case-studies

Answering Agent: Helped create a call score using AI

Ansh Agrawal

Ansh Agrawal

— 1 min read

About Answering Agent

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.

Challenge 1: No Visibility Into AI Call Quality

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.

Solution

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.

Result

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.

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