Call center quality assurance software RFP template

Automated call center QA uses AI to score your contact center interactions against a defined scorecard, without a human reviewer listening to each one. It can evaluate every interaction rather than a small sample, flag compliance issues, and route findings to coaching. The goal is consistent, complete quality measurement instead of the thin, subjective sample that manual review can cover.

Manual QA has evaluators listen to a small sample of interactions, often only 1 to 5 percent, and score them by hand. Automated QA scores up to 100% of interactions with AI, so coverage is complete and consistent. Most teams keep humans in the loop for calibration and disputes, using automation for scale and people for judgment.

AI call scoring evaluates an interaction against your scorecard criteria and produces a score with supporting evidence. Strong systems transcribe the conversation, use generative AI to understand what was said rather than matching keywords, apply your scorecard, and tie each result to the moment in the transcript that triggered it, so evaluators can see why a score was given.

It depends on the item. Automated scoring is strong on binary and compliance items and weaker on subjective ones like tone or empathy. Reputable vendors report how closely their scores agree with calibrated human evaluators and share a validation method. Ask for agreement rates broken out by item type, not a single headline number, and plan a calibration period before you trust the scores.

Aim for 100%. If a tool reviews only a small sample, you are guessing at quality rather than measuring it, and you will miss the rare interactions that carry the most compliance and customer risk. Automated QA exists to remove sampling, so make coverage one of the first questions you ask any vendor.

Calibration compares AI scores to your experienced evaluators on the same interactions, measures where they agree and differ, and tunes the AI until the gap is acceptable. Teams usually run the AI in parallel with human scoring for a few weeks, review disagreements together, and adjust the scorecard and model before relying on automated scores.

Score every vendor on the same dimensions: coverage and scoring approach, accuracy and calibration, fairness and workflow, compliance and data controls, the closed loop into coaching, AI governance, integrations, implementation, support, and total cost. Define your metrics up front, ask for agreement rates against your own evaluators, and run a proof of concept on your own interactions.

It varies by vendor and by how many integrations and scorecards you need, but a focused rollout is usually measured in weeks, not quarters, because it runs on top of your existing telephony rather than replacing it. Ask each vendor for a plan with milestones, the resources they need from your team, and a proof of concept on your own interactions.

Yes. The template is a free Excel scorecard with no form to fill out. Download it, edit it however you like, and send it to the QA software vendors you are evaluating. It was compiled by Balto from more than 100 contact center AI RFPs, so it reflects the questions that tend to matter most.