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Local-only business quality-assurance worksheet

AI Customer Support QA Scorecard Calculator

Score an already human-reviewed batch of draft customer replies by its worst error, then account for drafting, checking, corrections and incident work. A good pass percentage never cancels a serious failure.

Review outcomes for one batch

The default 50-reply batch is fictional and matches the worked example in our step-by-step guide. Replace every value with your own aggregate counts. Do not enter customer identities or messages.

Count each response exactly once

A major failure includes unsupported material claims, privacy breaches, unauthorized promises and missed high-stakes escalations. If a reply also has a minor error, classify it as major only.

Total labor minutes for this same batch

Enter real, comparable staff time. Software charges, permissions, overhead and future incident risk are not priced here. A zero here means zero observed minutes, not that a risk cannot occur.

Interpret the result before changing a support workflow

A response with a serious error belongs in the major-failure group regardless of its tone or how many other criteria it passed. A pilot with major failures calls for investigation and a decision under your own incident process. Even a sample with no observed major failures does not prove that future messages will be safe, compliant or representative.

If you compare staff effort, use the same question types, quantities and scope for the manual baseline and assisted process. Include review, correction and escalation minutes. The time result describes this specific batch; it excludes software fees, onboarding, future maintenance and organizational approvals. Read the full support QA scoring guide for the rubric and worked 36/9/5 example. Then screen the proposed use with our workflow suitability checklist before considering any additional automation or a financial ROI estimate.

This worksheet calculates numbers only in your browser, without uploading or retaining inputs. It is intended for aggregate, synthetic or authorized non-sensitive counts. The voluntary NIST AI Risk Management Framework is a separate resource for identifying and governing organizational risk; Lynara does not certify compliance.