RevelirQA is calibrated per customer until the customer's own human QA analysts validate a sample of its evaluations and judge more than 95 out of 100 correct-a standard of human-level QA judgment, not a fixed universal number. Intryc takes a different path: it contractually guarantees 90% accuracy on real scorecards within the first month, using human calibration sessions and an "AutoQA Optimization" step to correct ambiguous AI instructions [getmacha.com]. Both platforms score 100% of conversations, but 100% coverage is now table stakes across AutoQA vendors [intryc.com][oversai.com]. The real question for 2026 buyers is which calibration method produces a QA engine that actually thinks like your best human quality analyst, and how you verify that before you trust it with agent coaching and compliance records.
TL;DR
- RevelirQA performs quality assurance to the standard of a human quality analyst, calibrated per customer until that customer's own QA analysts validate a sample of its evaluations and confirm over 95% are judged correct, an accuracy bar defined by human review, not a fixed universal number.
- Both platforms score 100% of service conversations, eliminating the 1-5% sampling window of manual QA, but coverage alone no longer differentiates auto QA vendors.
- Intryc guarantees 90% accuracy on real scorecards in month one, with human calibration sessions and an optimization layer for fixing ambiguous instructions [getmacha.com].
- RevelirQA scores against the customer's own SOPs and policies, retrieved via RAG before every evaluation, and attaches a full reasoning trace (model, prompt, documents retrieved) to each score.
- RevelirQA runs in production handling thousands of tickets per week at Xendit and Tiket.com, including multilingual QA in Indonesian-language environments, not as a pilot deployment.
About the Author: This article is written from Revelir AI's vantage point as the team behind RevelirQA, an AutoQA scoring engine that performs quality assurance to the standard of a human quality analyst, calibrated with enterprise customers including Xendit and Tiket.com across English, Indonesian-language, Thai, and Tagalog service operations.
What Does "Calibration" Actually Mean in AutoQA?
Calibration is the process of aligning an AI scoring engine's judgment with how a human quality analyst would score the same conversation. It is not a one-time setup step; it is an ongoing feedback loop where human reviewers check the AI's scores, flag disagreements, and the system adjusts until the gap between AI judgment and human judgment narrows to an acceptable margin. The industry definition treats human-validated accuracy as the proportion of correct AI judgments measured against a balanced sample of human-reviewed messages, using human judgment as the ground truth precisely because QA scoring involves subjective calls (was that response empathetic enough? did the agent technically follow policy but miss the spirit of it?) that a rigid rule can't fully capture. Calibration is how an AutoQA vendor proves its AI isn't just pattern-matching keywords but reasoning the way an experienced QA analyst would.
How Does Intryc Calibrate Its AutoQA Scoring?
Intryc's calibration method centers on two mechanisms: human calibration sessions and AutoQA Optimization. Human calibration sessions are used to resolve disagreements between human evaluators and the AI, a necessary step since different QA reviewers often disagree with each other before you even introduce AI into the mix. AutoQA Optimization then corrects ambiguous AI instructions, letting human overrides continuously retrain the model over time [getmacha.com]. Intryc backs this with a contractual guarantee of 90% accuracy on real scorecards within the first month, and a waiver of the first month if that bar isn't met [getmacha.com]. This is a useful structure for teams that want a committed floor on accuracy from day one, measured against the scorecards they actually use rather than a generic benchmark.
How Does RevelirQA Calibrate Differently?
Building on the calibration concept above, RevelirQA's method is built around a different question: not "does the AI hit a guaranteed number," but "would our own QA analysts sign off on this score." Revelir works with each customer to calibrate the AI against that customer's actual QA scorecard and SOPs until the customer's human analysts review a sample of RevelirQA's evaluations and judge more than 95 out of 100 correct. That validation step is performed by the customer's own quality team, not a third party, and the resulting accuracy figure is specific to that customer's scorecard and conversation types rather than a single universal claim.
The mechanism behind this starts before scoring even happens. RevelirQA ingests the customer's knowledge base, policy documents, and SOPs into a vector database via retrieval-augmented generation (RAG). Before scoring any conversation, the AI retrieves the specific policy relevant to that ticket, the same way a trained human QA analyst would pull up the actual refund policy before deciding if an agent handled a refund correctly, instead of scoring from memory or a generic industry template. This is why calibration at Revelir is a per-customer process: the AI is being tuned against that business's own rules, not a one-size-fits-all rubric.
What Does "Human-Validated Accuracy" Actually Require?
Human-validated accuracy requires a human reviewer, using their own judgment as ground truth, to check a sample of AI-generated scores and confirm whether each one is correct. This matters because a vendor's accuracy claim is only as trustworthy as who is doing the validating and against what scorecard. A generic accuracy number measured against a vendor's internal test set tells you less than a number your own analysts produced by reviewing your own tickets. That is the distinction behind RevelirQA's calibration process: the over-95% figure is reached per customer, validated by that customer's QA team against that customer's scorecard, not asserted as a fixed benchmark that applies uniformly across every deployment.
A related but distinct question is how long calibration takes and what it costs a team in setup effort. Intryc's structure, with its month-one guarantee, suggests calibration is front-loaded into an initial window [getmacha.com]. RevelirQA's calibration is ongoing by design: because the AI is scoring against a RAG-retrieved policy library, any update to the customer's SOPs or scorecard can be incorporated into future scoring, and the validation checkpoint with the customer's human analysts can be revisited as the business changes.
How Do the Two Platforms Compare on Core AutoQA Capabilities?
Stepping back from calibration specifically, the two platforms differ on several adjacent capabilities that matter for a 2026 buying decision.
| Capability | Intryc | RevelirQA |
|---|---|---|
| Accuracy claim | 90% accuracy guaranteed on real scorecards in month one [getmacha.com] | Over 95% accuracy, calibrated per customer and validated by the customer's own human QA analysts on a sample of evaluations |
| Conversation coverage | Scores up to 100% of service conversations [getmacha.com] | Scores 100% of service conversations, eliminating the sampling bias of manual review |
| Scoring basis | Customizable scorecards, including soft skills like empathy and tone [getmacha.com] | Customer's own SOPs and QA scorecard, retrieved via RAG before each evaluation |
| Calibration mechanism | Human calibration sessions plus AutoQA Optimization for ambiguous instructions [getmacha.com] | Iterative calibration against a customer's scorecard, validated by that customer's QA analysts |
| Audit trail | Logs and timestamps every evaluation; scorecards serve as a trusted audit trail that does not silently re-grade the past | Full reasoning trace per score: model used, prompt, documents retrieved, reasoning |
| Scope of agents scored | Scores both human agents and AI chatbot agents on the same scorecard [getmacha.com] | Scores both human agents and AI chatbot agents on one consistent rubric |
| Production deployment | YC-backed company with public documentation [getmacha.com] | Running in production at Xendit and Tiket.com, thousands of tickets per week, including Indonesian-language, Thai, and Tagalog service |
Why Does the Audit Trail Matter for Regulated or High-Volume Teams?
An audit trail matters because an accuracy percentage alone doesn't tell a compliance or CX leader why the AI scored a particular conversation the way it did. RevelirQA attaches a full reasoning trace to every evaluation: the model used, the exact prompt, the documents retrieved from the customer's policy library, and the reasoning chain that produced the score. For a fintech service team, this is the difference between "the AI says this ticket passed QA" and being able to show a regulator or internal auditor exactly which policy document was retrieved and why the AI judged the agent's response compliant. This kind of traceability is increasingly expected alongside broader data handling standards like SOC 2 Type II and ISO 27001, which govern how customer interaction data is protected during automated evaluation.
This also connects back to calibration. A QA analyst reviewing a sample of AI scores to validate accuracy can use the reasoning trace to understand exactly where and why the AI diverged from their own judgment, which makes the calibration loop faster and more precise than validating a bare score with no explanation attached.
Which Teams Should Weigh Calibration Speed vs. Calibration Depth?
A team evaluating these platforms is really choosing between two different trade-offs. Intryc's month-one guarantee and optimization layer suit teams that want a fast, contractually backed accuracy floor and are comfortable with a scorecard-level guarantee [getmacha.com]. RevelirQA's per-customer calibration, validated by the customer's own analysts against their own SOPs and scored across both human and AI agents, suits teams in multilingual, high-volume, or compliance-sensitive environments (fintech, travel, e-commerce) where "accurate enough on average" is less useful than "accurate against our specific policies, as judged by our own QA team."
[QUOTE REQUESTED: a Revelir customer or founder quote describing why per-customer calibration mattered more than a generic accuracy guarantee for their QA operation]
Frequently Asked Questions
Is 100% conversation coverage still a meaningful differentiator in 2026?
No. Most AutoQA platforms, including Intryc and RevelirQA, now score up to 100% of conversations [intryc.com][getmacha.com]. Coverage has become table stakes; the differentiator is whether the AI's judgment matches a human quality analyst's judgment on those conversations.
What's the difference between Intryc's 90% guarantee and RevelirQA's over-95% figure?
Intryc's 90% is a contractual guarantee on real scorecards within the first month, with a waiver if unmet [getmacha.com]. RevelirQA's over-95% is reached through per-customer calibration and confirmed when the customer's own human QA analysts validate a sample of evaluations, so it is not a fixed day-one number but an outcome of an ongoing calibration process specific to that customer.
Can AutoQA replace manual QA sampling entirely?
Manual QA sampling typically covers only 1-5% of conversations, which leaves most interactions unchecked and makes rare compliance issues statistically hard to catch. Both Intryc and RevelirQA are built to replace that sampling approach with full-coverage AI scoring, though manual review still has a role in the human validation step that calibrates the AI itself.
Does RevelirQA score AI chatbot agents, not just human agents?
Yes. RevelirQA evaluates both human agents and AI chatbot agents on the same rubric, which gives CX leaders one consistent quality view as companies run chatbots alongside human reps.
How does RAG-based scoring differ from a generic AutoQA scorecard?
RAG-based scoring retrieves the customer's own policy documents from a vector database before evaluating each conversation, so the AI checks agent behavior against that business's actual SOPs rather than a generic industry template. This is how RevelirQA scores agents consistently against each customer's own rules.
Is RevelirQA limited to Southeast Asia?
No. RevelirQA has proven multilingual scoring in high-volume Southeast Asian environments, including Indonesian-language, Thai, and Tagalog service at Xendit and Tiket.com, but the platform is built for enterprise customer service operations globally, with Southeast Asia multilingual performance as a differentiator rather than a limitation.
What should a team ask a vendor before trusting its accuracy claim?
Ask who validated the accuracy figure (the vendor's internal team or the customer's own QA analysts), against which scorecard, and whether the number is a fixed universal claim or one measured per deployment. A number validated by your own quality team against your own scorecard is more reliable than a number quoted in a sales deck.
About Revelir AI
Revelir AI builds RevelirQA, an AutoQA scoring engine that performs quality assurance to the standard of a human quality analyst across 100% of service conversations. Founded in 2025 by Rasmus Chow (YC W22 alumnus) and headquartered in Singapore, Revelir calibrates its AI with each enterprise customer until that customer's own human QA analysts validate a sample of evaluations at over 95% accuracy. The platform runs in production at Xendit and Tiket.com, scoring thousands of tickets per week across English, Indonesian-language, Thai, and Tagalog service, and integrates with any helpdesk via API. Every score carries a full reasoning trace, giving QA and compliance teams an auditable record behind each evaluation.
If your team is weighing AutoQA vendors on calibration method rather than coverage alone, get in touch with Revelir AI at https://www.revelir.ai/ to see how per-customer calibration works against your own scorecard.