Scalable AI-Powered Voice Analytics for Sales Quality and Compliance

About project

An automated system that transcribes and analyzes sales calls, flags script violations and risks using NLP rules, and triggers workflows via n8n — improving call quality without manual QA overhead.

Challenge

U.S.-based B2B SaaS company with 130 employees and a 40-person sales team needed better visibility into call quality. Despite strong lead flow, sales leaders couldn’t efficiently monitor how reps followed scripts or handled key messaging.

Key pain points:

• No scalable way to verify compliance with required scripts or disclaimers
• Manual call reviews were random, time-consuming, and often inconclusive
• Difficulty resolving disputes or customer complaints without transcript evidence
• Lack of insight into rep communication trends and behavior patterns
• No structured feedback process to support coaching or performance improvement

The client wanted an automated QA layer to evaluate calls, surface risks, and drive action — without hiring a dedicated QA team.

Solution

Phase 1: Call Transcription and Tagging

We integrated with the client’s VoIP system and used Whisper API to transcribe calls within 5–10 minutes post-call. The system scaled to handle hundreds of daily recordings with minimal human oversight.

Phase 2: NLP-Based Scoring Engine

Using spaCy and Python, we built a rule-based analysis engine customized to the client’s sales scripts and tone-of-voice guidelines.

Each transcript was automatically scored for:

• Script Compliance: Was the introduction used? Did the rep mention required value points and ask for the close?
• Behavioral Cues: Hesitations, filler words, negative sentiment
• Risk Flags: Missing legal language, overselling, aggressive tone

Results were passed/fail with confidence scores. Edge cases or low-confidence transcripts were flagged for manual QA review.

Phase 3: Workflow Automation via n8n

Using self-hosted n8n, we configured automation flows that:

• Created CRM tasks for reps or managers when issues were flagged
• Sent severity-based alerts to team leads or QA staff
• Generated weekly summaries of flagged calls and rep activity
• Allowed operations staff to modify workflows without writing code

Sales Rep Feedback Layer

Each rep had a private dashboard displaying:

• Recent call scores and review tags
• Missed script elements
• Long-term trends and suggested improvements
• Optional gamified score (visible only to the rep)

Project Team

• 1 Project Manager
• 1 AI/NLP Engineer
• 1 Backend Developer
• 1 Automation Specialist (n8n)
• 1 QA Engineer
• 1 DevOps Engineer

Tech stack

Impact

After go-live, the system immediately improved call visibility and QA coverage:

• All sales calls were automatically transcribed and analyzed
• ~70% of detectable script and tone issues were flagged without manual review
• Call review time dropped from 5–6 minutes to under 1 minute for triage
• Sales managers saved 12–15 hours per week previously spent on ad hoc audits
• Compliance scores improved by ~35% in the first month, based on adherence trend data

Most calls are auto-scored, while edge cases are flagged for review — maintaining balance between automation and human judgment.

Business Outcome

The platform gave sales leadership real-time insights into communication quality, without expanding the QA team. Reps received structured, private feedback. Management gained scalable, repeatable coaching insights — resulting in stronger sales consistency, fewer compliance gaps, and better overall client experience.