List of Rackspace Artificial Intelligence & Machine Learning Customers
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Since 2010, our global team of researchers has been studying Rackspace Artificial Intelligence & Machine Learning 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 Rackspace Artificial Intelligence & Machine Learning for Professional Services 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 Rackspace Artificial Intelligence & Machine Learning for Professional Services include: Sentrics, a United States based Professional Services organisation with 200 employees and revenues of $25.0 million, Brave, a United States based Professional Services organisation with 210 employees and revenues of $24.0 million and many others.
Contact us if you need a completed and verified list of companies using Rackspace Artificial Intelligence & Machine Learning, 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.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Brave | Professional Services | 210 | $24M | United States | Rackspace Technology | Rackspace Artificial Intelligence & Machine Learning | Professional Services | 2021 | Onica by Rackspace Technology |
In 2021, Brave implemented Rackspace Artificial Intelligence & Machine Learning to operationalize its browser advertising and website classification models, aligning machine learning workflows with its privacy-first browser strategy. Brave, a United States based professional services company with approximately 210 employees, targeted automated model deployment to support 30 million monthly active users and more than 1 million verified publishers within the browser advertising stack.
The implementation leveraged Onica by Rackspace Technology and the Rackspace Artificial Intelligence & Machine Learning offering to introduce MLOps Foundations pipelines that standardized model development, training and deployment. Functional capabilities implemented included CI/CD for models, automated training orchestration, model approval and QA gates, and optimized model packaging to enable on-device inference while preserving user privacy.
The solution integrated explicitly with AWS platform services, using AWS SageMaker for model training, AWS Lambda to invoke Jenkins endpoints, AWS CodePipeline for CI/CD orchestration, AWS CodeCommit for source control, AWS Systems Manager for perimeter storage, and Amazon S3 for training data storage. The engagement used AWS Jumpstart funding and Onica’s MLOps Foundations architecture pattern to ensure traceable artifact promotion from training to production.
Operational scope covered Brave’s data science and DevOps teams, including two data scientists and a DevOps specialist, with Onica training Brave staff on the MLOps framework and deployment troubleshooting. Deployment governance introduced a notification and approval workflow so model training metrics are reviewed and accepted or rejected before production rollout, and the project adapted midstream to rearchitect an NLP model for efficient cloud training under a six week schedule.
Outcomes reported by Brave were reduced model training time from several days to six hours, streamlined deployment that moved from multi-day processes to a few hours, and a 50 percent reduction in infrastructure costs through use of AWS spot instances. The implementation impacted advertising prediction workflows, browser performance modeling and publisher monetization pipelines while preserving Brave’s privacy-oriented architecture.
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Sentrics | Professional Services | 200 | $25M | United States | Rackspace Technology | Rackspace Artificial Intelligence & Machine Learning | Professional Services | 2018 | n/a |
In 2018, Sentrics implemented Rackspace Artificial Intelligence & Machine Learning to embed voice-control into its Engage360 infotainment application, a Professional Services use case focused on resident engagement in senior care facilities. The initiative targeted touch-free communication capabilities for assisted-living residents and positioned voice interaction as the primary accessibility mechanism for the application.
The implementation centered on conversational AI via AWS Lex integrated into Rackspace Artificial Intelligence & Machine Learning workflows, delivering a chatbot-driven voice interface that surfaces community activity menus, television and movie listings, dining options, and basic transactional capabilities such as ordering. Functional modules included intent and slot modeling for natural language understanding, automatic speech recognition pipelines, and a service layer exposing chatbot features through a RESTful API, with team efforts focused on training intents and value extraction to improve conversational accuracy.
The solution architecture relied on AWS platform services that Onica and Rackspace Technology used to operationalize the chatbot, including Amazon API Gateway to expose the API, Amazon S3 for audio storage, AWS Lambda for serverless execution, CloudWatch Logs for centralized logging, AWS CodeCommit for source control, and AWS CloudFormation for automated deployments. Integration points explicitly included the television infotainment endpoints in senior residences, and Onica built the underlying API and chatbot backend to connect those endpoints to AWS Lex and the supporting services.
Project governance followed an agile scrum delivery model provided by Onica and Rackspace Technology, with a team composition of developers, data scientists, and a scrum master, and a four-phase rollout plan accelerated to meet pandemic-driven urgency. The engagement delivered an accelerated time-to-market outcome, with Onica completing phase one and advancing through subsequent phases within the compressed six-week schedule and delivering the foundational voice application within the stated rapid timeframe.
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