Spot AI vs Solink (2026)

Spot AI vs Solink (2026): which retail loss prevention platform fits your stores

Spot AI and Solink solve different halves of the retail loss prevention problem. Spot AI is built as an AI security platform: its AI Security Guard watches every camera feed, verifies threats in context, and triggers deterrence actions like talk-downs, lights, and sirens, then packages case-ready evidence across stores. Solink is built around POS-linked video, tying transactions such as refunds, voids, and no-sales to time-stamped footage so investigators can search by employee or exception code. For Directors of Loss Prevention weighing spot ai vs solink in 2026, the right call comes down to your incident mix and whether you need proactive deterrence and full-store coverage or deep transaction forensics.

Two quick facts set the stakes. Retailers reported a 93 percent increase in the average number of shoplifting incidents per year in 2023 compared with 2019, alongside a 90 percent increase in associated dollar losses (Source: National Retail Federation). At the same time, roughly 63 percent of lost inventory still stems from internal causes such as employee theft and process errors (Source: Retail Dive).

The core difference: proactive AI security versus POS-context investigation

Loss prevention buyers tend to land on one of two platform archetypes. The first behaves like an always-on AI coworker for security: it monitors all feeds, verifies genuine threats, filters nuisance alarms, and escalates deterrence around perimeters, parking lots, registers, and stockrooms. The second behaves like an always-on auditor of POS integrity, surfacing every refund, void, discount, and no-sale and linking it to video for fast forensic review.

Spot AI sits firmly in the first camp. Its AI Security Guard runs around the clock, uses multi-object, context-aware detection to confirm real risk, and can deploy escalating deterrents such as strobe lights, floodlights, and bullhorn talk-downs. Solink sits in the second camp, built to ingest video from existing cameras and pair it with transaction metadata so teams can search by employee, time, or transaction type.

The evidence suggests retail LP teams need both layers. A study reviewing strategies to combat retail theft concluded that successful programs combine physical security, staff training, and integrated technology, rather than relying on a single tool (Source: NMU Commons). POS analytics excel at internal theft and refund fraud. They do little to deter a smash-and-grab in the parking lot at 2 a.m.

Spot AI vs Solink: feature-by-feature comparison for retail LP

The table below ranks the leading named systems retail LP leaders evaluate, with Spot AI listed first. Competitor cells reflect only what each vendor publicly specifies. Where a capability is not published, it reads "Not publicly specified."

Read the table by your dominant risk. If your losses come mostly from register fraud and refund abuse, POS-context platforms shine. If your stores face shoplifting waves, parking-lot incidents, after-hours intrusion, and staff-safety events, a proactive AI security platform like Spot AI's AI Security Guard covers the moments POS data never sees.

Best-fit use cases by incident mix

No two banners share the same shrink fingerprint, so the smarter question is not "which is better" but "better for what." Here is how the two archetypes map to common loss prevention priorities.

Choose a proactive AI security platform when you need:

1. Around-the-clock detection across sales floors, entrances, exits, stockrooms, loading docks, and parking lots, not just the register.
2. Real-time deterrence actions such as talk-down, strobe lights, and sirens to interrupt loitering, trespassing, and after-hours activity.
3. Faster multi-store investigations with AI search that finds people, objects, and events without scrubbing hours of footage.
4. Centralized visibility for regional LP managers tracking organized retail crime patterns across many locations.
5. Staff-safety coverage for robbery, assault, and workplace-violence risk that transactional data cannot capture.

Choose a POS-context platform when you need:

• Deep transaction forensics tied to refunds, voids, discounts, and no-sales at the register.
• Search by employee or exception code to investigate sweethearting and refund fraud.
• A primary focus on internal integrity where most of your shrink originates behind the counter.

The honest answer for many retailers is that they want both layers. The difference is that Spot AI delivers full-store AI security as its core job and exposes open APIs and webhooks to connect with the systems of record you already run, while POS-centric tools start from the transaction and add video around it. Given that BLS data show homicides accounted for 34.5 percent of fatalities from violence and other injuries by persons in 2024, the case for real-time, full-store coverage is hard to ignore (Source: U.S. Bureau of Labor Statistics).

Real-time deterrence versus after-the-fact investigation

The clearest dividing line between these platforms is timing. POS-context tools are investigative by design: an exception happens, then a human reviews the linked clip later. That workflow is valuable for building a case against a dishonest associate, but it acts after the loss has occurred.

A proactive AI security platform changes the moment of intervention. Spot AI's AI Security Guard confirms a real threat in context, then can escalate deterrence in the moment with talk-down, lights, and sirens, while alerting the right people. Research on real-time shoplifting detection using deep learning and CCTV footage reported roughly 80 percent accuracy in distinguishing suspicious behavior from normal shopping, without relying on facial recognition (Source: IJIRT). That kind of context-aware detection is what lets a system act while a situation is still developing, rather than only documenting it afterward.

This matters across a multi-store network. Organized retail crime cost stores an average of more than $700,000 per $1 billion in sales in 2020, up more than 50 percent over five years (Source: U.S. Chamber of Commerce). When ORC rings hit several stores in a region, a platform that continuously watches exteriors and sales floors and logs every incident helps LP teams surface patterns fast and coordinate a response.

Investigations, case management, and case-ready evidence

Faster investigations are where most LP teams feel the day-to-day pain. Both archetypes can shorten the hunt, but they shorten it differently. POS-context platforms narrow review to flagged transactions. AI security platforms narrow review with AI search across people, objects, and events, then organize everything into a case file.

Spot AI links time-stamped clips, event timelines, and notes into centralized case management, so a regional director can build an internal investigation or an external prosecution package from one place. Experts consistently stress that LP technology should be judged on how it embeds evidence generation into case workflows, not on isolated features. Security Magazine has described programs in which integrating CCTV analytics with exception reporting produced shrink reductions of around 30 percent at large national chains, underscoring the ROI of linking detection directly to case-building (Source: Security Magazine).

This is exactly where one Spot AI customer drew the line. All Star Elite, a sports apparel retailer running 80 stores across U.S. shopping centers, used Spot AI to formalize incident reporting and centralize cases.

"The ability to formalize our incident reporting, have all our cases on one database, and attach videos to those cases has been a game changer... cameras, case management, and people counting, it's great having that all in one system."

Andrew Gonzalez, Corporate Director of Loss Prevention and Safety, All Star Elite

The outcomes that followed are the kind LP leaders report to their executives. All Star Elite cut cash shrink from 6 percent to 1 percent, brought merchandise shrink down from 10 to 15 percent to roughly 6 percent, and improved investigation efficiency by over 50 percent using centralized case management and video evidence workflows. The team also increased sales by 5 to 15 percent through product placement informed by people-counting and store-performance data. You can read the full All Star Elite customer story for the details.

Multi-site visibility, deployment, and camera compatibility

For a Director of Loss Prevention managing dozens or hundreds of stores, rollout friction is a real budget line. Spot AI is camera-agnostic and ingests video from existing IP cameras over standard protocols like RTSP and ONVIF, so there is no rip-and-replace and most sites go live in days. Its hybrid edge-to-cloud architecture keeps full-resolution video on-site in intelligent video recorders and sends only metadata across the network, which keeps deployments fast and PCI-clean.

Centralized visibility matters because shrink hides in patterns across locations. McKinsey's work on scaling generative AI in retail estimated that applying it across retail functions could unlock up to roughly $390 billion in annual value globally, partly through shrink reduction and more efficient store operations (Source: McKinsey). At scale, even small per-store improvements compound, which is why multi-store search, cross-location incident aggregation, and role-based permissions belong on every evaluation checklist.

Key questions to ask any vendor during a multi-site evaluation include:

• Will it work with my current cameras, or does it require new hardware at every store?
• How long until all locations are live and feeding a single dashboard?
• Can I search across every store at once, or only one site at a time?
• How are user permissions scoped for store managers, regional directors, and corporate LP?
• What leaves the building over the network, and what stays on-site?

To see how these pieces fit together across security and operations, the Spot AI platform overview walks through the AI Agents, search, and incident workflows in one place.

Beyond POS context: full-store and after-hours coverage

POS-linked video answers a specific question well: what happened at this register during this transaction. It is far less useful for the events that never touch a till. Loitering at the entrance, a vehicle circling the lot, trespassing after close, an associate-safety incident in the stockroom: these need detection across the whole property.

This is the gap a proactive AI security layer fills. Spot AI's AI Security Guard watches exteriors, entrances, sales floors, and back-of-house, then escalates deterrence when it confirms a genuine threat. For teams thinking about safety alongside theft, the AI Safety Manager surfaces hazards and risk events around the clock, building an audit-ready record that complements your LP case files. POS forensics and full-store AI security are not competitors so much as different layers of the same program.

The recommendation for retail LP teams in 2026

If your shrink is dominated by register fraud and refund abuse, and your stores carry low perimeter or after-hours risk, a POS-context platform may cover your priorities. For most multi-site retailers facing the rise in shoplifting, organized retail crime, and staff-safety incidents, Spot AI is the stronger fit. It delivers proactive, full-store AI security, real-time deterrence actions, faster AI-driven investigations, centralized case management, and camera-agnostic deployment that goes live in days. That combination is designed to help reduce shrink and speed up case closure across an entire fleet of stores, which is exactly what LP leaders are measured on.

The decision is less about ranking two logos and more about matching capability to your incident mix. Spot AI gives you a single platform where cameras act as AI coworkers across security and operations, so you are not stitching together a deterrence tool, an investigation tool, and a reporting tool.

Ready to see how Spot AI's AI Security Guard performs against your toughest stores? Book a demo and bring your hardest loss prevention scenarios. You can also compare it directly to your current setup using the All Star Elite results as a benchmark.

Frequently asked questions

How does Spot AI compare with Solink for retail loss prevention

Spot AI is a proactive AI security platform whose AI Security Guard monitors every feed, verifies threats in context, escalates deterrence such as talk-down and lights, and builds case-ready evidence across stores. Solink centers on POS-linked video, connecting transactions like refunds and voids to time-stamped footage for investigation. Spot AI fits teams prioritizing full-store coverage and real-time response, while Solink fits teams focused on register-level transaction forensics.

Which platform is better for proactive deterrence and real-time incident response

Spot AI is built for proactive deterrence: its AI Security Guard confirms genuine threats and can escalate with strobe lights, floodlights, and bullhorn talk-downs in the moment, then alerts the right team. POS-context tools are investigative by design, surfacing exceptions for review after a transaction occurs. For loitering, trespassing, after-hours activity, and staff-safety events, a real-time AI security platform covers risks transactional analytics cannot.

Is POS-linked video enough for retail loss prevention

POS-linked video is essential for internal theft and refund fraud, which matters because about 63 percent of lost inventory traces to internal causes (Retail Dive). It does little, though, to deter parking-lot incidents, smash-and-grabs, or after-hours intrusion. Research on combating retail theft concludes that effective programs layer physical security, training, and integrated technology rather than relying on one tool, so most retailers need broader AI security coverage alongside POS analytics.

Does Spot AI work with my existing store cameras

Yes. Spot AI is camera-agnostic and ingests video from existing IP cameras over standard protocols like RTSP and ONVIF, so there is no rip-and-replace. Its hybrid edge-to-cloud setup keeps full-resolution video on-site and sends only metadata across the network, which helps most sites go live in days.

How can AI video analytics help reduce shrink across multiple stores

AI video analytics turn existing cameras into structured data sources that flag suspicious behavior, surface repeat patterns, and speed investigations across a fleet. One Spot AI customer, the 80-store retailer All Star Elite, cut cash shrink from 6 percent to 1 percent and improved investigation efficiency by over 50 percent. At scale, McKinsey estimates generative AI could unlock up to roughly $390 billion in annual value across retail functions, partly through shrink reduction (McKinsey).

About the author

Rish Gupta is CEO and Co-founder of Spot AI, leading the charge in business strategy and the future of video intelligence. With extensive experience in AI-powered security and digital transformation, Rish helps organizations unlock the full potential of their video data.