AI Service Quality
E-commerce customer support
In e-commerce customer support, companies can usually afford to review only a tiny fraction of their calls by hand. This fully automated AI system listens to recorded support calls, grades the agent’s performance objectively and detects how satisfied the customer really is, all without a post-call survey.
100%
of recorded calls evaluated automatically, not a spot-check sample
11
point rubric the AI uses to grade every call, from policy accuracy to resolution
1–10
Client Emotion score per call, measured without a survey
The situation
When an e-commerce customer calls about a defective product or a missing refund, tensions are high, and how the agent handles that moment decides brand loyalty. Traditional quality assurance relies on supervisors filling in subjective checklists, which makes it far too slow and expensive to evaluate every interaction.
Key challenges
- Supervisors cannot listen to thousands of hours of audio to check quality.
- Standard audits miss the link between an agent following the rules and the customer actually leaving happy.
- Email satisfaction surveys get low response rates and arrive late.
How it works
Transcribe every call
Each recorded support call is processed automatically and turned into accurate text with OpenAI Whisper, capturing the full conversation between agent and customer.
Audit with a strict rubric
Gemini 2.5 Flash acts as an impartial judge. A strict JSON schema and an 11-point rubric force it to return precise, quantifiable grades, from policy accuracy to problem resolution.
Read the customer’s emotion
The AI analyses the customer’s language and conversational cues to calculate a 1–10 Client Emotion score, measuring satisfaction at scale without waiting for surveys.
Visualise the results
Because the output is machine-readable JSON, it flows straight into pandas and a Power BI dashboard that combines procedural metrics with emotional outcomes.
The outcome
The business gained a 360-degree view of its support operation that was impossible to build by hand.
- Targeted coaching: management instantly sees top-performing agents and recurring communication failures.
- Real-time sentiment: customer emotion is tracked across the whole business.
- Scalable QA: slow, subjective spot-checks were replaced by continuous, data-driven evaluation.