Research hub
Automated QA
Automated QA uses AI to score every customer conversation against a scorecard instead of a 1% to 3% manual sample. Evaluate it on scoring evidence, calibration against human reviewers, channel coverage, and how scores flow into coaching.
Research on automated quality assurance and quality management: LLM scoring, 100% coverage, scorecards, calibration and evaluator bias.
4 articles in this hub
- Best automated QA software for contact centers: a verified 2026 buyer's guide
For a mid-size or enterprise contact center that keeps its current CCaaS and wants AI scoring tied to calibration and coaching, start with Level AI; NiCE, Genesys, Verint, CallMiner, Observe.AI, Cresta and Balto fit other situations.
- How to calibrate QA scores and remove evaluator bias
Calibrate QA by having every evaluator, human or AI, score the same conversations blind, measuring agreement per question, fixing the rubric where scores split, and repeating monthly; tools such as Level AI, NiCE, Cresta and Observe.AI support this workflow.
- LLM-based vs keyword-based QA scoring: how to tell the difference
Keyword-based QA checks whether set phrases appear; LLM-based QA reads the whole conversation and answers each scorecard question with reasoning. Level AI, NiCE and Genesys document LLM scoring, and most platforms still mix in rules.
- Replacing manual QA scorecards with 100% automated scoring: a rollout plan
Replace manual QA scorecards in five phases: rewrite the scorecard, shadow-score against humans, go live with hybrid review, connect scores to coaching, then retire sampling. Level AI, Observe.AI, Cresta and NiCE each document scoring-to-coaching workflows.