AI Shoplifting Prevention Software | Global POS-Video Loss…

The Gap

Why Traditional CCTV Can't Prevent Shoplifting

Ordinary CCTV is a recording system, not a prevention system. Footage is reviewed after a loss is discovered, never as it happens. (Cost and split figures below are illustrative global industry ranges drawn from NRF, ECR Retail Loss, and regional analyses.)

No Real-Time Alerts

Footage is reviewed after a loss is discovered, not as it happens. By the time the inventory variance shows up at month-end, the goods, the receipt, and the cashier's memory are all long gone.

No POS Integration

Can't correlate video with transactions, so checkout fraud stays invisible. Sweethearting and scan-mismatch leave no signature in the cameras or the POS log alone — only the correlation between them shows the gap.

No Behavioural Intelligence

Doesn't understand what constitutes theft, so nothing is flagged. The camera doesn't know the difference between a customer browsing and a customer concealing — neither does the recorder.

Manual Review Doesn't Scale

Someone watches hours of footage to find a 30-second incident — and that's for one store. For a 40-store chain, this approach is mathematically impossible to keep up with, so most incidents are never reviewed at all.

Typical Shrinkage Breakdown — Where the Loss Actually Comes From

External shoplifting35–40%

Internal / employee theft25–30%

Billing fraud (sweethearting, scan mismatch)15–20%

Administrative / process error10–15%

A tool that targets only shoplifters misses most of the loss. Self-checkout expansion (Walmart, Tesco, Carrefour, Aldi globally; Reliance Smart, DMart Ready, Decathlon in India), more sophisticated organized retail crime, and high retail staff turnover (often cited at 60–100% in mature markets, 150–200% in emerging markets) all push shrink higher — and multi-store chains can't physically audit every location every week.

Definition

What is AI Shoplifting Prevention Software?

AI shoplifting prevention software is a video-analytics platform that integrates with a retail store's existing CCTV cameras and POS systems to detect theft patterns — including sweethearting, scan mismatch, product concealment, barcode manipulation, refund abuse, and employee fraud — in real time, alerting store managers instantly with video-linked evidence, and flagging every event for human review with full context.

CCTV records; shoplifting prevention software detects, alerts, and provides evidence — in the moment, not after the loss is discovered at month-end inventory.

Loss prevention, not retail analytics — the buyer distinction

This is loss prevention, not retail analytics, and the difference matters because they're sold to different buyers solving different problems. Retail video analytics helps you understand customer behaviour — footfall, heatmaps, dwell time, conversion — to sell more. Shoplifting prevention targets loss — theft detection, billing fraud, shrinkage reduction — to lose less. Both run on Tentovision as separate modules on the same AI video analytics platform, for different teams. Loss-prevention managers buy this page; store operations and marketing buy that one.

POS-video integration is the moat

Most competitors list "POS integration" as a bullet. It is the mechanism that catches checkout fraud which pure video or pure POS data each miss — sweethearting transactions look legitimate to the POS log alone (a complete-looking receipt), and an item passing the till looks normal to the camera alone (just a hand). Only the correlation between camera and POS exposes the gap. The 5-step workflow in the next section walks through exactly how that correlation happens.

Honest scope — what this is and isn't

The software detects, alerts, and evidences theft. It does not claim to stop 100% of it — false positives are inherent to any AI vision system, and the store manager always makes the final call to act on an alert. Detections are explicitly flagged for human review with full context, not treated as automated accusation. Used with clear policies and lawful, DPDP/GDPR-aligned data handling, this protects honest staff from false positives as much as it catches genuine fraud — and that hygiene is part of why CFOs and HR sign off on it.

Global deployment — POS-agnostic, camera-agnostic, multi-region hosting

Designed for the global retail POS stack — NCR Voyix, Oracle Retail/MICROS, SAP Retail, Toshiba TCx, Lightspeed — plus the India POS stack widely used across Indian retail (Ginesys, Gofrugal, Marg ERP, Logic ERP), and custom POS via API. Vendor-agnostic across 100+ camera brands over ONVIF/RTSP. Multi-region data residency on Microsoft Azure — India (Mumbai/Pune), Singapore, EU (Dublin/Frankfurt), US (East/West) per customer choice. Pricing is per store, regionally calibrated.

Diagram — Loss Prevention (lose less) vs
Retail Analytics (sell more) — same platform Loss prevention detects theft and fraud while retail analytics measures footfall and conversion on the same Tentovision platform

Loss prevention detects theft and fraud while retail analytics measures footfall and conversion on the same Tentovision platform

How It Works

How POS-Video Integration Prevents Theft

Most competitors list "POS integration" as a bullet. Here's the mechanism that is the moat — five steps from camera to evidence package, in real time. (Timings illustrative; verify against scope.)

Camera Positioning

Overhead cameras at each checkout capture the billing zone — items on the counter, past the scanner, into bags.

existing cameras

POS Data Feed

Connects to your POS — NCR Voyix, Oracle Retail/MICROS, SAP Retail, Toshiba TCx, Lightspeed; Ginesys, Gofrugal, Marg, Logic ERP for India; or custom API. Every scan, void, cancel, override — in real time.

real-time stream

AI Correlation

Compares what the camera sees with what the POS records. Item past without a scan — or scan without an item — is flagged.

~seconds (verify)

Instant Alert

Manager gets a real-time alert with clip + POS data to review and act now — routable to the command-centre platform.

within SLA

Evidence Package

Timestamped clip, POS log, item details, cashier ID — for LP, HR, or legal, retained on multi-region Azure (your chosen jurisdiction).

automatic

Typical full deployment runs about 2–3 weeks per store (verify against scope) for full POS-video integration, connecting your existing cameras via the Cloud Adapter where needed — and stores can be rolled out in phases, starting with the highest-shrink locations.

Capabilities

Six Capabilities That Turn the Mechanism Into a Working LP Toolkit

Five capabilities map to the five workflow steps — plus a sixth that ties them all together into the evidence package LP, HR, and legal teams actually use.

POS = 8 items
Camera = 11 items · flagged for review POS-video transaction validation comparing scanned items to items physically passing the counter

POS-video transaction validation comparing scanned items to items physically passing the counter

Step 1 · Validate

Real-Time POS-Video Transaction Validation

Every transaction validated against the camera feed at the till. Scan-count vs item-count comparison; price-override monitoring; void and cancellation flagging with the corresponding video clip. When POS says 8 items and the camera counts 11, it's flagged for review — the manager decides what to do. Powered by Tentovision's correlation engine, built into the platform.

AI Analytics

Item past, no scan event
The sweethearting signature AI scan-mismatch detection flagging an unscanned item at the till

Step 2 · Detect

AI-Powered Scan-Mismatch Detection

The capability that catches sweethearting — the most common and hardest-to-detect retail fraud. Items passing the scanner with no scan event; multiple items scanned as one (bundling fraud); weight-discrepancy flags where a scale exists; category-vs-appearance mismatch (small-item barcode scanned for a large-item appearance). Because a sweethearting transaction still processes and looks legitimate to the POS log alone, only this scan-versus-movement correlation makes it visible.

AI Analytics

Self-checkout fraud
Non-scan, substitution, walk-away Self-checkout fraud prevention detecting a skipped scan at a kiosk

Step 3 · Watch Kiosks

Self-Checkout Fraud Prevention

Self-checkout often shows higher shrink than manned lanes — this makes the kiosk accountable. Non-scan detection at kiosks; walk-away-without-payment alerts; item substitution (scan cheap, bag expensive); multi-item grab detection. Critical as Walmart, Tesco, Carrefour, and Aldi globally — plus Reliance Smart, DMart Ready, and Decathlon in India — expand kiosks; the lane that's least supervised becomes the lane that loses most without this.

Retail

Rank where to look
Not who to punish — human-reviewed Cashier risk-scoring dashboard directing loss-prevention review, human-verified

Step 4 · Alert

Employee Theft & Sweethearting Detection

Internal theft handled fairly — flag for review, never auto-accuse. Cashier risk scoring to direct LP attention, reviewed by a human; cash-drawer-open-without-transaction flags; after-hours POS activity; void and refund abuse patterns. The system ranks where to look, not who to punish — DPDP & GDPR aligned, lawful-basis processing, protects honest staff from false positives as much as it catches genuine fraud.

Command Center

40 stores · one screen
Which stores lose most Multi-store loss-prevention dashboard ranking shrink by store and cashier

Step 5 · Estate View

Multi-Store Loss-Prevention Dashboard

A 40-store chain needs to know which stores lose most, which cashiers are highest-risk, and which time windows see the most incidents. Shrink comparison across all stores; store / zone / cashier drill-down; weekly and monthly trend with automated LP exception reports across all sites; optional face recognition for repeat-offender flagging — consent and lawful-basis gated, human-reviewed.

Multi-Site

Evidence package
Timestamped audit trail Video-linked evidence package with timestamped clip, POS log, item details, and cashier ID for compliance review

Video-linked evidence package with timestamped clip, POS log, item details, and cashier ID for compliance review

Cross-Cutting · 06

Video-Linked Evidence & Audit Trail

The documentation layer that makes everything else defensible. Every flagged event produces a complete evidence package — timestamped video clip, POS transaction log, item details, cashier ID, operator review notes — retained on multi-region Microsoft Azure (India · Singapore · EU · US) with AES-256 at rest under DPDP and GDPR-aligned governance. The package is acceptable for internal LP review, HR proceedings, insurance claims, and legal action — and just as importantly, it protects the company from unsupported accusations by establishing exactly what was observed and when.

Cloud VMS

The five capabilities run continuously across every checkout; the sixth makes every flagged event reviewable, defensible, and DPDP-compliant. Start with a free shrinkage assessment on your highest-shrink stores.

POS-Agnostic · Global & Regional

Integrates With the POS Systems Retail Already Runs

The POS-video correlation is only useful if it connects to your POS. Tentovision is designed for the global retail POS stack plus the regional systems common in India and other emerging markets — with a custom API for the rest. (Confirm the specific integrations you need at assessment.)

NCR Voyix

Global POS

Oracle Retail

Enterprise · MICROS

SAP Retail

Enterprise

Toshiba TCx

Global Retail

India POS Stack

Ginesys · Gofrugal · Marg · Logic

Custom API

Any POS

The integration receives scan events, voids, cancellations, and price overrides in real time so they can be correlated with video at the checkout — across global retail estates and regional deployments alike. Lightspeed, Square, Shopify POS, and other modern cloud POS are supported via custom API. POS integration scope is confirmed during the assessment phase.

Works With the Cameras You Already Own

No rip-and-replace · 100+ camera brands supported

Tentovision is vendor-agnostic over ONVIF, RTSP, and standard NVR APIs — works with your existing mixed-brand checkout cameras. For tills that need a specific overhead angle, a camera may be added selectively.

HikvisionWorld #1

DahuaGlobal Top 2

CP PlusIndian Leader

UniviewTier-1 China

Sparsh CCTVIndian OEM

HiFocusIndian Brand

HikvisionWorld #1

DahuaGlobal Top 2

CP PlusIndian Leader

UniviewTier-1 China

Sparsh CCTVIndian OEM

HiFocusIndian Brand

AxisSwedish

VivotekTaiwanese

BoschGerman

PelcoAmerican

HoneywellAmerican

TiandyChinese

AxisSwedish

VivotekTaiwanese

BoschGerman

PelcoAmerican

HoneywellAmerican

TiandyChinese

The Central Technical Argument

Why POS-Video Integration Is the Moat

Pure video misses billing fraud. Pure POS data misses physical theft. Only the correlation between them catches sweethearting and scan-mismatch — the hardest-to-detect retail fraud.

The Mechanism

Camera alone, POS alone, and the gap only the correlation exposes

A sweethearting transaction is, by design, the hardest fraud to detect — because to the POS log alone, it looks like a complete, legitimate transaction with a valid receipt. To the camera alone, it looks like a normal queue at a normal till. The only place the gap is visible is in the correlation between them. That's why "POS integration" as a bullet line on a competitor's page doesn't tell you whether the actual mechanism is there.

Pure video misses billing fraud. The camera sees a hand pass items over a counter. It doesn't know how many of them got scanned versus pocketed against a friend's tab — that detail lives only in the POS log.
Pure POS data misses physical theft. The POS log sees a clean transaction. It doesn't know that two items physically passed the till for every one scanned — that detail lives only in the camera feed.
Only the correlation exposes sweethearting. POS says 8 items, camera counts 11. That delta is the signature. Without the correlation, the loss is invisible until inventory variance shows up at month-end — by which point recovery is gone.
It also catches the related family of frauds. Bundling (multiple items scanned as one), category-vs-appearance mismatch (small-item barcode scanned for a large item), weight discrepancies where a scale exists, and void/refund abuse correlated with the corresponding till video — every one of these depends on the same POS-video link.
And it directly fuels the evidence package. Because each flag carries both sides — POS entry plus video clip plus cashier ID — the documentation needed for HR or legal review is automatic. The mechanism produces the evidence as a by-product of the detection.

Tentovision's correlation engine is built into the platform, not a third-party layer on top. POS-side integrations are designed out of the box for the global retail stack — NCR Voyix, Oracle Retail/MICROS, SAP Retail, Toshiba TCx, Lightspeed — and for the India POS stack (Ginesys, Gofrugal, Marg, Logic ERP), plus custom POS via API.

Feature image — POS-video correlation diagram
(camera feed + POS log → mismatch flagged)

POS-video correlation diagram showing camera item-count compared to POS scan-count with the mismatch highlighted

Theft Taxonomy

10 Shoplifting & Billing Fraud Patterns Tentovision Detects

Loss has a finite set of methods. All ten patterns below are detected through the POS-video correlation and physical-behaviour analytics, and every flag is sent for human review with linked evidence — never treated as automatic guilt.

Sweethearting

Cashier deliberately doesn't scan items for friends/family — POS-video item-count comparison flags it.

Self-checkout non-scan

Customer bags items without scanning. Object-to-bag tracking and scan-confirmation flag it.

Barcode swap / price switch

Cheap barcode on expensive item — scanned-barcode category checked against item's visual appearance.

Product swap inside store

Expensive item taken out of packaging, cheaper variant put back. Shelf-to-checkout tracking + packaging integrity flags it.

Concealed items at billing

Pays for some, hides others in bags or clothing. Entry-vs-exit comparison and bag analytics flag it.

Fake returns / refund abuse

Returns without valid purchase, or reused receipts. Return-counter video + POS refund correlation flags it.

Employee-assisted theft

Void abuse, manual overrides, cash-drawer manipulation. Surfaced for human LP review, never auto-accusation.

Cart/bag/stroller hiding

Items under cart or in personal bags missed at billing. Multi-angle checkout cameras + count validation.

Distraction billing

Customer distracts cashier during fast billing — billing-speed-vs-item-count anomaly flags it.

Repeat small-value theft

Cross-visit pattern recognition + optional face recognition for repeat-offender flagging (consent-gated, human-reviewed).

For vehicle-borne organized retail crime, licence-plate recognition can link a getaway vehicle as supporting evidence. All face-recognition and behavioural-pattern features are explicitly DPDP & GDPR aligned, consent-based, lawful-basis processed, and human-reviewed — this is not biometric watchlisting.

Feature image — 10-pattern theft taxonomy
(grouped by detection method) 10-pattern theft taxonomy diagram grouped by detection method (POS-video correlation, physical behaviour, cross-visit pattern)

10-pattern theft taxonomy diagram grouped by detection method (POS-video correlation, physical behaviour, cross-visit pattern)

The Comparison

Traditional LP vs Tentovision AI LP

Not "audits and store-walks are bad" — but for catching checkout fraud and tracking shrink across many stores, AI-powered POS-video integration outperforms manual methods on every axis. (All figures illustrative ranges.)

Dimension	Traditional LP (CCTV + Manual Audits + Store-Walks)	Tentovision AI Loss Prevention
Detection speed	Discovered at month-end inventory	Flagged in seconds at the till
Checkout fraud catch	Sweethearting/scan-mismatch invisible	POS-video correlation exposes the gap
Multi-store visibility	Store-by-store, manual rollup	One dashboard, every location ranked
Coverage of cameras	A few feeds reviewed manually	All checkouts, all the time
Evidence quality	Memory + partial footage	Timestamped clip + POS log + cashier ID package
Scalability	Each store needs its own LP visit	Estate-wide dashboard scales without proportional cost
Fairness for staff	Anecdotal suspicion + uneven enforcement	Risk-scored direction, human-reviewed, lawful basis
Self-checkout coverage	Effectively unsupervised	Kiosks made as accountable as manned lanes

Honest scope note: AI loss prevention does not claim to stop 100% of theft — false positives are inherent to any AI vision system, and the store manager always makes the final call to act on an alert. What it does is make checkout fraud visible, give multi-store chains an estate-wide view they cannot otherwise have, and produce defensible, DPDP-compliant evidence packages that traditional methods cannot. Used with clear policies, it protects honest staff from false positives as much as it catches genuine fraud.

Why Tentosoft

Why Choose Tentovision for Shoplifting Prevention

The category has capable global players, so the honest question is fit. Here's where Tentovision is genuinely different for global retail estates with strong regional depth — including the India POS stack and multi-region data residency that most global LP vendors don't cover natively.

AI-First, Built-In Correlation Engine

The POS-video correlation engine is built into the Tentovision platform — not a third-party analytics layer bolted on top. The mechanism that catches sweethearting is core, not an add-on.

Works With Your Cameras

Vendor-agnostic across 100+ camera brands over ONVIF/RTSP — no proprietary hardware lock-in, no rip-and-replace. For tills that need a specific overhead angle, a camera may be added selectively only where missing.

POS-Agnostic · Global & Regional

Designed for NCR Voyix, Oracle Retail/MICROS, SAP Retail, Toshiba TCx, and Lightspeed globally — plus the India POS stack (Ginesys, Gofrugal, Marg, Logic ERP) and custom POS via API. Out of the box for retail, not an integration the LP buyer has to drive themselves.

Multi-Region Data Residency

Customer-choice hosting on Microsoft Azure — India (Mumbai/Pune), Singapore, EU (Dublin/Frankfurt), US (East/West) — keeps evidence packages, video clips, and shrink analytics in your chosen jurisdiction. DPDP Act 2023, GDPR, and regional retail-data regulations aligned. Critical for HR and legal admissibility of the evidence.

Camera Health Monitoring Included

Built-in camera health and downtime prevention mean the checkout cameras the LP system depends on don't silently fail. No "we couldn't catch the fraud because the till camera was offline" excuse.

One Platform: LP & Retail Analytics

Loss prevention runs on the same Tentovision platform as retail video analytics — sell-more and lose-less for the same store, on the same cameras. Add footfall, heatmaps, or repeat-customer analytics later without a second deployment.

The Complete Suite

Tentovision AI Video Analytics — Loss Prevention & Business Intelligence

Each sibling page covers one analytics use-case on the same platform. This page is the loss-prevention option — designed to help you lose less. The retail-analytics page covers customer-behaviour intelligence to help you sell more.

AI Video Analytics Parent Platform

The umbrella platform — all analytics use-cases, one camera infrastructure.

Retail Video Analytics Sell More

Footfall, heatmaps, dwell time, conversion — customer-behaviour intelligence for store ops and marketing.

People Counting System

Entry/exit count and zone footfall — also powers the LP entry-vs-exit comparison.

Face Recognition

Optional repeat-offender flagging for LP — consent-gated, lawful-basis, human-reviewed.

Shoplifting Prevention You Are Here · Lose Less

The loss-prevention module — POS-video correlation detects theft and billing fraud in real time, flags every event for human review with linked evidence, and gives multi-store chains an estate-wide shrink dashboard. Same platform as the analytics modules above; different buyer, different problem.

Loss prevention and retail analytics serve different teams with different questions. Both run on Tentovision, on the same cameras you already own.

Retail Formats

Shoplifting Prevention Across Retail Formats — Worldwide

Each format has its own theft modes — and the same POS-video mechanism adapts to each, across global retail estates and regional deployments. (Client names below are enterprise references, consent-gated; confirm written permission per name before publishing.)

Grocery & Supermarkets

High volume, low-value items, self-checkout fraud, repeat small-value theft. Scan-mismatch at the till is the dominant pattern. (e.g. Carryfresh.) See retail.

Fashion & Apparel

Fitting-room concealment, tag switching, organized boosting groups targeting premium SKUs. Entry-vs-exit count and shelf-to-checkout tracking are the key capabilities. (e.g. Naidu Hall.)

Electronics & Appliance

High-value items, barcode swapping (cheap barcode on premium SKU), return fraud with reused receipts. Category-vs-appearance mismatch is the high-value catch.

Jewelry

Display-case theft, distraction theft during high-touch consultations, employee collusion. Risk-scored cashier review and after-hours POS monitoring are most relevant. (e.g. Akshaya Gold.)

Pharmacy & Health

OTC-medication theft, prescription and refund fraud, controlled-substance accountability. Cross-visit pattern recognition and dispense-vs-POS reconciliation are key.

Quick-Service Restaurants

Cashier skimming, order manipulation, employee-meals fraud, void/refund abuse. POS-exception monitoring with linked counter video is the core LP loop. (e.g. Bubble Organic.)

Hypermarkets & Multi-Format Stores

The highest-shrink format because all the patterns above happen simultaneously under one roof — grocery aisles plus electronics plus apparel plus self-checkout plus QSR food court. A Walmart, Carrefour, Tesco, Costco, DMart, Reliance Smart, or Spencer's-scale store needs the full capability stack at once: POS-video correlation across many tills, multi-zone behavioural analytics, kiosk fraud prevention, and a multi-store dashboard that benchmarks dozens to hundreds of locations across regions. This is where the platform argument matters most — separate point tools for each format don't aggregate.

CUSTOMER STORIES

Real deployments. Verified results.

From data centres to factories to hospitals to malls — real clients, real problems, real outcomes across India.

CASE 1 OF 6

CASE 01

ETA Group Retail · Smart security

MALL SECURITY ANPR INTRUSION AI

How ETA Group enhanced mall security and crowd management with AI

Manual monitoring led to blind spots, congested parking, and delayed incident response in a high-footfall environment.

ANPR

Auto vehicle tracking

Real‑time

Intrusion alerts

Live

Crowd analytics

Vehicle entry trackingAutomated

Mall-wide visibilitySingle dashboard

Vehicle automation100%

Crowd visibilityReal-time

Response time improvementFaster

“Tentovision’s AI analytics gave our team instant visibility into vehicles, crowd movement, and intrusions.”

— Security Operations Team, ETA Group

CASE 02

City Centre Mall premium retail environment

City Centre Mall Retail · Smart analytics

PEOPLE COUNTING PARKING ANALYTICS ANPR

How City Centre Mall optimised footfall and parking with AI analytics

IR-based counters gave inaccurate footfall data. Manual parking caused congestion. AI replaced both — and unlocked heatmap insights for retailers.

98%

Footfall accuracy

40%

Parking congestion cut

70%

Faster response

Heatmap zone analyticsActive

Blacklist vehicle detectionAuto-flagged

Footfall counting accuracy98%

Parking congestion reduction40%

Response time improvement70%

“TentoVision gave us visibility we never had before. We now run the mall like a data-driven business.”

— Mall Operations, City Centre Mall

CASE 03

Nippon Paint India Manufacturing · Edge AI

FIRE DETECTION SMOKE DETECTION PPE DETECTION

How Nippon Paint deployed edge AI for fire, smoke and PPE detection

A 2024 plant fire melted local DVR/NVR units. Tentosoft deployed on-premise edge AI for fire, smoke and PPE detection — with off-site backup.

<5s

Fire detection

Edge AI

On-premise

100%

Off-site backup

Detection layers deployedFire + Smoke + PPE

Footage survives hardware lossAlways

Fire detection speed<5 sec

Critical footage recovery100%

Audit & insurance readinessCompliant

“The fire destroyed our local recorders, but the centralized system saved all the footage. We’ve fully transitioned to AI-based safety.”

— Head of Safety Operations, Nippon Paint India

CASE 04

Narayana Health Healthcare · Multi-site CCTV

MULTI-SITE CCTV HEALTHCARE BRAND-AGNOSTIC

How Narayana Health unified CCTV across multi-brand hospital infrastructure

Multiple hospitals, different camera brands, no central view. One platform brought them together — without replacing hardware.

Multi‑site

Live monitoring

Any brand

Camera + NVR

Role‑based

Secure access

Hardware replacements neededZero

On-demand camera accessAny device

Multi-brand compatibility100%

Bandwidth optimisation60%+

Incident response speedInstant

“Tentovision gave us centralized live monitoring with controlled user access and efficient bandwidth usage.”

— Operations Team, Narayana Health

CASE 05

NIFT Chennai National Institute of Fashion Technology

CLOUD VMS EDUCATION ZERO HARDWARE

How NIFT Chennai eliminated local hardware with full cloud surveillance

DVR failures meant permanent footage loss. Cloud migration removed all local hardware and gave teams remote access from anywhere.

Hardware failures

100%

Cloud migration

Footage lost

Hardware maintenance burdenEliminated

Remote accessAnywhere, any device

Cloud uptime99.9%

Hardware dependency reduction100%

Storage scalabilityUnlimited

“All footage is safe on the cloud and our team can access it from anywhere. We’ll never go back to local hardware.”

— Head of Administration, NIFT Chennai

Theft patterns detected

POS-Video

Correlation engine

5+(verify)

POS integrations

100+(verify)

Camera brands

2–3 wks(verify)

Per-store deploy

Multi-Store

LP dashboard

Compliance & Governance

Shoplifting Prevention Governance & Fairness

A loss-prevention system that monitors staff behaviour and customer interactions sits at the centre of privacy and data-protection scrutiny across DPDP, GDPR, and regional retail-data regulations. The governance frame is part of the product — and what makes the evidence usable in HR and legal settings worldwide.

DPDP & GDPR Aligned

Retail LP video, POS telemetry, and cashier-behaviour data governed under India DPDP Act 2023 and EU GDPR — plus regional retail-data regulations for cross-jurisdictional retail estates.

ISO 27001 Aligned

Information-security practices mapped to ISO/IEC 27001 controls for retail data handling.

SOC 2 Aligned

Operational controls mapped to SOC 2 Type II principles for security & availability.

VAPT Aligned

Internal vulnerability assessment and penetration testing for the LP platform and integrations.

Multi-Region Azure Hosting

India (Mumbai/Pune), Singapore, EU (Dublin/Frankfurt), and US (East/West) regions for evidence-package data residency by customer choice. AES-256 at rest; encrypted transit.

Lawful-Basis Employee Monitoring

Cashier risk-scoring and POS-exception review operated under clear lawful-basis processing, with worker notice and consent frameworks aligned to applicable employment-law guidance per jurisdiction (India, EU, Singapore, US).

Human-Reviewed Detection

Every alert is flagged for human review with full context — the system directs LP attention; humans make the call. No automated accusation or punitive action.

Per-Project Attestation

Formal SLA documentation, integration scope, evidence-retention policy, and audit-readiness packs per client deployment on request.

CUSTOMER REVIEWS

What clients say about us

From data centres to factories to hospitals to malls — real teams, real outcomes across India.

★★★★★

Managing security in a high-footfall mall requires real-time visibility. With Tentovision’s ANPR, intrusion detection, and people counting, our team now operates proactively with faster response times and better situational awareness.

Security Operations Team

ETA Group · Retail · Chennai

★★★★★

The AI quality assurance system has transformed our packing accuracy. We went from multiple daily errors to near-perfect shipments. The real-time detection and instant alerts have saved us countless hours and prevented costly mistakes.

Operations Team

Oral-B · FMCG · Quality Assurance

★★★★★

Monitoring CCTV across multiple hospitals and different camera brands was extremely complex. Tentovision gave us a single, secure view with controlled access and on-demand streaming — helping us reduce bandwidth and respond faster.

Hospital Operations Team

Narayana Health · Healthcare · Pan India

★★★★★

Managing surveillance across multiple plants was chaotic with local NVRs. Tentovision’s VMS unified everything. Now we have a single, secure view of all operations from our head office — with edge AI for fire, smoke and PPE detection on every site.

Head of Safety Operations

Nippon Paints · Manufacturing · Pan India

★★★★★

We had DVR failures almost every month and no way to check footage remotely. Tentovision solved both overnight. Nothing replaced, and now our entire campus is monitored from one app. Genuinely impressive.

Head of Administration

NIFT Chennai · Education · Chennai

Book a Demo →

FAQ

Shoplifting Prevention — Frequently Asked Questions

The 10 questions buyers ask before signing off. Answers preserve the honest-scope framing throughout.

What is shoplifting prevention software?

Shoplifting prevention software is an AI video-analytics system that detects retail theft and fraud in real time by analysing camera feeds and correlating them with point-of-sale data — catching not just walk-out shoplifting but billing fraud, sweethearting, and scan-mismatch at the checkout. Unlike ordinary CCTV, which only records for later review, it flags suspicious events as they happen and links each to transaction evidence. Retail loss-prevention teams use it to reduce shrinkage and protect margin across one or many stores.

How does AI detect sweethearting at checkout?

Sweethearting is detected by correlating POS data with the camera over the till: the software compares the items scanned against the items physically passing the counter, and flags any item that moves past without a corresponding scan event. It also watches POS exceptions — voids, no-sales, low-value transactions, and excessive discounts — and matches them to the synchronised video. Because a sweethearting transaction still processes and looks legitimate, this scan-versus-movement correlation is what makes it visible, and every flag is sent for human review with the linked clip.

Does it work with self-checkout systems?

Yes. Self-checkout is especially exposed to non-scans, deliberate skip-scans, item substitution, and walk-aways, which is why it often shows higher shrink than manned lanes. The software watches the kiosk area and cross-checks scanned items against what is physically presented, flagging mismatches in real time so staff can step in. It is designed to make unmanned checkout as accountable as a staffed one.

Which POS systems does Tentovision integrate with?

It integrates with global POS and retail-ERP systems including NCR Voyix, Oracle Retail/MICROS, SAP Retail, Toshiba TCx, and Lightspeed, plus the India-stack POS commonly used in Indian retail — Ginesys, Gofrugal, Marg ERP, and Logic ERP — and with custom POS through an API. The integration receives scan events, voids, cancellations, and price overrides in real time so they can be correlated with video. Confirm the specific integrations you need during your assessment.

How much does retail shrinkage cost retailers worldwide?

Shrinkage — the gap between recorded and actual inventory — commonly runs between 1.5 and 3 percent of revenue, with the global average around 1.6 to 1.8 percent and emerging markets often higher. For a chain doing 200 million dollars in annual revenue (or 200 crore rupees), that translates to roughly 3 to 6 million dollars (or 3 to 6 crore rupees) in annual loss. Industry analyses attribute the largest share to external shoplifting, with internal or employee theft and billing fraud together making up a large portion and administrative error the rest — meaning a tool that only targets shoplifters misses most of the loss. The precise figure for your business depends on your format, region, and controls, which a shrinkage assessment quantifies. Figures are illustrative industry ranges drawn from NRF, ECR Retail Loss in Europe, and regional retail-industry analyses.

Can it detect employee theft?

Yes — it detects internal theft such as sweethearting, void and refund abuse, and cash-drawer manipulation by correlating POS exceptions with video, and it can rank cashiers by risk to direct where loss prevention looks. Importantly, it is built to flag transactions for human review rather than accuse anyone automatically, so every alert comes with the evidence a trained reviewer needs. Used with clear policies and lawful, DPDP/GDPR-aligned data handling, it protects honest staff from false positives as much as it catches genuine fraud.

Does it work with existing CCTV cameras?

Yes. It is vendor-agnostic and works with the cameras you already own over standard protocols, so there is typically no rip-and-replace — for checkouts that need a specific overhead angle, a camera may be added selectively. This keeps deployment fast and capital cost low.

How is this different from retail video analytics?

Retail video analytics is customer-behaviour intelligence — footfall, heatmaps, dwell time, and conversion — to help you sell more. Shoplifting prevention is loss prevention — theft detection, billing fraud, and shrinkage reduction — to help you lose less. Both run on Tentovision as different modules for different buyers — loss-prevention managers on one side, store-operations and marketing teams on the other.

Can I monitor shrinkage across all my stores from one dashboard?

Yes. The multi-store loss-prevention dashboard compares shrink across every location, with drill-down to store, zone, and cashier level, plus weekly and monthly trend and exception reports. It also surfaces patterns that recur across locations, so a chain can see which stores lose most, which cashiers are highest-risk, and which time windows see the most incidents. This estate-wide view is what per-store detection alone cannot give a loss-prevention manager.

How quickly can it be deployed?

Typical deployment is about 2 to 3 weeks per store for full POS-video integration, since it uses your existing cameras and connects to your existing POS. Stores can be rolled out in phases — many chains start with their highest-shrink locations to prove ROI before scaling. Timelines depend on store complexity and POS access.

Stop Watching Footage After the Loss — Prevent It

If shrink is quietly taking 1.5–3% of your revenue, AI shoplifting prevention turns your existing cameras and POS into a real-time loss-prevention platform — catching sweethearting, scan-mismatch, self-checkout fraud, and concealment as they happen, with the video-linked evidence to act on. Start with a free shrinkage assessment on your highest-shrink stores, anywhere in the world.

Get a Free Shrinkage Assessment→ See Live Detection Demo

Free Shrinkage Assessment

Find out what shrink is actually costing you — before you commit to LP spend

Tell us a little about your chain. A Tentosoft loss-prevention specialist will respond within one business day with a tailored shrinkage assessment — your stores, your current shrink range, where the loss is leaking, and the projected recovery on your numbers.

Shrinkage assessment

We map your shrink exposure against the four-segment breakdown — external, internal, billing, admin — using your category and store count.

Store-level walkthrough

Highest-shrink locations identified, checkout-camera audit reviewed, POS integration scope confirmed against NCR / Oracle / SAP / Toshiba / Lightspeed / Ginesys / Gofrugal / Marg / Logic / custom.

Pilot on 1–2 stores

Prove the loop on your highest-shrink stores — POS-video correlation live, evidence packages produced, LP team trained on the dashboard.

Phased chain rollout

Roll out to remaining stores typically 2–3 weeks per location (verify), with multi-store LP dashboard standing up as stores onboard.

Global LP Sales: +91 99620 37023

Email: info@tentosoft.com

HQ: 7th Floor, 4/293, RAR Technopolis, OMR, Perungudi, Chennai 600096, India

Get Your Free Shrinkage Assessment

Single-page form. Response within one business day. No commitment, no spam.

Trademarks, attributions & honest-scope notes. Indicative ranges, third-party POS & camera trademarks, governance frame, and consent-gated client references. Read more

Trademarks and third-party brand attributions. Tentovision and Tentosoft are trademarks of Tentosoft Solutions Private Limited. NCR Voyix is a trademark of NCR Voyix Corporation; Oracle Retail and MICROS are trademarks of Oracle Corporation; SAP Retail is a trademark of SAP SE; Toshiba TCx is a trademark of Toshiba Global Commerce Solutions; Lightspeed is a trademark of Lightspeed Commerce; Square is a trademark of Block, Inc.; Shopify POS is a trademark of Shopify Inc.; Ginesys is a trademark of Ginesys One; Gofrugal is a trademark of Gofrugal Technologies; Marg ERP is a trademark of Marg Compusoft Pvt Ltd; Logic ERP is a trademark of Logic ERP Solutions. Hikvision, Dahua, CP Plus, Uniview, Sparsh CCTV, HiFocus, Axis, Vivotek, Bosch, Pelco, Honeywell, and Tiandy are trademarks of their respective owners and are referenced here only to indicate the camera estates Tentovision is designed to work with via ONVIF, RTSP, and standard NVR APIs. Walmart, Tesco, Carrefour, Costco, Aldi, DMart, DMart Ready, Reliance Smart, Decathlon, and Spencer's are referenced solely as illustrative examples of self-checkout and hypermarket retail formats globally and in India; this page does not claim a commercial relationship with any of those companies.

DPDP, GDPR, lawful-basis processing, and human-reviewed detection — governance frame. Cashier risk-scoring, POS-exception review, after-hours till activity flagging, and optional face-recognition for repeat-offender flagging are operated under applicable data-protection law per jurisdiction — including the India Digital Personal Data Protection Act 2023, the EU General Data Protection Regulation, the UK Data Protection Act, and equivalent regional regimes — with worker notice and lawful-basis processing aligned to applicable employment-law guidance in each market. Every detection is flagged for human review with full context; the system is explicitly designed to direct loss-prevention attention, not to produce automated accusation or punitive action. Face-recognition use is consent-gated and is not biometric watchlisting. Used with clear policies, the LP loop protects honest staff from false positives as much as it catches genuine fraud — that hygiene is part of why the evidence package is acceptable for internal HR and legal use across jurisdictions.

Cloud and multi-region data residency. Tentovision's default cloud and evidence-retention environment is Microsoft Azure, with customer-choice multi-region hosting across India (Mumbai & Pune), Singapore, EU (Dublin & Frankfurt), and US (East & West) regions under ISO 27001 and SOC 2 aligned controls, with AES-256 encryption at rest and encrypted transit. We do not run customer evidence packages on AWS by default and do not represent ourselves as an AWS-hosted service. References to other global cloud providers elsewhere on the internet are not statements about Tentovision's hosting model.

All figures are illustrative industry ranges unless explicitly verified for your deployment. Global retail shrinkage of 1.5–3% of revenue (NRF cites ~1.6–1.8% global average; ECR Retail Loss Europe cites ~1.3–1.7%; CRISIL/ASSOCHAM cite ~2–3% for Indian organized retail), $3–6M loss for a $200M chain or ₹3–6 Cr for a ₹200 Cr chain, the 35–40 / 25–30 / 15–20 / 10–15 percent shrink-cause split, ~$1.5M / ₹1.5 Cr recovered at 30% shrink reduction, retail staff turnover of 60–100% in mature markets and 150–200% in emerging markets, 2–3 weeks per-store deployment, "5+ POS integrations," and "100+ camera brands" supported — all of these are indicative ranges drawn from public industry analyses (NRF National Retail Security Survey, ECR Retail Loss Group in Europe, regional studies) and Tentosoft project experience. Real figures for your business depend on store format, region, controls, POS scope, camera estate, and integration access; the shrinkage assessment quantifies them on your actual data.

Honest scope. AI loss prevention does not stop 100% of theft. False positives are inherent to any AI vision system, and the store manager always makes the final call to act on an alert. What the platform does is make checkout fraud visible, give multi-store chains an estate-wide view they otherwise cannot have, and produce defensible, DPDP/GDPR-compliant evidence packages.

Client and reference consent notes — verify before publishing. Naidu Hall, Akshaya Gold, Carryfresh, and Bubble Organic are consent-gated client references for platform use of the Tentovision loss-prevention module across Indian retail formats (fashion, jewelry, grocery, QSR); confirm written permission per name before publishing. Specific recovery figures, shrink-reduction percentages, deployment scope details, and store counts associated with any named client must be confirmed in writing with that client before being represented publicly. Enterprise references including Nippon Paint, Apollo Tyres, TVS, Delphi TVS Technologies, Oral-B, Narayana Health, NIFT Chennai, and STT GDC India are used in the client marquee with general consent for platform-use indication only — they do not constitute outcome claims (shrinkage reduction, ROI, or specific recovery figures) on this loss-prevention page, and any such outcome claim requires a separate written attestation from the named client. Global retailer names (Walmart, Tesco, Carrefour, Costco, Aldi, etc.) appear only as illustrative examples of retail format and do not represent client engagements.

AI Shoplifting Prevention Software — for Retail Chains Worldwide

Why Traditional CCTV Can't Prevent Shoplifting

No Real-Time Alerts

No POS Integration

No Behavioural Intelligence

Manual Review Doesn't Scale

Typical Shrinkage Breakdown — Where the Loss Actually Comes From

Tentovision converts your existing CCTV from a recording system into an active loss-prevention platform.

What is AI Shoplifting Prevention Software?

Loss prevention, not retail analytics — the buyer distinction

POS-video integration is the moat

Honest scope — what this is and isn't

Global deployment — POS-agnostic, camera-agnostic, multi-region hosting

How POS-Video Integration Prevents Theft

Camera Positioning

POS Data Feed

AI Correlation

Instant Alert

Evidence Package

Six Capabilities That Turn the Mechanism Into a Working LP Toolkit

Real-Time POS-Video Transaction Validation

AI-Powered Scan-Mismatch Detection

Self-Checkout Fraud Prevention

Employee Theft & Sweethearting Detection

Multi-Store Loss-Prevention Dashboard

Video-Linked Evidence & Audit Trail

Integrates With the POS Systems Retail Already Runs

No rip-and-replace · 100+ camera brands supported

Why POS-Video Integration Is the Moat

Camera alone, POS alone, and the gap only the correlation exposes

10 Shoplifting & Billing Fraud Patterns Tentovision Detects

Sweethearting

Self-checkout non-scan

Barcode swap / price switch

Product swap inside store

Concealed items at billing

Fake returns / refund abuse

Employee-assisted theft

Cart/bag/stroller hiding

Distraction billing

Repeat small-value theft

Traditional LP vs Tentovision AI LP

Why Choose Tentovision for Shoplifting Prevention

AI-First, Built-In Correlation Engine

Works With Your Cameras

POS-Agnostic · Global & Regional

Multi-Region Data Residency

Camera Health Monitoring Included

One Platform: LP & Retail Analytics

Tentovision AI Video Analytics — Loss Prevention & Business Intelligence

AI Video Analytics Parent Platform

Retail Video Analytics Sell More

People Counting System

Face Recognition

Shoplifting Prevention You Are Here · Lose Less

Shoplifting Prevention Across Retail Formats — Worldwide

Grocery & Supermarkets

Fashion & Apparel

Electronics & Appliance

Jewelry

Pharmacy & Health

Quick-Service Restaurants

Hypermarkets & Multi-Format Stores

How ETA Group enhanced mall security and crowd management with AI

How City Centre Mall optimised footfall and parking with AI analytics

How Nippon Paint deployed edge AI for fire, smoke and PPE detection

How Narayana Health unified CCTV across multi-brand hospital infrastructure

How NIFT Chennai eliminated local hardware with full cloud surveillance

How STT GDC India enforced construction site safety with AI

Shoplifting Prevention Governance & Fairness

DPDP & GDPR Aligned

ISO 27001 Aligned

SOC 2 Aligned

VAPT Aligned

Multi-Region Azure Hosting

Lawful-Basis Employee Monitoring

Human-Reviewed Detection

Per-Project Attestation

Shoplifting Prevention — Frequently Asked Questions

Stop Watching Footage After the Loss — Prevent It