Quality at the scale of your conversations.
Apply a configurable scorecard to interactions and give reviewers a consistent evidence trail.
Automated Quality Assurance
The business challenge
Small QA samples and inconsistent scoring make it difficult to identify coaching priorities.
What better looks like
Apply a configurable scorecard to interactions and give reviewers a consistent evidence trail. Teams can focus their effort on evidence, review and continuous improvement.
Review a sample call for greeting, verification, ownership and resolution.
Core AI capabilities
Designed as an integrated workflow. Availability and model performance require implementation and validation.
- 01Configurable QA scorecards
- 02Automated call evaluation
- 03Weighted quality scoring
- 04Critical failure detection
- 05Evidence-based evaluation
- 06Compliance-related flags
- 07Human review and calibration
- 08Agent performance trends
- 09Coaching recommendations
- 10Quality improvement tracking
How it works
Human oversight is part of the workflow.
Choose a weighted scorecard
Evaluate evidence against criteria
Flag critical failures
Calibrate with a reviewer
See the idea in action.
Review a sample call for greeting, verification, ownership and resolution.
Deterministic sample output. No recording is uploaded and no AI model is invoked.
KPIs impacted
QA score, calibration agreement, critical failure rate, review coverage.
Use case
Review a sample call for greeting, verification, ownership and resolution.
Planned integrations
Recording systems, quality platforms, coaching workflows. Connectors are not active in this showcase.
Implementation considerations
Validate scoring against a human-labelled set. Critical flags require reviewer confirmation and documented appeal routes.