Answering Agent: Helped create a call score using AI

In short: Answering Agent had no visibility into the quality of its AI calls. We studied the product and industry benchmarks, defined a full event set, and built an AI-generated call score. They could finally measure call quality directly.

Answering Agent is an AI-powered calling platform that automates customer outreach, follow-ups, and support conversations. As an AI-first product, the quality of each call directly impacts customer satisfaction, product trust, and conversion outcomes. But without a clear way to evaluate call performance, the team struggled to understand how well the AI was functioning — and what needed improvement.

At a glance. Client: Answering Agent · Industry: AI voice · Result: AI-generated call scoring built

The core issue Answering Agent faced was the inability to evaluate whether an AI-generated call was “good” or “bad,” and more importantly, why.

This created multiple problems:

• The team couldn’t pinpoint weaknesses in the AI’s calling logic
• Product decisions were based on assumptions rather than data
• There was no standardized framework for evaluating calls
• Improving the AI became guesswork instead of a structured process

Without call-level insights, the product's evolution was limited.

To give the team clarity and actionable insights, we built an end-to-end evaluation framework for AI call quality.

1. Deep dive into the product & industry benchmarks

We studied:

• How the AI conducted calls
• Typical call flows and expected outcomes
• Industry standards for conversational AI performance
• Real-world call scenarios and edge cases

This groundwork enabled us to define what “good” looked like.

2. Defined a complete set of evaluation metrics

We created a structured metric system that captured dimensions such as:

• Call clarity and coherence
• Response relevance
• Latency and hesitation patterns
• Completion of the intended task
• User sentiment cues
• Compliance with call scripts or guidelines

These metrics formed the foundation of a consistent scoring framework.

3. Built an AI call scoring model

On top of the defined metrics, we developed a model that:

• Analyzed each AI call
• Scored it across the defined dimensions
• Highlighted specific issues when a call underperformed
• Provided an overall call quality score

This transformed raw call data into actionable insights.

The scoring system is now actively used across the team:

• They can instantly see which calls performed poorly and why
• Product and engineering teams can prioritize improvements based on real data
• The model enables continuous optimization of the AI calling logic
• Over time, call quality has become significantly more predictable and measurable

The team now operates with full visibility into AI performance, making improvements faster, more targeted, and far more effective.