AI Scorecards for Customer Service QA | Solidroad

AI Scorecards

Your rubrics. Your rules. Every interaction graded.

Your definition of quality, applied automatically to every conversation. Build scorecards around your standards and let Solidroad do the rest.

Customer follow-up

Generate with Al

Graded

Pass / Fail

Generate with Al

Write manually

Performance Ratings

Sections

The sharpest QA instincts, now on autopilot.

Explanation

Poor performance

0-3 points Should ask for reservation number first before offering a refund for the two tickets yesterday in order to confirm that they you are refunding the correct ticket in case there were multiple purchased.

Example
"Can you please provide your reservation number?"

Scoring that gets smarter with each calibration

Each calibration sharpens the scoring model, aligning evaluations with your team’s real standards.

Conversations scored

Instantly score your entire queue

No sampling, no guesswork. 100% of conversations are evaluated with consistent, reliable logic.

Personalized point system

Rating card
Poor
*Adjustment in progress*
Average
5-7
Good
7-10

Dynamic scorecards fit the way you grade

Customize criteria and weighting so scoring aligns with your exact QA standards and workflows.

Skill

Clarity

Generate with Al

Graded

Pass / Fail

Pass criteria
10 points
The agent uses simple language and avoids jargon when explaining technical details, troubleshooting steps, and platform.

Turn your QA logic into a unique scoring system.

Control how quality is measured by configuring custom metrics, weighting critical steps, establishing automatic pass/fail triggers, and embedding your team’s expertise into every evaluation the system performs.

Score the subtle signals that shape experiences.

Solidroad interprets multi-turn exchanges, customer emotions, and agent decision-making, scoring each moment using the same depth of understanding your most experienced QA reviewers would apply manually.

Improve quality continuously. Not quarterly.

Every calibration, correction, and score adjustment feeds back into the model, refining how conversations are evaluated and ensuring your QA standards get sharper with each interaction reviewed.

1
/
4

We finally have consistent visibility into quality across every region.

And we can verify readiness before agents go live, not after.”

Destiny Young
Sr. Manager, Experience Quality Programs
1hr
Saved per simulation
2000+
Hours of training completed

Case study

Case study