Designing an AI Sales-Coaching Loop

Designing an AI Sales-Coaching Loop

Connecting pre-call preparation, deliberate practice, and post-call learning in one AI-assisted product experience.

ROLE AND CONTEXT Product Designer | AI sales coaching web application | 2025

AmpUp was designed to help sales teams capture useful patterns and support representatives before and after important conversations. The published product model contains three connected flows: Pre-Call Intelligence, Practice Partner, and Post-Call Analysis.

THE PROBLEM Sales coaching often arrives too late, depends heavily on manager capacity, and becomes disconnected from the next live conversation. AI can increase coverage, but generic or unexplained advice is difficult to trust and even harder to apply.

PRODUCT STRUCTURE • Pre-Call Intelligence helps a representative prepare with relevant account and conversation context. • Practice Partner creates a space to rehearse difficult moments before the call. • Post-Call Analysis connects the transcript, key moments, coaching feedback, and follow-up actions.

KEY DECISIONS • Organize the product around the selling rhythm instead of a standalone analytics dashboard. • Make coaching specific to the conversation and visible source context. • Keep recommendations connected to transcripts and key moments. • Support practice before a live call rather than limiting coaching to retrospective analysis. • Treat manager review, permissions, privacy, and correction as product behaviour rather than secondary settings.

AI TRUST CONSIDERATIONS The design direction considers what happens when transcription is incomplete, speakers are misidentified, recommendations lack enough evidence, CRM data conflicts, or a suggested action should require human approval. These considerations are presented as design requirements and future safeguards, not as claims that every state shipped.

OUTCOME AND EVIDENCE NOTE This case study demonstrates the product model and interaction direction. Large conversion, ramp-time, coverage, revenue, and time-saved numbers are intentionally excluded because their methodology and my individual contribution have not been verified.

Full case study: https://savitasingh.vercel.app/case-studies/ampup