AWS Software / SaaS Global

Solink

Fraud Detection

As a company born in the cloud, Solink provides video analysis and AI-powered analytics to retailers who have historically treated security cameras as an insurance policy to manually retrieve footage after an incident takes place.

"We can reduce shrink by up to 70 percent for a company with Solink," says Farrell.
2% increase in profits through loss reduction
3–5x average return on investment for businesses
70% reduction in shrink

Reported impact

Business outcome

True to its foundation in the cloud, Solink is investing in generative AI solutions such as Solink Sidekick AI, which the company launched in 2023. "Solink has a highly scalable system on AWS, and we will continue to use the scale and distribution of AWS to implement new technologies and deliver more value to our customers," says Soukup.

  • Revenue Growth
  • Risk Reduction
  • Scalability

Solution

When Solink , a security and loss prevention solutions company, set out to change the face of retail by offering a comprehensive physical security solution, it looked to the cloud to provide the scalability and availability it needed. The company's product combines video surveillance with transaction data to help retailers assess and identify risk across thousands of locations, access footage of incidents, and drill into the details of every transaction. Solink chose to build on Amazon Web Services (AWS) and to use artificial intelligence (AI) for video footage analysis. On AWS, Solink is scaling with a lean engineering team while providing reliable, user-friendly software to its customers.

Data flow

Solink's solution creates a pipeline built on AWS to continuously process live security footage from customers. The pipeline uses a hybrid model that records video at the edge while continually determining what footage needs to be cloud processed. Certain segments of the footage are stored in Amazon Simple Storage Service (Amazon S3), which provides object storage built to retrieve virtually any amount of data from anywhere.

Solution architecture

3 components · 2 layers
  1. Compute
    • Amazon SageMaker Builds, trains, or deploys the machine learning models behind the fraud and loss-detection workflows.
    • Amazon Rekognition Analyzes image and video content used by the fraud and loss-detection workflows.
  2. Storage
    • Amazon S3 Stores source data, documents, or generated artifacts used by the fraud and loss-detection workflows.

Architecture clues

  • Solink's solution creates a pipeline built on AWS to continuously process live security footage from customers.
  • The pipeline uses a hybrid model that records video at the edge while continually determining what footage needs to be cloud processed.

Evidence from the source

"Having high availability across Availability Zones in an AWS Region was especially important to us." Solink also received cost-optimization support from the AWS team while it built a solution capable of handling hundreds of thousands of...
"We used a lot of solutions that AWS provided," says Wong.
On AWS, Solink is scaling with a lean engineering team while providing reliable, user-friendly software to its customers.
Solink chose to build on Amazon Web Services (AWS) and to use artificial intelligence (AI) for video footage analysis.
Solink manages tens of thousands of sites in more than 40 countries and monitors more than 1 billion transactions per month.
The company chose AWS and took advantage of AWS Regions , physical locations around the world where AWS clusters its data centers, to go global with its multinational customers.
Transparency Evidence & methodology 96/100 reference value · 90% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value96/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Solink customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/solink-case-study/
    Source type
    Vendor-published customer story
    Published
    Apr 1, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. LinkedIn

    Solink | LinkedIn

    Independent secondary
    https://www.linkedin.com/company/solink/

    "numberOfEmployees":{"value":391,"@type":"QuantitativeValue"}

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.