List of Amazon Rekognition Customers
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Since 2010, our global team of researchers has been studying Amazon Rekognition customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Amazon Rekognition for Computer Vision from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Amazon Rekognition for Computer Vision include: Absa Group, a South Africa based Banking and Financial Services organisation with 60000 employees and revenues of $98.92 billion, C-Span Archives, a United States based Media organisation with 400 employees and revenues of $80.0 million, Marinus Analytics, a United States based Professional Services organisation with 10 employees and revenues of $1.0 million, Aella Credit Limited, a United States based Banking and Financial Services organisation with 10 employees and revenues of $1.0 million and many others.
Contact us if you need a completed and verified list of companies using Amazon Rekognition, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Artificial Intelligence software purchases.
The Amazon Rekognition customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Artificial Intelligence software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Absa Group | Banking and Financial Services | 60000 | $98.9B | South Africa | Amazon Web Services (AWS) | Amazon Rekognition | Computer Vision | 2022 | n/a | In 2022, Absa Group deployed Amazon Rekognition on Amazon Web Services as part of its AI Platform initiatives, integrating image and video analysis into digital channels. The initial deployment positioned Amazon Rekognition as a core inference service for automated media analysis across customer-facing touchpoints. Amazon Rekognition was configured to support image and video analysis modules such as face detection, text detection, and content moderation, leveraging Rekognition APIs for real-time and batch inference workflows. The implementation explicitly integrated with the bank's public web assets via Lumen CDN to optimize media delivery and client-side upload performance. Architecturally the solution was implemented as a cloud-native service on AWS, with API-driven calls from front-end applications and centralized processing in back-end pipelines. Operational scope emphasized digital channels in South Africa and targeted business functions including customer onboarding, identity verification, and automated content screening across online banking and public web properties. Governance focused on data handling and compliance controls for biometric and PII data, role-based access to Amazon Rekognition APIs, and centralized logging for auditability and operational monitoring. Rollout followed an incremental channel-by-channel approach, paired with standardized inference workflows and runbooks for platform and security teams. | |
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Aella Credit Limited | Banking and Financial Services | 10 | $1M | United States | Amazon Web Services (AWS) | Amazon Rekognition | Computer Vision | 2017 | n/a | In 2017 Aella Credit Limited implemented Amazon Rekognition as an AI Platform to automate identity verification on its mobile application serving emerging markets. The deployment focused on real time facial detection and verification to support KYC and customer onboarding workflows. Aella Credit Limited used Amazon Rekognition for identity verification to link biometric checks with employer and mobile phone data as part of its lending underwrite process. The implementation leverages Amazon Rekognition face detection and face comparison capabilities to detect and verify an individual’s identity in real time without human intervention. It was configured to combine biometric verification with employer and mobile phone data sources to uncover overlapping profiles and duplicate datasets within KYC processes. The integration emphasized on demand API verification from the mobile application to enable automated identity decisions at the point of application. The solution was deployed on Amazon Web Services and integrated directly into Aella’s customer facing mobile application, enabling verification at point of application submission. Operational coverage targeted onboarding and KYC functions across Aella’s markets in emerging economies, reducing human review in initial identity checks and streamlining downstream underwriting workflows. Amazon Rekognition was used alongside Aella’s existing data inputs to improve automated identity workflows. According to Aella Credit the use of Amazon Rekognition reduced verification errors significantly and improved recognition across various skin tones compared to other alternatives. The company reports faster access to loan products as a result of real time verification and the ability to scale identity verification without additional human intervention. Amazon Rekognition also supported KYC by helping discover overlapping profiles and duplicate datasets. | |
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C-Span Archives | Media | 400 | $80M | United States | Amazon Web Services (AWS) | Amazon Rekognition | Computer Vision | 2017 | n/a | In 2017, C-SPAN Archives deployed Amazon Rekognition as an AI Platform to automate timecoded tagging and indexing of broadcast video across its channels. The implementation was focused on content from three network stations and five additional video feeds, increasing the archival throughput from 3,500 hours per year to 7,500 hours per year and enabling the organization to plan for indexing 100% of its first run content, a result noted by Alan Cloutier, Technical Manager at C-SPAN Archives. Amazon Rekognition was used to deliver face recognition and camera presence tagging, speaker on camera identification down to the second, and automated metadata enrichment to support search and discovery workflows. The deployment leveraged Rekognition for entity resolution and automated tagging, mapped against C-SPANs internal entity database containing approximately 97,000 entities, and produced timestamped metadata suitable for full text and metadata indexing. The architecture was operated on Amazon Web Services, with Rekognition integrated into C-SPANs media ingestion and cataloging pipeline and its existing archival metadata store. Operational scope centered on the Archives team and the cataloging workflow that supports public access, with automated processing designed to scale with incoming broadcast and feed content. Governance and process changes emphasized operationalizing automated tagging into existing catalog procedures, with configuration and onboarding reported as straightforward by internal technical staff. Outcomes explicitly cited by the organization include a doubling of processed hours and the ability to index all first run content, and the implementation served to instrument searchability and archival completeness without extensive setup overhead. | |
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Professional Services | 10 | $1M | United States | Amazon Web Services (AWS) | Amazon Rekognition | Computer Vision | 2017 | n/a |
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Buyer Intent: Companies Evaluating Amazon Rekognition
- KMC Solutions, a Philippines based Construction and Real Estate organization with 5000 Employees
- Credo Asset Finance, a United Kingdom based Banking and Financial Services company with 17 Employees
- PAXAFE, a United States based Professional Services organization with 30 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
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