List of WhyLabs AI Control Center Customers
Seattle, 98103, WA,
United States
Since 2010, our global team of researchers has been studying WhyLabs AI Control Center 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 WhyLabs AI Control Center for AI Model Deployment and Monitoring 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 WhyLabs AI Control Center for AI Model Deployment and Monitoring include: Regions Bank, a United States based Banking and Financial Services organisation with 19969 employees and revenues of $7.53 billion, Glassdoor, a United States based Professional Services organisation with 1500 employees and revenues of $350.0 million, Yoodli, a United States based Professional Services organisation with 50 employees and revenues of $10.0 million and many others.
Contact us if you need a completed and verified list of companies using WhyLabs AI Control Center, 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 WhyLabs AI Control Center 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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Glassdoor | Professional Services | 1500 | $350M | United States | Whylabs | WhyLabs AI Control Center | AI Model Deployment and Monitoring | 2023 | n/a |
In 2023, Glassdoor deployed WhyLabs AI Control Center to instrument and monitor its salary-estimate machine learning model within a US-based real-time prediction service. The work targeted AI Model Deployment and Monitoring for production inference telemetry and continuous observability of model inputs and outputs.
The implementation integrated the whylogs logging library together with the WhyLabs Observability Platform, configuring WhyLabs AI Control Center to collect feature-level telemetry, structured whylogs artifacts, and lightweight in-process profiling directly in the inference path. The configuration emphasized low-overhead client-side logging and pipeline metric collection to support distribution and schema monitoring alongside runtime profiling.
Instrumentation was embedded in the production prediction stack to reduce external sampling latency and to enable profiling during inference, outcomes explicitly reported as reduced monitoring latency overhead and enabling in-process profiling. Operational scope covered Glassdoors salary-estimate model in the US real-time service, and governance centered on production monitoring workflows, alerting, and continuous telemetry collection to support operational troubleshooting and model observability.
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Regions Bank | Banking and Financial Services | 19969 | $7.5B | United States | Whylabs | WhyLabs AI Control Center | AI Model Deployment and Monitoring | 2024 | n/a |
In 2024, Regions Bank deployed WhyLabs AI Control Center to monitor production ML and LLM applications. Regions Bank implemented WhyLabs AI Control Center as part of its AI Model Deployment and Monitoring capabilities to support model risk management in banking.
The implementation leverages the WhyLabs AI Control Center observability suite, configured for real-time monitoring, drift detection, security safeguards, reliability checks, and alerting workflows. Configuration included telemetry collection and baseline profiling, anomaly detection for data and model drift, and centralized dashboards to surface incidents for investigation.
Operational coverage focuses on production ML and LLM applications and extends to model owners, data science, compliance, and model risk management teams, centralizing telemetry and incident workflows to support governance and audit trails. The deployment instruments model observability pipelines and triage dashboards to accelerate detection and resolution of AI incidents in live systems.
Governance changes emphasize AI-specific monitoring and safeguards where traditional observability platforms are insufficient, aligning monitoring outputs with model risk management processes. WhyLabs AI Control Center is used to provide continuous surveillance of model behavior and to support regulatory and operational controls for AI in banking.
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Yoodli | Professional Services | 50 | $10M | United States | Whylabs | WhyLabs AI Control Center | AI Model Deployment and Monitoring | 2024 | n/a |
In 2024, Yoodli implemented WhyLabs AI Control Center to add real-time safeguards and monitoring for its LLM-based speech-coaching application in the United States. The WhyLabs AI Control Center was deployed as a control plane aligned to the AI Model Deployment and Monitoring category, instrumenting inference traffic and telemetry streams to observe model outputs in near real time. The deployment architecture emphasized runtime observability and safety, routing model responses into the WhyLabs control plane for anomaly detection and policy enforcement.
Functional modules implemented included real-time LLM safeguards, output monitoring, anomaly and drift detection, and alerting that product and engineering teams used to accelerate prompt iteration and feature releases. Operational coverage focused on Yoodli's product and engineering workflows for the speech-coaching application in the US market, with monitoring data feeding into prompt testing and rollback processes to improve safety and user experience. Governance shifted to embed model observability into deployment workflows, using WhyLabs AI Control Center telemetry to close the loop between incidents and prompt adjustments.
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