The AI Sales Agent MVP used real call data to solve key sales problems and improve results.
We had over 100 million minutes of recorded sales calls across thousands of reps. I partnered with the data team to study what was actually happening in those conversations. Where reps stalled. Where buyers checked out. What triggered objections. We didn’t rely on dashboards. We listened.
We used Whisper for transcription and OpenAI models to surface early patterns. Claude Opus, though expensive, was a godsend. We weren’t chasing perfection, just enough signal, fast enough to build.
That gave us the raw input. We mapped it to training tasks and built our first agent.
The first version did what it needed to. It answered calls, followed a script, and handled simple objections. We didn’t try to boil the ocean. We focused on high-volume, low-stakes calls like top-of-funnel lead capture and first touches.
After a week of research, I defined the requirements with engineering, stripped out anything that didn't serve that use case, and got it out for internal dogfooding within 3 weeks total.
We ran structured surveys across five verticals, sent demo calls, and watched who leaned in. Hard Money Lending stood out immediately. They had compliance needs, heavy phone usage, and lean teams that couldn't scale reps.
I helped design the survey, ran working sessions with sales and CS, and pushed for a vertical-first go-to-market instead of a generic pitch.
Hard Money Lending showed 3x higher engagement than any other vertical in our structured surveys.
The first calls were brutal. Dead air. Clunky phrasing. Missed cues. The hours were long, but the energy was real. Everyone wanted to ship. We watched every call, flagged what broke, and fixed it fast.
I ran the feedback loop directly. No middle layers. PM to engineer to call review to fix.
Weekly iteration cycles based on real call analysis drove measurable improvements across all key metrics.
We tried to serve five verticals at once. It didn't work. The scripts got watered down. The model got noisy. The product felt unfocused.
I pushed for the pivot. Got alignment with GTM and leadership. We dropped the rest and focused on lending. Rewrote the flows from real lender calls. Trained on sharper data. The difference showed up fast.
We turned repetitive, error‑prone first‑touch calls into consistent outcomes. Mined 100M+ minutes, picked one job, and built only what moved the metric.
Shipped the MVP in 3 weeks… then iterated weekly from real calls. Completion 60→85%. Objection handling 10→40%. Sentiment accuracy 3.2×. Not magic. Focus, data, tight loops.
Closed alpha with select customers.
One narrow use case. Real usage. Weekly improvement. If you have volume and a clear problem, I’ll help you scope, ship in ~3 weeks, and iterate from calls.
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