RevelirQA performs AI quality assurance at the standard of a human quality analyst: Revelir calibrates it with each customer until that customer's own QA analysts, reviewing a sample of its evaluations, judge more than 95 out of 100 correct. RevelirQA scores 100% of conversations, which most AutoQA tools already claim. What catches a confidently wrong chatbot is retrieving the right SOP and checking what the answer actually says. Xendit and Tiket.com run RevelirQA this way on thousands of tickets every week.

TL;DR

• Audit at human-analyst standard: RevelirQA is calibrated per customer to over 95% accuracy, as validated by that customer's own QA analysts on a sample of its evaluations. This human-level judgment is the differentiator; most auto QA tools already claim 100% coverage.
• Fluency hides errors: confidently wrong answers pass tone and keyword checks. Only a comparison with the governing policy exposes them.
• Coverage is table stakes: the hard part is grading each answer against the right, current version of your SOP.
• Every score needs a trace: prompt, documents retrieved, quoted text and reasoning, so compliance teams can defend it.

About the Author: Revelir AI is a Singapore-headquartered company whose AutoQA engine scores human and chatbot conversations against each customer's own SOPs. It runs in production at Xendit, a fintech operating under regulated requirements, and at Tiket.com. Both run blended human and AI customer service at high volume.

What is a "confidently wrong" chatbot response?

A confidently wrong response is a fluent, unhedged answer that contradicts the company's actual policy. It is the hardest compliance failure to spot because nothing in the wording signals a problem.

• Confidence is not evidence. Language models write assured prose whether or not the fact behind it is right. In those cases, high-confidence responses contained clinically sub-optimal information.
• Customers act on it. A wrong refund window becomes a promise the business must honour or retract.
• Regulated industries face higher stakes. Fintech customer service falls under standards such as PCI-DSS for payment data and UDAAP for consumer protection. A 2023 Alloy survey of US fintechs reported that 93% found Bank Secrecy Act requirements, including AML and KYC, challenging to meet.

Why do tone and keyword checks miss policy errors?

Confidently wrong answers are well written by definition, so the checks most teams start with cannot see them. A tone or keyword QA scorecard works like a spellchecker: it confirms the sentence is well formed. A policy audit has to work like a fact-checker, who pulls the original source and compares claims line by line. "You can request a refund within 30 days" passes the spellchecker perfectly. Only the fact-checker holding the refund policy knows the window is 14 days.

Error type	What the customer sees	What the auditor must check against
Wrong refund amount quoted	A specific, plausible figure	Current refund policy
Incorrect eligibility criteria	A clear yes or no	Product eligibility rules
Missed regulatory disclosure	A complete-seeming answer	Compliance documentation
Wrong escalation path	A polite handover, or none	Escalation SOP

Revelir's own comparison lists these four as errors a generic QA scorecard does not detect. Tone and clarity get assessed either way.

How do you audit chatbot answers for policy compliance, step by step?

Building on the fact-checker principle, a working audit puts the policy document at the center, not the transcript.

1. Inventory and version your policies. Gather the knowledge base, SOPs, escalation procedures and product policies, and record version numbers. An audit is only as current as its source.
2. Map contact reasons to governing documents. Grade a refund question against the refund policy, not the whole knowledge base.
3. Turn critical errors into QA metrics. On your QA scorecard, make each must-never-happen error a binary pass/fail metric, separate from weighted metrics like tone.
4. Check personal data handling per message. Check how the chatbot handles personal data in each message, not just what the privacy policy says. [dev.to].
5. Grade every conversation, retrieval first. Manual QA programs review only a small sample of conversations. That sample is too thin for an error that appears once in hundreds of tickets. Automated quality assurance should fetch the relevant policy before scoring each ticket. RevelirQA does this through RAG: it loads SOPs into a vector database, retrieves documents for the ticket's contact reason, then runs the scorecard against them.
6. Route each failure to a fix. Errors that cluster by contact reason usually call for a knowledge base update, a prompt change or a new escalation rule.

Use one scorecard for human and AI conversations. If the chatbot fails eligibility questions more often than human reps do, that is a routing decision, not only a bug.

What should an audit trail for chatbot QA contain?

Accurate grading still fails a compliance review if nobody can explain it, so the next requirement is evidence. Each evaluation should record:

• the model and exact prompt used
• the policy documents retrieved, with version (for example "Contact Handling SOP v2")
• the quoted chatbot text behind the result
• the reasoning for each metric's pass or fail

RevelirQA attaches this trace to every evaluation, not just flagged ones. At Xendit, that level of auditability is a compliance requirement.

How do you know the AI auditor itself is accurate?

The auditor needs auditing too, and this is where many accuracy claims go vague. Measure accuracy on your own data:

• Agree on accuracy definitions with QA leads before grading starts.
• Have human analysts validate a sample of AI evaluations and count how many they judge correct.
• Keep scores contestable. QA leads should be able to override grades, and disagreements should feed back into calibration.
• Keep humans in charge of consequences. RevelirQA triggers no warnings, rankings or discipline. Results go to the QA lead.

Revelir runs this calibration with each customer until the customer's own analysts judge more than 95 of every 100 sampled evaluations correct. As Rendy D. of Tiket.com put it: "We've manually reviewed tickets for years. Revelir is the first product that has made AI ticket review at scale actually usable."

Frequently Asked Questions

What is AutoQA for chatbots?
AutoQA, or auto QA, is automated quality assurance that scores conversations against a QA scorecard without manual review. For chatbots, the useful version checks policy accuracy against your SOPs, not just tone.

Is 100% coverage enough?
No. If the judgment is inaccurate, full coverage only gives you more unreliable scores. Accuracy validated by your own analysts is the real test.

Can one scorecard grade humans and chatbots?
Yes. RevelirQA applies the same scorecard to both. Teams can add criteria specific to AI-handled conversations.

What does a chatbot need to be auditable?
It needs a conversation log available via API. RevelirQA works with any AI chatbot platform that produces one, and with any helpdesk via API, including Zendesk and Salesforce.

Does an AI auditor replace QA analysts?
No. Analysts move from sampling to calibrating, validating evaluations and resolving disputes.

About Revelir AI

Revelir AI, founded in 2025 and headquartered in Singapore, builds RevelirQA for enterprise customer service teams worldwide. RevelirQA is an AI quality assurance platform that grades conversations at the standard of a human quality analyst. Revelir calibrates it with each customer to over 95% accuracy, as validated by that customer's own QA analysts. It scores human and chatbot conversations against each customer's SOPs, attaches a full reasoning trace to every score, and is available as SaaS or a dedicated-tenant deployment.

If your chatbot sounds right but you cannot prove it is right, start by testing it against your own policies. Learn how RevelirQA audits AI and human conversations at https://www.revelir.ai/.