SeeChange

Name: SeeChange
Brand: SeeChange

Creating powerful edge to cloud, Vision AI solutions for retail

Est 2018|Raised $13.3M|7 Ref Customers

LinkedIn 3K|Website|FTEs 61|HQ Manchester, England, GB

MarketsPhysical Security Systems

Published Oct 15, 2026

01Common complaints

1. Organizations that require transparent, published pricing and self-serve trials: the vendor follows a sales-led procurement model without public pricing tiers. 2. Retailers whose primary need is organized retail crime (ORC) intelligence and case management rather than event detection: case- and incident-management platforms focused on ORC may deliver faster LP-team productivity gains for those specific needs. 3. Buyers seeking a checkout-only vendor with large-scale, publicly documented deployments: if the only objective is self-checkout shrink reduction and public scale proof points are the primary selection criterion, checkout-specialist vendors with more published large-sample research may be closer matches. 4. Organizations unable to support the pilot-to-rollout operational changes required by vision deployments (training, SOP updates, response processes). 5. Procurement processes that mandate specific, publicly documented certifications (for example, SOC 2 Type II): the vendor references ISO-aligned governance but does not present extensive public audit artifacts.

Deal disqualifiers (poor fit)

Smaller retailers without sufficient scale to justify integration and rollout costs.
Organizations unwilling to expand camera usage due to privacy or regulatory concerns.
Stores with very poor camera placement or image quality and no remediation plan.

Vendor-acknowledged or implied limitations

False positives and the need for outcome-based tuning are recognized issues; detection systems require iterative model and rules adjustment.

Gaps in public documentation important to buyers

- Pricing transparency: no public pricing tiers were found. - Limited public independent customer review footprint on major review platforms, which constrains independent validation of claims. - Certification clarity: the vendor references ISO-aligned governance but does not publish a broad set of audit artifacts (for example, SOC 2 Type II) in public materials. - Audit log retention: publicly disclosed default retention is 30 days; buyers with longer retention or compliance needs should request configurable retention options.

Roadmap / upcoming features

No public product roadmap with dates was identified; procurement should request roadmap and release cadences under NDA if roadmap detail is required for evaluation.

02Why they buy

Documented strengths and vendor claims

Partner integration approach for embedding detection capabilities into retail technology ecosystems.
A multi-use-case vision approach intended to address checkout shrink, aisle monitoring, exit tracking, and hazard detection within a single platform architecture.
Vendor-described privacy and security controls for camera data (encryption, identity controls, and access logging).

Areas where competitors have comparative advantages

Some competitors provide more extensive public research and scale figures for checkout shrink, which can be relevant for checkout-focused procurements.
Other vendors emphasize case-management and community-driven incident sharing or shelf-availability optimization, which can be more directly relevant to certain operational priorities.

03Field reports

Limited public end-user reviews

The vendor primarily sells into enterprise retail programs and through partners, which corresponds with relatively few public, verified customer reviews on mainstream SaaS review platforms. Employee reviews and vendor-supplied customer quotes are present in public materials.

Positive feedback (examples)

Employee perspective (employee-review site): employees cited positive aspects of working on the technology, while also noting startup-related risks. These comments reflect workplace sentiment rather than independent product efficacy verification. Vendor-cited operational outcomes (customer example): the vendor reported a pilot at a retail store with reductions in measured shrink and fewer associate interventions. These results were reported by the vendor; methodology and independent verification were not disclosed.

Negative feedback and concerns

Employee reviews mention startup risks, which can be relevant to vendor stability and execution risk.
Market-level concerns common to in-store camera analytics include privacy, false positives, and customer friction; these are general objections for vision-based approaches and were not exclusively tied to the vendor in public materials.

Reasons customers chose the product (inferred)

Claims of reusing existing camera infrastructure and flexible edge/cloud deployment.
Modular platform with configurable rules intended to address multiple in-store use cases.
Partner-led route to market for integrations with self-checkout and POS ecosystems.

Reasons customers might not purchase or might churn (inferred)

Operational false positives and resulting staff alert fatigue.
Integration complexity with self-checkout and POS vendors.
Challenges scaling a pilot to chain-wide deployment.
Procurement concerns if enterprise certifications and audit artifacts are not publicly available.

Retail pilot — vendor-published metrics

The vendor reported a pilot at a store with a reduction in measured shrink and fewer associate interventions over a two-month period. These results are vendor-published; the methodology (baseline definition, controls, store mix, seasonality, and measurement approach) is not disclosed publicly. Single-store pilot outcomes may not generalize across banners or geographies without additional validation.

Trade-show demonstrations and partner deployments

The vendor has demonstrated the product with technology partners at industry events and reported partner-led deployments. Trade-show demonstrations and pre-rollout notices do not constitute independently verified chain-wide outcomes.

Facilities partner (example)

A facilities/security partner reported trial activity and planned rollouts with the vendor. This constitutes a third-party indication that trials progressed to planned rollouts in at least one partnership context, but the partner did not publicly publish quantified ROI metrics in the materials reviewed.

04Best fit

Company sizes

Best fit: multi-store retail chains with central loss-prevention governance.
Indicative threshold: enterprises with roughly 50 or more stores where centralization and standardization justify the deployment investment.

Industries / verticals

Grocery and supermarket formats (fresh-produce recognition and self-checkout are relevant).
Big-box and mass retail formats with high transaction volumes and self-checkout lanes.

Roles and personas

Decision makers: VP/Head of Asset Protection or Loss Prevention, CIO/CTO (store technology and integration), and VP of Store Operations.
Daily users: self-checkout attendants, LP analysts, store managers, and security operations center personnel.

Pain points addressed

Self-checkout shrink and fraud patterns that are difficult to detect manually.
Associate time wasted on unnecessary self-checkout interventions.
Safety incident detection and mitigation for spills and hazards.

Qualification criteria (good fit)

Existing camera infrastructure and willingness to integrate camera feeds into a detection platform.
Capacity to run pilots and support change management necessary for store-level adoption.
Organizational commitment to respond to alerts and operationalize insights.

05The pitch

What the product does (core value proposition)

SeeChange provides a vision AI (computer vision) platform intended primarily for retail loss prevention and operational risk reduction. The product analyzes video and other in-store signals (for example, self-checkout cameras, CCTV, scanner and scale telemetry) to detect events such as: - Self-checkout shrink and fraud patterns (mis-scans, barcode switching, product stacking, walkaways) and to trigger on-screen prompts or staff alerts. - Fresh produce (non-barcoded) recognition to speed item identification at self-checkout. - High-value item protection by tracking movement through shelf → checkout → exit. - Hazard and spill detection and other safety issue detection (blocked exits, trip hazards) to reduce incidents. Vendor messaging emphasizes reuse of existing camera infrastructure and an edge-to-cloud deployment model (mix of on-prem/edge and cloud components).

Target market and market positioning

- Primary target: mid-market to enterprise grocery and big-box retailers with substantial self-checkout volume and shrink exposure. - Secondary adjacent target (via partners): facilities management and security operations where hazard detection or theft analytics can be part of a managed service. The vendor positions the product as a connected-store vision system that aims to correlate events across aisle, shelf, checkout, and exit areas rather than operating solely as a point solution at checkout.

Get a comprehensive analysis of SeeChange including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Oct 15, 2026

01Common complaints

Organizations that require transparent, published pricing and self-serve trials: the vendor follows a sales-led procurement model without public pricing tiers.
Retailers whose primary need is organized retail crime (ORC) intelligence and case management rather than event detection: case- and incident-management platforms focused on ORC may deliver faster LP-team productivity gains for those specific needs.
Buyers seeking a checkout-only vendor with large-scale, publicly documented deployments: if the only objective is self-checkout shrink reduction and public scale proof points are the primary selection criterion, checkout-specialist vendors with more published large-sample research may be closer matches.
Organizations unable to support the pilot-to-rollout operational changes required by vision deployments (training, SOP updates, response processes).
Procurement processes that mandate specific, publicly documented certifications (for example, SOC 2 Type II): the vendor references ISO-aligned governance but does not present extensive public audit artifacts.

Deal disqualifiers (poor fit)

Smaller retailers without sufficient scale to justify integration and rollout costs.
Organizations unwilling to expand camera usage due to privacy or regulatory concerns.
Stores with very poor camera placement or image quality and no remediation plan.

Vendor-acknowledged or implied limitations

False positives and the need for outcome-based tuning are recognized issues; detection systems require iterative model and rules adjustment.

Gaps in public documentation important to buyers

Pricing transparency: no public pricing tiers were found.
Limited public independent customer review footprint on major review platforms, which constrains independent validation of claims.
Certification clarity: the vendor references ISO-aligned governance but does not publish a broad set of audit artifacts (for example, SOC 2 Type II) in public materials.
Audit log retention: publicly disclosed default retention is 30 days; buyers with longer retention or compliance needs should request configurable retention options.

Roadmap / upcoming features

No public product roadmap with dates was identified; procurement should request roadmap and release cadences under NDA if roadmap detail is required for evaluation.

02Why they buy

Documented strengths and vendor claims

Partner integration approach for embedding detection capabilities into retail technology ecosystems.
A multi-use-case vision approach intended to address checkout shrink, aisle monitoring, exit tracking, and hazard detection within a single platform architecture.
Vendor-described privacy and security controls for camera data (encryption, identity controls, and access logging).

Areas where competitors have comparative advantages

Some competitors provide more extensive public research and scale figures for checkout shrink, which can be relevant for checkout-focused procurements.
Other vendors emphasize case-management and community-driven incident sharing or shelf-availability optimization, which can be more directly relevant to certain operational priorities.

03Field reports

Limited public end-user reviews

Positive feedback (examples)

Vendor-cited operational outcomes (customer example): the vendor reported a pilot at a retail store with reductions in measured shrink and fewer associate interventions. These results were reported by the vendor; methodology and independent verification were not disclosed.

Negative feedback and concerns

Employee reviews mention startup risks, which can be relevant to vendor stability and execution risk.
Market-level concerns common to in-store camera analytics include privacy, false positives, and customer friction; these are general objections for vision-based approaches and were not exclusively tied to the vendor in public materials.

Reasons customers chose the product (inferred)

Claims of reusing existing camera infrastructure and flexible edge/cloud deployment.
Modular platform with configurable rules intended to address multiple in-store use cases.
Partner-led route to market for integrations with self-checkout and POS ecosystems.

Reasons customers might not purchase or might churn (inferred)

Operational false positives and resulting staff alert fatigue.
Integration complexity with self-checkout and POS vendors.
Challenges scaling a pilot to chain-wide deployment.
Procurement concerns if enterprise certifications and audit artifacts are not publicly available.

Retail pilot — vendor-published metrics

Trade-show demonstrations and partner deployments

Facilities partner (example)

04Best fit

Company sizes

Best fit: multi-store retail chains with central loss-prevention governance.
Indicative threshold: enterprises with roughly 50 or more stores where centralization and standardization justify the deployment investment.

Industries / verticals

Grocery and supermarket formats (fresh-produce recognition and self-checkout are relevant).
Big-box and mass retail formats with high transaction volumes and self-checkout lanes.

Roles and personas

Decision makers: VP/Head of Asset Protection or Loss Prevention, CIO/CTO (store technology and integration), and VP of Store Operations.
Daily users: self-checkout attendants, LP analysts, store managers, and security operations center personnel.

Pain points addressed

Self-checkout shrink and fraud patterns that are difficult to detect manually.
Associate time wasted on unnecessary self-checkout interventions.
Safety incident detection and mitigation for spills and hazards.

Qualification criteria (good fit)

Existing camera infrastructure and willingness to integrate camera feeds into a detection platform.
Capacity to run pilots and support change management necessary for store-level adoption.
Organizational commitment to respond to alerts and operationalize insights.

05The pitch

What the product does (core value proposition)

Self-checkout shrink and fraud patterns (mis-scans, barcode switching, product stacking, walkaways) and to trigger on-screen prompts or staff alerts.
Fresh produce (non-barcoded) recognition to speed item identification at self-checkout.
High-value item protection by tracking movement through shelf → checkout → exit.
Hazard and spill detection and other safety issue detection (blocked exits, trip hazards) to reduce incidents.

Vendor messaging emphasizes reuse of existing camera infrastructure and an edge-to-cloud deployment model (mix of on-prem/edge and cloud components).

Target market and market positioning

Primary target: mid-market to enterprise grocery and big-box retailers with substantial self-checkout volume and shrink exposure.
Secondary adjacent target (via partners): facilities management and security operations where hazard detection or theft analytics can be part of a managed service.

The vendor positions the product as a connected-store vision system that aims to correlate events across aisle, shelf, checkout, and exit areas rather than operating solely as a point solution at checkout.

Get a comprehensive analysis of SeeChange including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →