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.
âś… Accuracy Improvement: Elevates detection accuracy from 96% (manual review) to 99% (AI-powered), ensuring near-human performance.
âś… Cost Reduction: Cuts QA costs by over 80%.
âś… Enhanced Compliance: Achieves 100% call coverage compared to the previous 10%, ensuring no policy breach goes undetected.
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 company, 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.Ensuring each sales call adhered to 18 detailed internal guidelines was labor-intensive and error-prone.
- Risked human error and subjectivity.Human QA accuracy hovered around 96%, with the risk of subjectivity and oversight.
- Provided limited scalability as call volumes grew. Only 10% of all calls were reviewed by a team of five QA experts.Manual reviews led to missed policy breaches and inconsistent quality.
The client also experimented with out-of-the-box solutions to generate transcripts from mp3 recordings for subsequent automated evaluations. However, these systems proved to be both expensive and insufficiently customizable to meet their specific needs, ultimately necessitating additional manual re-verification.
Project Goals
Improve Call Monitoring Efficiency
​​The goal was to expand automated analysis from a mere 10% to 90%-100% automated evaluation. Thus, human reviewers would be able 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, Character Error Rate (CER), Sentence Error Rate (SER))
- 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
- Our team rigorously evaluated and measured each model's effectiveness to ensure it delivers optimal transcription accuracy and cost efficiency.
- 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
- x10
Test Coverage Increase
- >80%
QA Cost Reduction
- 99%
AI Test Accuracy
vs. Human 96% - 9m
months ROI
- 500%
Increase of QA capacity
All about the ROI
Automating Sales QA services has proven beneficial after about 8 months. If we consider the client's plans of expanding their team of human testers to enhance test coverage for potential company scaling, the return on investment (ROI) could be realized in roughly 3-4 months.
Why This Matters For The Client
Reduces operational costs and streamlines QA - Our solution achieved an 80%+ reduction in QA costs enabling confident scaling of operations.
Achieves uniform service quality, a crucial factor for brand reputation in the competitive EdTech space. Coverage increased significantly – from 10% to 100% monitoring, surpassing human accuracy (99% accuracy vs. human 96% accuracy).
Improves customer satisfaction by quickly spotting and correcting rep errors before they impact broader clientele.