→ Leads the team managing the full lifecycle of Gong’s ML models
→ Analyzes model performance across languages, customer segments, and platforms
→ Needed monitoring to continuously learn from production data and optimize models over time
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Revenue Intelligence / Conversational AI
Manual monitoring
couldn’t scale
Proactive, segmented monitoring with Mona
Gong's teams now detect issues early—before users ever notice
Gong.io is the leader in Revenue Intelligence on a mission to empower companies to unlock their full revenue potential by turning sales conversations into actionable insights. Their Reality Platform™ helps customer-facing teams unlock insights from real-world interactions—calls, meetings, and emails—using AI models for transcription, speech recognition, and NLP.
Monitoring so many moving pieces—across different data sources, languages, and customer segments—required more than QA and support tickets. So Gong turned to Mona Labs for a smarter, scalable solution.
Gong’s Engineering Lead Yaniv Levi focused on keeping models reliable in production, while Research Lead Noam Lotner monitored model behavior across regions and customer segments. Together, they adopted Mona to gain full visibility and control over their AI systems.
→ Leads the team managing the full lifecycle of Gong’s ML models
→ Analyzes model performance across languages, customer segments, and platforms
→ Needed monitoring to continuously learn from production data and optimize models over time
→ Oversees the system that runs Gong’s production AI models
→ Responsible for maintaining performance and stability at scale
→ Needed real-time insight into model behavior—and a safer way to roll out changes
As Gong grew—adding more customers, more languages, and new communication channels—their model ecosystem became more complex. Monitoring them manually, as they had in the past, was no longer sustainable.
The team began to face major challenges:
Limited confidence when releasing new models
Hidden drift from new user behavior, languages, or data
Delayed responses due to lack of proactive alerting
No performance segmentation by customer or geography
Gong’s team monitors every model—from transcription to NLP—in one unified platform, ensuring nothing falls through the cracks.
Mona automatically notifies the team when prediction quality dips, enabling fast, informed responses.
Instead of guesswork, Gong gets clear context—what changed, where, and how it’s impacting model performance.
Each team member tracks what matters most to their models, with personalized alerts, views, and benchmarks.
New models run alongside production in “stealth mode,” giving Gong insight into how they’ll perform—without user risk.
Performance is tracked across key dimensions, helping Gong pinpoint where and why issues arise—before they scale.
Speaker Identification
Mona tracks how well Gong’s AI distinguishes speakers on a call—crucial for segmenting seller vs. prospect dialogue.
NLP Classification
Gong uses Mona to monitor NLP model accuracy and ensure consistent tagging of conversation topics and intent across languages.
Shadow Deployments
Mona runs experimental models alongside production to compare performance—before customers are impacted.
Gong detects model problems early, preventing surprises and protecting user experience.
Each team monitors performance by language, customer, or channel—no more blind spots.
Experimental models are validated in real-world conditions—without production risk.
With Mona, Gong scales AI with trust, control, and operational peace of mind.
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