RevelirQA performs AutoQA to the standard of a human quality analyst, calibrated with each customer to over 95% accuracy as validated by that customer's own QA analysts on a sample of evaluations. Each score is based on the customer's own SOPs, which RevelirQA retrieves from their knowledge base before grading. Every score also comes with a reasoning trace that cites the policy passage it checked. Calabrio's Auto QM is a credible alternative. It evaluates voice and digital interactions and scores both human and AI agents. Scoring 100% of conversations is table stakes most AutoQA tools already claim. The real decision is how each vendor proves its scores match your analysts' judgment on your policies. Revelir's view comes from production: Xendit and Tiket.com run RevelirQA on thousands of tickets per week.

TL;DR

RevelirQA scores to the standard of a human quality analyst, calibrated with each customer to over 95% accuracy as validated by that customer's own QA analysts on a sample of evaluations.
The useful questions are about accuracy and evidence.
"Against your own SOPs" can work two ways. Policy can be written into scorecard criteria once, or the policy documents can be retrieved each time a conversation is scored.
Calabrio suits voice-heavy contact centers. Revelir suits high-volume digital teams on helpdesks that need scores they can audit against policy.

About the Author: Revelir AI builds RevelirQA, an AI quality assurance platform for customer service. It is in production at Xendit and Tiket.com, where it grades conversations in English and Indonesian against each company's own SOPs.

What does "the standard of a human quality analyst" mean in AutoQA?

This comparison turns on one bar. An AI grade counts as correct only when your experienced QA analyst would agree with it after reviewing the same conversation against the same QA scorecard.

Why coverage is not the bar: Manual QA reviews only a small sample of conversations by hand, so rare compliance errors go unseen. Automated quality assurance solves that coverage problem. However, an inaccurate grader applied to every conversation simply produces inaccurate scores at scale.
How the industry validates accuracy: Several analysts grade a fixed set of conversations blind, and that set becomes the reference standard. The AI's scores are then measured against it.
Revelir's version: Accuracy definitions and targets are agreed with your QA leads in week one. Revelir then adjusts the grading until your analysts sign off.

Key insight: An accuracy figure only means something when you know who checked it and what they checked it against.

How do Revelir AI and Calabrio compare on scoring against your SOPs?

With that bar defined, the two platforms can be compared on what each publicly states.

Dimension	RevelirQA	Calabrio Auto QM
Channels	Helpdesk conversations, plus chatbot logs available via API	Voice and digital channels
Human and AI agents	Same QA scorecard for both, shown on one dashboard	Omni Agent Intelligence scores both
How SOPs enter scoring	SOPs are loaded into a vector database. The documents for each ticket's contact reason are retrieved before scoring.	Custom questions and scoring, plus a knowledge base integration that adds context to evaluations (per Calabrio)
Evidence per score	Model, prompt, retrieved documents and reasoning for each criterion, on every evaluation	Ask the vendor for a sample evaluation record
Accuracy validation	Calibrated per customer. Your QA analysts validate a sample before go-live.	Vendor-stated accuracy claim. Ask how it was validated.
Integrations	Any helpdesk via API, including Zendesk and Salesforce	Any CCaaS, CRM, ITSM and AI agent platform, per Calabrio's Omni Agent Intelligence announcement

Why does retrieving the SOP at scoring time change the score?

Of all the rows above, "how SOPs enter scoring" matters most when the requirement is grading against your own policies. Picture two auditors:

The first auditor works from a checklist that summarises the policy manual.
The second auditor opens the manual to the relevant page for each file.

Both will catch tone problems. Now suppose the refund table changes on Tuesday. The checklist is only as current as its last rewrite. The manual is current the moment it is updated.

A well-maintained scorecard can stay current, but someone has to do that upkeep. RevelirQA uses the second approach. Revelir states that an updated document is used "on the next relevant evaluation." Its policy-aware scoring flags four types of error:

a wrong refund amount quoted
incorrect eligibility criteria
a missed regulatory disclosure
a wrong escalation path

Ask every vendor the same question: how does a policy change made on Tuesday reach Wednesday's scores?

How should you test any auto QA vendor's accuracy claim?

Since the human-analyst standard is the deciding factor, test it directly rather than accepting a headline number:

Freeze a reference set. Have your analysts grade it blind before seeing any AI output.
Define "correct" per criterion before the pilot starts.
Measure each criterion separately. A strong overall average can hide a weak compliance metric.
Require evidence for each score. That means the quoted exchange, the SOP passage it was measured against and the reasoning.
Change a policy mid-pilot and watch how quickly the scores reflect it.

Calabrio's QM Intelligence product page publishes an accuracy claim for its AI scoring. Ask Calabrio how that claim was validated and request its detailed validation methods. Apply the same scrutiny to Revelir. Its six-week pilot grades an agreed subset of 10,000 conversations against targets set in week one.

Which platform fits which customer service team in 2026?

Once you have your test results, fit comes down to your channels and how much you need to explain each score.

Calabrio: suits teams that need voice and digital evaluated together and a QA tool connected to their CCaaS stack.
Revelir: suits digital-first fintech, travel and e-commerce teams that need policy-grounded scores they can audit. Revelir lists its fit as 30 or more agents and 5,000 or more conversations a month.

Rendy D. of tiket.com: "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 does RevelirQA's over 95% accuracy mean?
Revelir calibrates the AI with each customer. The customer's QA analysts then review a sample of evaluations and judge more than 95 out of 100 correct.

Which is better for voice-heavy contact centers?
Calabrio's Auto QM evaluates voice and digital channels. Revelir's public materials describe scoring helpdesk conversations and chatbot logs, so confirm channel coverage with Revelir directly.

Can both score AI chatbots?
Yes. Calabrio does this through Omni Agent Intelligence. RevelirQA applies the same QA scorecard to any chatbot that produces a conversation log via API.

Does RevelirQA discipline agents automatically?
No. Results go to your QA lead, who can override any grade. Agents can also contest scores.

How is Revelir priced?
Pricing has two parts: a one-time deployment fee and a usage fee per conversation graded. The $10,000 pilot fee is credited in full against the deployment fee if you proceed.

What security documentation should I request?
Ask both vendors for current evidence on SOC 2 Type II, GDPR and CCPA, and on PCI DSS where payment data is involved. Revelir offers dedicated-tenant deployment for teams with data residency requirements.

Revelir AI vs Calabrio: Which Auto QA Platform Scores Customer Service Conversations to the Standard of a Human Quality Analyst Against Your Own SOPs in 2026?