Sales Call Compliance AI: Every Call Reviewed, Automatically
An AI system that transcribes, diarizes, and scores every sales call against a compliance rubric. Replaced manual QA that covered 5% of calls with automated analysis of 100%.
The Problem
The client's sales team handled hundreds of calls per week. Their compliance team manually reviewed about 5% of them. That 95% gap wasn't a staffing issue. It was math: a human reviewer takes 20-30 minutes to properly audit a 15-minute call against a compliance checklist. You can't hire enough reviewers to cover everything without the cost becoming absurd.
Compliance issues were getting missed. Calls that should have included specific disclosures, consent language, and product explanations often didn't. The team only found out during audits or, worse, customer complaints.
What We Built
An end-to-end pipeline that automatically reviews every sales call for compliance:
- Audio ingestion via webhook from the client's call recording platform
- Transcription using Deepgram Nova-2 (switched from Whisper after testing on noisy call audio)
- Speaker diarization to isolate rep speech from customer speech
- LLM compliance scoring against a structured rubric with per-section pass/fail and reasoning
- Dashboard showing scores, trends, and specific failure explanations per call
Key insight: The hardest part wasn't the AI. It was getting the client to write down exactly what "compliant" means. That took two days of workshops before we wrote a single line of pipeline code. Without a precise rubric, no model can score compliance accurately.
Technical Decisions
Why Deepgram over Whisper: Whisper large-v3 hit ~4% word error rate on clean audio, but degraded to ~18% on noisy call recordings (speakerphone, mobile). Deepgram Nova-2 held at ~6% WER across quality levels and processed audio at 400ms per minute versus 3.2 seconds for Whisper. At 200+ calls per day, that gap compounds.
Why LLM scoring over keyword matching: Our first prototype used keyword detection. It scored 58% accuracy. Compliance isn't about exact phrases. A rep saying "just so you know, we record these calls" is compliant even though it doesn't match the script verbatim. LLM analysis against a structured rubric reached 94% agreement with human reviewers.
Scoring approach: GPT-4o evaluates each compliance section independently, returning a pass/fail plus a 2-3 sentence explanation. The per-section breakdown gives managers actionable coaching feedback, not just a single number.
Tech Stack
Results
After six weeks in production:
- 40% improvement in overall sales compliance scores across the team
- 95% reduction in QA review cost (humans now only review flagged edge cases)
- 100% call coverage versus 5% before. Every call gets reviewed, not a sample
- Same-day feedback instead of the 2-3 week delay of manual review cycles
The compliance team shifted from reviewing calls to reviewing the AI's edge-case flags and coaching reps based on the AI's per-section breakdowns. That's a better use of their time.
Read the Full Build Story
We wrote a detailed technical walkthrough of this project, including both wrong turns we took before landing on the final architecture: How We Built a Sales Call Compliance AI in 2 Weeks.
Technical Deep Dives
How we think about the problem, the tech trade-offs we made, and what we'd do differently. Written by the engineers who shipped it.
Sales Call Compliance AI: 5 Architecture Choices
The 5 architecture decisions that determine what your compliance AI costs and whether it holds up in production. Numbers from a build we shipped.
How We Built Our Call Compliance AI: 5 Decisions
5 decisions that shaped our sales call compliance AI, from rubric design to dashboard UX. What the client said in our retrospective.
Agentic AI in Production: Tool-Calling, Planning, Recovery
Tool schemas, planning loops, and error recovery for production AI agents. Six deployed systems, real failure data, and the patterns that actually hold.
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