A support bot that makes up a refund policy is worse than no bot at all. Customer support AI evaluation grades your bot's answers against your actual policies, so you find the fabrications before your customers do.
We grade outputs. We grade your support bot against your real policies and help docs. We do not train models, run fine-tuning, or sell experts we cannot verify.
Customer support AI evaluation is the grading of a support bot's answers against your real policies, help center, and tone rules. Support teams need it because a bot's mistakes are public: every wrong answer goes to a real customer in real time. Good looks like answers that match the policy exactly, stay inside what the bot is allowed to promise, and escalate cleanly when they should not answer at all. If your bot reads from documents, pair this with a RAG chatbot evaluation to check the retrieval too.
Support bots fail in specific, repeatable ways. We grade for all of them.
The most common and most expensive failure: the bot invents a policy detail to sound helpful. A 30-day return becomes 60 days. A non-refundable fee becomes refundable. We grade every answer against your actual policy documents and flag each invention.
Bots love to promise: a callback by Friday, a manual review, an exception to the rule. When the promise is not real, your team inherits an angry customer and a screenshot. We check whether the bot stayed inside what it is actually allowed to offer.
A good bot knows when it is out of its depth: billing disputes, account security, anything emotional. A bad one keeps going. We grade whether the bot escalated at the right moment, to the right place, with the right context.
Correct information delivered coldly to a frustrated customer reads as not caring. We grade tone against your brand rules: clear, calm, and human, especially when the customer is not.
You send 20 to 50 bot conversations plus your policies and tone rules, or we help you turn your help center into a grading rubric.
Trained reviewers grade every conversation against your policies, backed by automated checks. Policy inventions get flagged hard: they are P0s.
Conversation-level grades, the policy gaps the bot keeps inventing around, recommended prompt and retrieval fixes, and a walkthrough call.
A support bot is only as honest as its grounding in your actual policies. That is why every grade in the report points to a specific source: the help article, the policy page, the tone rule. When a grade says the bot invented a return window, the report shows the real return window next to it. No vague feedback, no "be more accurate." The fix is always specific enough to act on the same day.
A correct answer delivered coldly to an upset customer still costs you. Our reviewers grade tone against your brand rules on the conversations where it matters most: complaints, billing disputes, and anything emotional. The report separates fact errors from tone errors so your team knows whether to fix the knowledge, the prompt, or both.
The report also shows you where your policies are unclear, which is a finding most teams do not expect. When the bot invents the same detail across many conversations, the root cause is often a gap in the source material rather than a bad model. Those gaps get their own section, so your documentation team gets a punch list alongside your AI team. Fixing the source fixes the bot permanently, which beats patching the prompt every time a new invention appears. Good grading does not just find bot errors. It finds the missing answers behind them.
Your real source material: policy documents, help center articles, and tone guidelines. If those live in a help center, we can help you turn them into a grading rubric first. Every grade is tied to a specific source, so a flag always shows what the bot got wrong and where the truth lives.
We grade those especially carefully. The bot should stay calm, not match the customer's tone, and escalate or close the conversation per your policy. A correct answer delivered rudely still loses points on tone.
It depends on the language and the rubric. Talk to us about which languages your bot serves and we will tell you plainly whether we can grade them well. We would rather decline a language than grade it badly.
Yes, when the bot uses retrieval. We can grade whether it pulled the right documents and whether the answer actually reflects them. That is the core of our RAG evaluation: retrieval quality and answer grounding, checked separately.
Yes. A handoff is part of the answer. We check whether the bot escalated at the right moment, passed useful context to the human, and set the customer's expectation correctly. A bot that dumps a confused customer into a queue without context fails that sample.
A standard pilot uses 20 to 50 conversations, chosen to cover your main intent types: billing, troubleshooting, account changes, and the edge cases where your bot usually struggles. More volume is possible if you want a wider sweep.
Send 20 to 50 conversations. We will grade them against your real policies.
Not sure where to start? Talk to us and we will point you at the right evaluation.