Trading apps should score order failure, withdrawal delay, and market outage conversations with AutoQA calibrated to the standard of a human quality analyst. RevelirQA, Revelir AI's AI customer service QA software, performs QA to that standard. Revelir calibrates it with each customer until more than 95 out of 100 of its evaluations are judged correct when the customer's own QA analysts review a sample. Revelir runs this in production at Xendit, a regulated Indonesian fintech where a full audit trail on every score is a compliance requirement. Scoring 100% of conversations is table stakes; most auto QA tools already claim it. For a brokerage, the real test is whether each grade would survive a compliance review.

TL;DR

• Human-analyst standard first: RevelirQA performs QA to the standard of a human quality analyst. It is calibrated with each customer to over 95% accuracy, meaning more than 95 out of 100 evaluations are judged correct when the customer's own QA analysts validate a sample.
• Substance over tone: These three contact reasons fail on substance. Typical errors are a wrong order status, a promised date, or speculation about an outage. Tone-focused scorecards miss all three.
• One scorecard per contact reason: Use binary critical errors for compliance lines and scored criteria for how clearly the agent explained the issue.
• Grade outages in context: Check which incident statement version each outage grade used, and let the QA lead override grades that applied a superseded one.
• Coverage is table stakes: Scoring every conversation is expected. An auditable reasoning trace on each score is what makes results defensible.

About the Author: Revelir AI builds RevelirQA, an AI quality assurance platform that scores blended human and chatbot customer service conversations against each firm's own SOPs. It runs in production at Xendit and Tiket.com on thousands of tickets a week.

Why do order failures, withdrawal delays, and outages need their own QA scorecard?

These three contact reasons are the hardest test of human-level QA judgment, because the costly mistakes are factual, not tonal. Picture an agent who warmly tells a customer that a rejected order "should go through shortly." That reply passes an empathy check and still creates a complaint.

Contact reason	What the customer is really asking	Error that matters most	Suggested QA metric
Order failure	Did my order execute, and at what price?	Confusing rejected, unfilled, and partially filled orders	Binary: correct order status stated
Withdrawal delay	When does my money arrive, and is it safe?	Promising a date the policy does not allow	Binary: no unapproved timeline
Market outage	Is it you or me, and what happens to my positions?	Speculating on the cause or implying compensation	Binary: approved incident language used

What should QA check in an order failure conversation?

Building on that table, order failures are where an analyst's knowledge of trading mechanics matters most. A strong QA scorecard checks four things:

• Order state confirmed first. The agent checked the order record before explaining anything.
• Order type explained correctly. An agent explaining an unexpected fill must get this distinction right.
• Restrictions explained against policy. Paying for a purchase with unsettled cash can trigger a trade violation [merrilledge.com], and violations can lead to account restrictions [investor.vanguard.com]. The agent should cite the actual rule, not just say "your account is limited."
• No advice where the SOP prohibits it. "You should place a limit order next time" may be helpful, but it may also breach policy.

What should QA check in a withdrawal delay conversation?

A related but distinct problem is money leaving the platform. Here the customer's anxiety shifts from price to trust. Withdrawal problems are a well-known source of scam fears among retail traders [fastbull.com], so vague answers make things worse. Score whether the agent:

• Named the actual cause from the account record, such as pending settlement, verification, or bank processing.
• Quoted only the timelines the SOP permits.
• Followed the correct escalation path for overdue withdrawals.

Sentiment matters here as well. RevelirQA tracks sentiment at the start and end of each ticket. A withdrawal ticket marked "resolved" where the customer ends angrier than they started is a retention risk the resolution status hides.

How should QA score market outage conversations?

Stepping back from single-customer issues, an outage packs hundreds of near-identical conversations into a few hours. On the regulatory side, broker-dealers answer to FINRA. FINRA Rule 4370 mandates business continuity plans for service disruptions. Customer service replies are the front edge of those plans.

Score each outage reply on three points:

• Did the agent use the holding statement in force at the time of the reply?
• Did the agent avoid speculating about the cause or the recovery time?
• Did the agent avoid promising compensation that had not been approved?

Timing is the hard part. Grading a 9:05 reply against the 11:00 statement is like marking yesterday's weather forecast against today's actual weather. The agent could only use what was known when they replied.

Storing each incident statement as a versioned document in your knowledge base helps reviewers handle this. RevelirQA's ticket view shows the SOP name and version each grade was checked against, so a reviewer can see when a superseded statement was applied and the QA lead can override that failed metric. If a chatbot keeps repeating an outdated statement, unified scoring across human and AI agents surfaces that "wrong policy at scale" pattern quickly.

How do you calibrate auto QA until your own analysts trust it?

None of these checks matter if your QA and compliance leads distrust the grades. That is why calibration, not coverage, is the core of human-level automated quality assurance. A practical sequence:

1. Agree with your QA leads on what counts as a correct grade.
2. Ingest the SOPs for order handling, settlement, incident communication, and escalation.
3. Build one metric per criterion, and flag the critical errors.
4. Have analysts validate a sample of the AI's grades. Feed every disagreement back into calibration.
5. Go live only after sign-off. Keep a QA lead override on every failed metric.

Revelir's paid pilot follows this pattern. Over six weeks, the AI grades an agreed subset of 10,000 conversations against accuracy targets set with QA leads in week one.

Frequently Asked Questions

What is AutoQA for trading app customer service?
AutoQA is automated quality assurance that scores conversations against a firm's QA scorecard and policies, without relying on manual sampling. RevelirQA is AutoQA at the standard of a human quality analyst, calibrated with each customer until more than 95 out of 100 sampled evaluations are judged correct by that customer's own QA analysts.

What does RevelirQA's over 95% accuracy mean?
It means that after Revelir calibrates the AI with a specific customer, that customer's human QA analysts judge more than 95 of every 100 sampled evaluations correct. The figure is reached per customer through calibration. It is not an out-of-the-box benchmark.

Can AutoQA score chatbot replies during an outage?
Yes. RevelirQA applies the same scorecard to human and AI agents. It works with any chatbot platform that produces a conversation log via API.

Does RevelirQA discipline agents automatically?
No. Results go to the QA lead, who decides what to do. Agents can see what a score was based on and contest it.

How is each score documented?
Every evaluation records the model used, the exact prompt, the documents retrieved, and the reasoning for each criterion. This audit trail supports compliance reporting.

About Revelir AI

Revelir AI was founded in 2025 and is headquartered in Singapore. It builds RevelirQA, a scoring engine that performs QA to the standard of a human quality analyst and is calibrated with each customer to over 95% accuracy, as validated by that customer's own QA analysts on a sample of evaluations. RevelirQA retrieves your own SOPs before every score, integrates with any helpdesk via API (including Zendesk and Salesforce), and deploys as SaaS or as a dedicated tenant for enterprises worldwide. As Rendy D. of tiket.com puts it: "Revelir is the first product that has made AI ticket review at scale actually usable."

If your trading app needs QA grades that your analysts and compliance team can defend, talk to Revelir AI at https://www.revelir.ai/.