Scoring Sales Calls at Scale With AI

Sales managers today are overwhelmed by the sheer volume of rep calls — making it impossible to review each one, let alone provide timely, consistent coaching. Manual review processes are time-consuming, subjective, and reactive. Existing tools provide call data, but lack the precision, customization, and contextual awareness needed to drive real performance improvement across the team.

Our team designed the system to plug directly into Gong, ingesting calls as they happen. We built a layered evaluation workflow using multiple AI agents, each responsible for a different rubric category (e.g. rapport building, discovery quality, objection handling). To safeguard accuracy, we layered in human sentiment review and feedback loops. All data was routed into a secure analytics suite built for real-time visibility and longitudinal tracking. We worked closely with client stakeholders to define the rubric, train the models, and tune alerting thresholds for when human review should kick in — ensuring adoption and trust in the system from day one.

We built a fully integrated call analysis platform that uses AI to listen, evaluate, and score every call against a standardized rubric. Each transcript passes through a multi-step review pipeline, powered by AI agents with deep knowledge of the client’s product, audience, and goals. Managers are alerted to low-quality calls, can provide manual review and feedback, and access real-time dashboards that track team- and rep-level performance metrics. The model continuously improves based on human-in-the-loop feedback — making every coaching cycle smarter and more scalable.