List of Mona Labs AI Customers
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United States
Since 2010, our global team of researchers has been studying Mona Labs AI 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 Mona Labs AI 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 Mona Labs AI for AI Model Deployment and Monitoring include: Fiverr, a Israel based Professional Services organisation with 775 employees and revenues of $392.0 million, Gong, a United States based Professional Services organisation with 1100 employees and revenues of $115.0 million, Hyro, a United States based Professional Services organisation with 90 employees and revenues of $10.0 million and many others.
Contact us if you need a completed and verified list of companies using Mona Labs AI, 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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Fiverr | Professional Services | 775 | $392M | Israel | mona | Mona Labs AI | AI Model Deployment and Monitoring | 2022 | n/a |
In 2022, Fiverr deployed Mona Labs AI in the AI Model Deployment and Monitoring category to instrument production machine learning across its marketplace, supporting product and marketing teams with continuous observability. The deployment targeted dozens of production models including search and ranking, recommendations, customer lifetime value scoring, and click-through rate models, with monitoring applied directly to inferencing pipelines and model output streams.
Mona Labs AI was configured to detect data drift and model quality issues, using flexible custom metrics and segmentations to align signals with marketplace KPIs. Monitoring capabilities emphasized automated anomaly detection, custom metric instrumentation, and segmented alerting, enabling model owners to surface distribution shifts and performance regressions tied to business outcomes.
Operational rollout expanded monitoring coverage to 15 model use-cases within a year, reflecting a broadening of scope from ranking and recommendations into customer lifetime value and engagement models. The implementation supported cross-functional teams, with observability feeds and segmented metrics consumed by product analytics and marketing operations for incident triage and model performance investigation.
Governance centered on measurable observability, with Mona Labs AI custom metrics and segmentation used to map model signals to marketplace KPIs and to prioritize investigations by business impact. The stated outcome was improved model observability and earlier detection of data drift and model issues that could affect Fiverr marketplace KPIs.
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Gong | Professional Services | 1100 | $115M | United States | mona | Mona Labs AI | AI Model Deployment and Monitoring | 2021 | n/a |
In 2021, Gong implemented Mona Labs AI as its AI Model Deployment and Monitoring platform to oversee production AI models for transcription, speaker separation, NLP topic extraction and intent detection. The deployment instruments inference endpoints and telemetry pipelines to support Gong's revenue intelligence and CRM use cases across regions, enabling continuous model health tracking.
Mona Labs AI was configured to track functional modules for transcription accuracy, speaker separation fidelity, topic and intent detection performance, and drift indicators, with automated alerting when anomalies occur. The architecture leverages model instrumentation, telemetry ingestion, and shadow deployment comparisons to contrast candidate and production model outputs without impacting live traffic. Configurations include automated retraining triggers tied to drift thresholds and operational dashboards for engineering and data science teams.
Governance and operationalization centered on unified dashboards, real time alerts and shadow deployment comparisons so teams catch issues before customers are impacted, according to Mona's case study. The implementation has been in multi year use, indicating sustained monitoring and periodic retraining workflows across Gong’s regional operations. This implementation aligned Mona Labs AI with Gong revenue intelligence and CRM business functions, enabling proactive detection of model degradation and automated operational responses.
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Hyro | Professional Services | 90 | $10M | United States | mona | Mona Labs AI | AI Model Deployment and Monitoring | 2023 | n/a |
In 2023, Hyro implemented Mona Labs AI to establish runtime observability for its conversational agents within the AI Model Deployment and Monitoring category. Mona Labs AI was deployed to monitor GPT and other conversational model endpoints used in Hyro deployments, providing continuous telemetry on model inputs, outputs, and token usage.
The implementation centered on Mona’s GPT monitoring module, configured to detect anomalous token consumption, surface prompt inefficiencies, and flag mixed-language interactions. Configuration included alerting thresholds for token anomalies, dashboards for prompt-level diagnostics, and instrumentation of conversational turn metadata to correlate prompts with downstream behavior.
Mona Labs AI was connected to Hyro’s conversational model endpoints and runtime traffic streams supporting customer support and healthcare deployments, enabling visibility across deployed contact center automation flows. Monitoring was applied at the model inference layer and integrated with Hyro’s operational telemetry so that anomalous requests and language-mix events could be traced back to specific conversation flows.
Operationally, the rollout established monitoring-driven triage workflows and alerting for prompt engineering and cost oversight, enabling engineering and operations teams to prioritize fixes. The monitoring surfaced anomalous token usage and prompt inefficiencies that Hyro used to reduce costs and improve user experience, while improving reliability in customer support and healthcare deployments.
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