Michael Hoefert

Built over 7+ months as a solo founder project, the real complexity lives in the closed feedback loop the platform creates. Every graded call feeds a "maverick" detection layer that surfaces wins where reps broke the playbook and still closed deals. Each maverick is analyzed by complex context-seeking LLM APIs to distinguish replicable techniques from deal-specific context, then verified by the manager. The manager verification stage is crucial; it provides a human-in-the-loop verification gate but also allows managers to add commentary on why or why they are not verifying a call. The manager commentary is then stored and used to power the self-learning maverick detection engine so that over time, the detections are more aligned with what the manager wants. Once enough verified mavericks accumulate, a correlation engine cross-references bypass frequency against historical coaching session records, and when a criterion has been coached repeatedly but top performers keep skipping it in winning calls, the system classifies it as terminally flawed and flags it for removal in the next evolution proposal. That proposal renders as a side-by-side rubric comparison with per-change rationale and evidence strength, with the pre-computed frequency analysis injected as structured context. The result is a methodology that updates itself from the team's own winning patterns, and a system that can tell a manager not just that their reps aren't following the script, but that the script is wrong.