AI Sales QA Tool

Quick Project Overview

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.

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.

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.

Key Requirements

Speech-to-Text Accuracy

Calls could contain varying accents, speech speeds, and background noise. Achieving high transcription fidelity was paramount.

Human-Level QA Benchmark

The client required proof that the AI-driven system could match or exceed the accuracy of their existing QA team.

Complex Policy Framework

The client provided 18 distinct guidelines - covering everything from age restrictions and disclaimers to marketing consents and pricing. The system had to accurately identify any violation.

Minimizing False Positives & Negatives

Achieving high recall (detecting real errors) and high precision (avoiding false alarms) was critical to user trust.

Strategic advantage

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

Exact Phrases

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.

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

Human-Level Accuracy

Side-by-side testing showed that on provided call sample, the AI not only matched QA performance but managed to outperform it through identifying policy violations in overlooked by QA (false-negative) and captured cases where QA wrongly marked fulfilled policy (false-positive).

Scalable Insights

The system aggregates errors into high-level reports - revealing patterns (e.g., repeated mention of wrong age limit or missed disclaimers) that inform targeted training programs.

Reduced QA Time & Cost

By automating the bulk of call analysis, manual review hours are dramatically reduced, allowing the QA team to focus on complex issues and targeted coaching. It showed potential for covering 100% of the calls in given time and cost.

Consistent Branding & Compliance

The AI solution enforces a uniform standard, ensuring all reps follow the same guidelines for pricing disclosure, disclaimers, and marketing consents.

x10

Test Coverage Increase

>80%

QA Cost Reduction

99%

AI Test Accuracy

Test Coverage Increase

500%

Test Coverage Increase

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.

Want to know how fast such an investment will pay off in your use case?

Get a free estimate

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.

80% less manual input thanks to smart data sync across CRMs

Quick Project Overview

Project Goals

The Challenge

Key Requirements

Our Approach

Key AI Techniques & Highlights

Results & Impact

All about the ROI

Want to know how fast such an investment will pay off in your use case?

Why This Matters For The Client

Looking to build a similar AI-powered sales support system?