List of RadiusAI Customers
Tempe, 85281, AZ,
United States
Since 2010, our global team of researchers has been studying RadiusAI 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 RadiusAI for ML and Data Science Platforms 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 RadiusAI for ML and Data Science Platforms include: Kroger, a United States based Retail organisation with 409000 employees and revenues of $147.12 billion, QuikTrip, a United States based Retail organisation with 24034 employees and revenues of $10.50 billion and many others.
Contact us if you need a completed and verified list of companies using RadiusAI, 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 software purchases.
The RadiusAI 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 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 |
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Kroger | Retail | 409000 | $147.1B | United States | RadiusAI | RadiusAI | ML and Data Science Platforms | 2022 | n/a |
In 2022, Kroger engaged RadiusAI under the ML and Data Science Platforms category to pilot AI led loss prevention and checkout analytics. Public panel appearances by Kroger asset protection and self checkout leaders alongside RadiusAI executives indicate trials or collaboration on retail security and checkout optimization in the United States.
Implementation signals point to deployment of RadiusAI capabilities for computer vision based shrink detection, anomaly detection on transaction streams, and checkout analytics models, inferred from industry reporting and forum presentations. Typical ML and Data Science Platforms workflows implemented likely include data ingestion and feature engineering pipelines, model training and validation, and near real time or batch scoring of checkout events. RadiusAI is referenced as the application providing model orchestration, monitoring, and deployment capabilities.
Operational ownership appears centered in asset protection and self checkout operations, with cross functional involvement from store operations and data teams for model validation and investigation workflows. Governance and rollout practices implied by the public discussions include pilot governance, staged deployment to test stores, and operational processes for alerts and model retraining driven by feedback loops. The public record indicates trials and collaboration rather than a broadly scaled production rollout.
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QuikTrip | Retail | 24034 | $10.5B | United States | RadiusAI | RadiusAI | ML and Data Science Platforms | 2023 | n/a |
In 2023, QuikTrip implemented RadiusAI as part of its ML and Data Science Platforms experimentation to pilot AI assisted checkout and in store vision analytics across select U.S. locations. RadiusAI was deployed in pilot form emphasizing ShopAssist AI assisted checkout and camera based analytics modules to target checkout efficiency, shrink prevention, and store operations visibility.
Pilot deployments focused on configuring the ShopAssist AI assisted checkout capability to surface real time assistance for clerks and expedite transaction flows, while camera based analytics were configured to detect operational patterns relevant to shrink and customer traffic. Configuration efforts emphasized model scoring, alerting, and per store tuning of detection thresholds rather than broad systems replacement, aligning analytic outputs with store level operational workflows. RadiusAI deployments used modular configuration to adjust assistance logic and detection sensitivity during the pilot period.
Operational scope was limited to in store pilots in the United States, with governance organized between store operations, loss prevention, and frontline teams to monitor model behavior and adjust parameters. Rollout was staged as pilot tests with iterative tuning based on observed checkout throughput and operational visibility metrics.
Reported outcomes from vendor materials and joint conference presentations highlight reduced checkout time and improved operational visibility, with RadiusAI vendor claims of 25 to 50 percent time savings per employee. These vendor reported pilot outcomes informed ongoing evaluation for potential broader adoption across additional stores.
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Buyer Intent: Companies Evaluating RadiusAI
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