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Case Study — The one that listens
Sales reps were making hundreds of calls with zero visibility. We built an AI that listens to every conversation, extracts the signal from the noise, and tells you exactly why deals close — or don't.
The Problem
Sales teams were drowning in conversations and starving for insights. Every call was a black box — closed or lost, nobody knew why. We were handed a mandate: make every call a data point.
Sales teams were making hundreds of calls weekly with zero visibility into what worked, what didn't, and where deals were lost.
Reps spent 30+ minutes per call documenting notes instead of selling. Critical insights were buried in handwritten scribbles.
Managers had no systematic way to identify patterns, coach reps, or replicate winning behaviors across the team.
What We Built
A real-time command center showing active calls, sentiment trends, and instant alerts when a call goes south. Managers can jump in before a deal dies.
Reduced deal loss rate by 34%.
Every call is automatically tagged by topic, sentiment, and outcome. Reps can search 'price objections last week' and get instant, relevant clips.
10K+ calls indexed monthly.
Individual performance dashboards tracking talk ratio, interruption frequency, question quality, and sentiment control. Gamified to make coaching feel less like a performance review.
92% rep adoption within 30 days.
The system learns from top performers and generates dynamic playbooks: 'When a customer mentions competitor X, try response Y.' It's like having your best closer whispering in your ear.
28% improvement in win rates.
Design Solutions
AI-powered speech-to-text converts every call into searchable, timestamped transcripts within seconds of completion.
NLP models track emotional tone throughout the conversation, flagging moments of friction, excitement, or disengagement.
Automatically identifies common objections (price, timing, competitors) and surfaces the best-performing rebuttals.
Managers get AI-generated call summaries with talk-ratio analysis, key moments, and personalized coaching recommendations.
Design Process
Lessons Learned
Reps won't adopt AI coaching tools if they feel like surveillance. We designed for transparency — reps see their own insights first, managers see aggregated trends.
Transcribing every call is table stakes. The real value is extracting 3 actionable insights from a 30-minute conversation, not 30 pages of text.
The product succeeded because it positioned as a coaching tool that helps reps win, not a monitoring tool that catches them failing. Framing changed everything.
Got a product that needs this level of care?