AI Service Quality
AI & NLP | Dashboard | E-commerce
In e-commerce customer support, companies typically only have the resources to manually review a tiny fraction of their calls. This project eliminates that blind spot. It is a fully automated AI system that “listens” to recorded support calls, objectively grades the agent’s performance, and detects the customer’s true emotional satisfaction—all without ever needing a post-call survey.
Key features
Audio-to-text
11-Point rubric
360° Overview
The how and the results
To eliminate the bottleneck of manual quality assurance, a fully automated evaluation pipeline was architected in Python to act as a 24/7 digital supervisor. Here is how the system transforms unstructured audio into clear business intelligence:
1. Audio Ingestion & Transcription First, every recorded support call is automatically processed and transcribed into highly accurate text using OpenAI Whisper, capturing every nuance of the interaction between the agent and the customer.
2. Schema-Driven AI Auditing Next, the AI steps in as an impartial judge. Rather than just generating a basic text summary, Gemini 2.5 Flash is constrained by a strict JSON schema and an 11-point evaluation rubric. This forces the model to act as a data-driven auditor, outputting precise, quantifiable grades on everything from policy accuracy to problem resolution.
3. Reading the Customer’s Emotion Beyond ensuring the agent followed procedural rules, the system reads between the lines. The AI analyzes the customer’s natural language and conversational cues to calculate a 1-10 “Client Emotion” score. This instantly measures true customer satisfaction at scale, bypassing the need for delayed, low-response email surveys.
4. Data Structuring & Visualization Finally, because Gemini’s output is strictly forced into machine-readable JSON, the resulting data is seamlessly parsed into pandas DataFrames and pushed directly into a dynamic Power BI dashboard.
The Results
By automating this entire flow, the business gained a 360-degree view of its support operations that was previously impossible to achieve manually:
Targeted Coaching: Management can now instantly identify their top-performing agents and pinpoint recurring communication failures.
Real-Time Sentiment: Customer emotion is tracked across the entire business at scale.
Scalable QA: Slow, subjective manual spot-checks were entirely replaced with continuous, data-driven evaluations.
Background
When an e-commerce customer calls about a defective product or a missing refund, tensions are high. Ensuring agents handle these high-friction moments correctly is critical for brand loyalty.
However, traditional quality assurance relies on human supervisors filling out subjective checklists, making it impossibly slow and expensive to evaluate every interaction. This system was built to solve that exact problem: giving management a 100% automated, unbiased view of how their team is performing and how their customers actually feel.
Key Challenges:
Supervisors cannot manually listen to thousands of hours of audio to check for quality.
Standard audits often miss the link between an agent following the rules and the customer actually leaving happy.
Relying on email surveys to measure customer satisfaction results in low response rates and delayed feedback.