AI Sales QA Tool đź”’
Automation of the quality assurance process for sales calls by detecting policy violations, flagging errors, and delivering actionable insights - thus improving compliance, brand consistency, and training efficiency.
The Challenge
In an industry where customer conversations can make or break a sale, ensuring consistently high-quality interactions is critical. Our client, a leading EdTech firm, wanted a powerful yet automated way to verify their sales calls’ compliance with internal guidelines - covering everything from price disclosure to age requirements and legal disclaimers.
Before our involvement, the client relied heavily on manual QA processes. This approach:
- Required time-intensive call reviews by QA personnel.
- Risked human error and subjectivity.
- Provided limited scalability as call volumes grew.
Project Goals
Improve Call Monitoring Efficiency
Automate the QA process, freeing human reviewers to focus on critical edge cases or advanced coaching.
Enhance Accuracy & Consistency
Deliver near-human-level detection of policy breaches while reducing overlooked or misclassified calls.
Provide Actionable Insights
Flag specific errors (e.g., quoting the wrong price or skipping essential disclaimers) and generate targeted recommendations for improvement.
Key Requirements
Our Approach
Comparative Model Testing
We evaluated four different speech-to-text and classification models, assessing:- Transcription Quality (Word Error Rate)
- Contextual Comprehension (handling domain-specific terminology, brand references)
- Robustness against background noise or varied accents
This multi-model approach allowed us to select the optimal pipeline for the client’s environment.
Rule-Based + AI Hybrid
While we leveraged advanced AI classification for nuanced conversation contexts, we combined it with a rule-based layer to capture explicit triggers:- Exact Phrases
- Policy-Specific Markers
This hybrid approach reduced the chance of missing straightforward red flags or incorrectly categorizing calls where the conversation closely mirrored a known scenario.
End-to-End Call Analysis Flow
- Audio Input
- Sales calls are recorded in real-time.
- Transcription
- Selected best-in-class speech-to-text model processes audio, generating a transcript.
- Policy Violation Detection
- AI classifier + rule-based checks parse the transcript, identifying any deviation from the 18 internal guidelines (e.g., offering courses below age 6, misquoting pricing, failing to mention required disclaimers).
- Scoring & Reporting
- Each call receives an accuracy score, and flagged segments are highlighted. A structured report pinpoints any policy breaches, along with recommended next steps.
- Audio Input
Key AI Techniques & Highlights
High-Accuracy Transcription Pipeline
We systematically compared four STT (Speech-to-Text) solutions, focusing on domain adaptation for education and sales.
Domain-Specific Language Model
Custom dictionaries and post-processing rules handle brand-specific terms, typical questions, and competitor mentions.
Contextual Classification
Our system uses ML classifiers to gauge context - was “free” used in a correct or violating scenario? Did the rep mention “PESEL” or “ID” requirements for the right target audience?
Self-Validation Against QA
We tested the system’s outputs against a human QA reference set. In one instance, the AI identified an error that human QA had incorrectly labeled as “passing” - resulting in an impressive “1:0 for AI.”
Handling False Positives
We discovered one false positive triggered by a transcription error. With minor refinements to domain-specific synonyms, we swiftly corrected the model - fully eliminating that error class in subsequent tests.
Results & Impact
Why This Matters For The Client
Achieves uniform service quality, a crucial factor for brand reputation in the competitive EdTech space.
Reduces operational costs and streamlines QA - enabling the team to scale confidently.
Improves customer satisfaction by quickly spotting and correcting rep errors before they impact broader clientele.