The 7 Checks Every AI Support Bot Quality Audit Template Needs

An AI support bot quality audit template should cover seven checks: answer accuracy, handoff quality, repeat contact after the bot, false containment, customer sentiment about the bot, coverage gaps, and impact by customer segment. Each check needs four fields: a metric, an evidence source the bot does not control, a condition that flags it, and an owner. The copy-ready template is below.

The reason for the evidence-source field is simple. A bot's own dashboard grades its own work. Containment rate records a conversation as handled when the customer stops replying, and a customer who gives up stops replying too. A useful audit measures what the customer did next: whether they came back, switched channels, complained somewhere else, or left.

AI support bot quality audit template

Copy this table into a spreadsheet or doc and set your own targets for each flag condition. Compare every metric against human-handled conversations on the same intent so the audit isolates the bot's performance from the mix of questions it receives.

The 7 checks explained

1. Answer accuracy

Accuracy is the baseline check and the one most teams already run. Review a fixed weekly sample of bot answers against current policy, pricing, and documentation, and score each answer as correct, partially correct, or wrong. Track wrong answers by intent, because a bot that is accurate overall can still be consistently wrong on refunds or billing.

2. Handoff quality

A bot that escalates at the right moment is working as designed. The failure is a late handoff, or a handoff where the human agent starts from zero. Measure how often customers restate their issue after the handoff and how long they wait for a person.

3. Repeat contact after the bot

This is the most reliable signal that a bot did not resolve anything. If a customer returns within seven days about the same issue, the first conversation was not a resolution, whatever the bot recorded. Compare the repeat contact rate for bot-resolved conversations against human-resolved ones on the same intent.

4. False containment

Containment counts a conversation as handled when the customer stops replying, which is also exactly what a frustrated customer does. Separate confirmed resolutions from silent endings, then check what the silent-ending customers did next: another channel, a refund request, or a cancellation.

5. Sentiment about the bot

Customers rarely tell the bot it failed. They say it in a CSAT comment, an app store review, a social post, or to their account manager on a call. An audit that reads only bot transcripts misses the strongest evidence, so this check has to span every channel where customers talk about support.

6. Coverage gaps

Group fallback responses and escalations by topic every week. A topic that appears suddenly usually means a launch, a policy change, or an outage the knowledge base has not caught up with. Each gap becomes a content task with an owner.

7. Impact by customer segment

A bot failure on a free-plan password reset and the same failure on an enterprise account near renewal are not the same problem. Join bot outcomes to plan, segment, lifecycle stage, and account value so the audit shows who is affected, not just how often.

How to run the audit: cadence and sampling

Weekly: review a fixed sample of bot transcripts for accuracy and handoff quality, and scan fallbacks for new topics.

Monthly: run all seven checks, including repeat contact, false containment, sentiment across channels, and segment impact, and compare each against the human-handled baseline.

After every change: rerun the accuracy and coverage checks whenever the bot's model, prompts, or knowledge base changes. Regressions cluster around changes.

Keep the categories identical for bot and human conversations. If the bot's intents and the support team's ticket tags use different labels, the comparison breaks and the audit turns into a translation exercise.

How to automate the audit with Enterpret

Checks 1 and 2 can live inside the bot platform. Checks 3 through 7 cannot, because the evidence sits in other systems: the ticket queue, call recordings, reviews, and the CRM. That is where Enterpret fits.

One taxonomy for bot and human conversations. Enterpret ingests AI agent conversations, such as those from Decagon, alongside Zendesk and Intercom tickets, calls, surveys, and reviews. The Adaptive Taxonomy classifies all of them into the same themes and evolves with customer language, so bot and human performance are comparable issue by issue.

Segment and account context on every conversation. The Customer Context Graph attaches segment, LTV, lifecycle stage, and usage to every signal, which is what check 7 needs and what bot dashboards do not have.

Agents that run the checks continuously. Enterpret's AI agents detect negative sentiment shifts in support threads, flag high-escalation-risk feedback, analyze agent-level CSAT for coaching, identify recurring questions not covered by existing help content and draft improvements, and convert critical feedback into tracked issues in Jira and Linear.

FAQ

What should an AI support bot quality audit include?

An AI support bot quality audit should include seven checks: answer accuracy, handoff quality, repeat contact after the bot, false containment, customer sentiment about the bot, coverage gaps, and impact by customer segment. Each check needs a metric, an evidence source outside the bot's own dashboard, a flag condition, and an owner.

How often should you audit an AI support bot?

Run a sampled review weekly, a full audit monthly, and an extra review after every change to the bot's model, prompts, or knowledge base. Changes are when regressions appear, so they deserve their own checkpoint.

Why isn't containment rate enough to measure bot quality?

Containment counts a conversation as handled when the customer stops replying, which also happens when a customer gives up. Repeat contact, abandonment followed by another channel, and sentiment outside the bot conversation show whether contained conversations were actually resolved.

How do you compare bot quality to human agents?

Use the same categories and the same metrics for both. Compare repeat contact, escalation, and sentiment for bot-handled and human-handled conversations on the same intent, so differences reflect the bot rather than the mix of questions it receives.

How does Enterpret help audit an AI support bot?

Enterpret ingests AI agent conversations, such as those from Decagon, alongside tickets, calls, reviews, and surveys, and classifies all of them with the same Adaptive Taxonomy so bot and human performance are comparable by theme. The Customer Context Graph attaches segment, LTV, and lifecycle stage to every conversation, and Enterpret's AI agents monitor sentiment shifts, flag escalation risk, and draft help content for coverage gaps.

Related reading: the metrics every support team scorecard should track and the signals that customers do not trust your AI feature.