1 link tagged with all of: cost efficiency + ai agents + call structure prediction + classification + latency
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Researchers tested an AI system called Jev on 2,029 real phone calls stripped down to pure call structure—no audio or transcripts—and it learned to predict which calls would result in bookings with 94% accuracy by the end, all for $3 in compute costs.
- Jev made 38,012 turn-level forecasts across the calls at 118ms median latency, processing thousands of calls in seconds without any fine-tuning or examples from the company's data.
- The system achieved 0.78 AUC by the halfway point and reached 94% ranking accuracy near the end, learning to separate bookable from non-bookable calls based solely on call structure, timing, and workflow patterns.
- The experiment revealed the AI could extract predictive signal from call metadata alone—turns, tool calls, workflow stages—demonstrating that meaningful patterns exist independent of what's actually said.