Frugo Stores needed a way to surface possible product concealment in real time so loss prevention could focus on the events most likely to matter.

Earlier detection

Frugo gained faster visibility into possible product concealment without relying on continuous live monitoring.

Exception-based review

Loss prevention can focus on flagged concealment events instead of reviewing hours of routine footage.

Uses existing cameras

Ojo AI was deployed on Frugo Stores' existing CCTV infrastructure without replacing cameras.

Faster verification

Flagged events include the video context needed for faster loss prevention review and response.

The solution

Ojo AI was added to Frugo Stores' existing camera system to detect possible product concealment, surface high-priority events, and give loss prevention faster video context for review.

Instead of replacing cameras or adding constant manual monitoring, the deployment turned existing CCTV into an exception-based loss prevention workflow.

01

Detect the event

Ojo AI monitored Frugo Stores' existing cameras for possible product concealment around merchandise.

02

Surface the event

When possible concealment was detected, Ojo AI surfaced the relevant video event for loss prevention review.

03

Verify and respond

Loss prevention could verify the event and decide whether intervention, documentation, or escalation was appropriate.

What changed

A faster loss prevention workflow built around verified video events.

The operational change was simple: monitor existing CCTV continuously, surface possible concealment, and let loss prevention review the video before taking action.

Product concealment

Ojo AI identifies possible concealment behavior around merchandise and surfaces the event for review.

High-shrink areas

High-risk shelves and departments can be monitored continuously without adding dedicated staff.

Event verification

Loss prevention reviews the video before deciding whether the event requires intervention or escalation.

Existing CCTV

The deployment uses Frugo Stores' existing security cameras, avoiding a major hardware replacement.

The outcome

Frugo Stores strengthened loss prevention without replacing its existing camera infrastructure.

Before Ojo AI

Loss prevention relies more heavily on live monitoring or post-incident video review.
Possible product concealment can be missed in busy aisles or high-shrink areas.
Video evidence often has to be found manually after an incident.
More cameras increase the amount of footage teams are expected to watch.

After Ojo AI

Possible concealment events are surfaced for review in real time.
Loss prevention receives the relevant video context for faster verification.
Teams can use exception-based review instead of watching routine footage continuously.
Frugo Stores added AI loss prevention capabilities without replacing existing cameras.
Case study summary

This Frugo Stores case study shows how Ojo AI turns existing CCTV into an AI-assisted loss prevention workflow that surfaces possible product concealment, reduces manual video review, and gives loss prevention teams faster video context when an event requires review.

FAQ

Everything you need to know

Quick answers to the questions we hear most.

How much does Ojo AI actually save?

Customers can significantly reduce monitoring costs, wasted guard time, and avoidable loss by using the cameras they already have. Because Ojo AI works on top of existing hardware, there is no large upfront rip-and-replace project, just software that can pay for itself quickly.

What cameras do you support?

Ojo AI works with existing camera environments and is designed to fit into the systems customers already have. See our Integrations page for supported platforms and deployment details.

How long does deployment take?

Ojo AI is built for fast rollout without heavy on-site lift. Because there is no new hardware to install in most deployments, teams can start with priority cameras quickly and expand from there with minimal disruption.

What happens when Ojo AI finds a real threat?

Ojo AI can trigger immediate deterrence actions such as voice warnings and lights, then route the event to the right people with clear context. If human review or response is needed, the alert can be escalated to your team, monitoring partner, or on-site security, with a full incident record included.

Will this replace our security guards?

No. Ojo AI handles the repetitive monitoring work that slows teams down, while guards and security staff stay focused on presence, judgment, customer interaction, and response.

Is this going to create more work?

No. Ojo AI is built to reduce manual footage review, cut noise, and make incidents easier to understand. Instead of wasting time scrubbing video and piecing reports together, teams get clearer alerts and a faster path to action.

Ready to see results?

Watch our Autonomous Guarding in action

© All right reserved

© All right reserved