TL;DR
- RevelirQA uses standard helpdesk APIs, so any platform that exposes conversation data via API can be connected without bespoke engineering work.
- Your SOPs and QA scorecard are ingested once and retrieved dynamically before every evaluation, so scoring always reflects your actual policies.
- Every score carries a full audit trail: prompt, documents retrieved, model used, and reasoning.
- RevelirQA scores both human agents and AI chatbots, giving support ops a single consistent view of quality across all channels.
- Xendit and Tiket.com run RevelirQA across thousands of tickets per week in production, not as a pilot.
About the Author: Revelir AI is an AI customer service QA software company headquartered in Singapore, purpose-built for high-volume, digitally-native enterprises. Its scoring engine runs in production at Xendit and Tiket.com, giving the team direct, ongoing experience with enterprise helpdesk connectivity at scale.
Why does helpdesk integration usually become a project?
Most AI tools that promise "easy integration" still require your engineering team to write middleware, map data schemas, and maintain a connection layer. The friction is structural: the AI tool and the helpdesk have different data models, and someone has to bridge them [1]. The typical result is a weeks-long scoping exercise, a backlog item that competes with product work, and a fragile connector that breaks when either platform updates its schema.
RevelirQA sidesteps this by treating helpdesk data access as a solved problem. Modern helpdesks like Zendesk and Salesforce expose well-documented REST APIs. RevelirQA reads from those APIs directly. Your support ops team provides API credentials during onboarding; Revelir handles the rest. No schema mapping, no middleware, no custom code on your side.
What does the connection architecture actually look like?
Building on the point above, the practical question for support operations is: what does my team actually configure? The answer is deliberately minimal.
| Step | Who does it | What it involves |
|---|---|---|
| 1. API credential setup | Support ops or IT admin | Generate a read-scope API token in your helpdesk; paste it into RevelirQA's settings panel |
| 2. SOP and scorecard ingestion | QA lead | Upload your policy documents and QA scorecard; Revelir indexes them into its vector database |
| 3. Metric configuration | QA lead | Define scoring criteria as binary, multi-option, or scored dimensions to match your existing QA scorecard |
| 4. Scoring begins | RevelirQA (automated) | The engine polls new conversations, retrieves relevant policy chunks via RAG, scores each ticket, and writes results to the dashboard |
Steps one through three typically take less than a day for a team already holding its own QA documentation. There is no professional services requirement and no engineering dependency on the client side.
How does RevelirQA score conversations against your own policies, not generic benchmarks?
Most AI quality assurance platforms apply a fixed set of criteria: tone, resolution, empathy. These are fine for a generic baseline, but they tell a fintech QA team nothing about whether an agent correctly applied the refund window policy, or told a customer the right document requirements for account verification.
RevelirQA takes a different approach. When you upload your SOPs, they are chunked and stored in a vector database. Before scoring every conversation, the engine runs a retrieval step, pulling the policy chunks most relevant to that specific ticket's topic. The score is then generated against those retrieved documents, not a hardcoded QA scorecard [2]. This means:
- A travel platform's refund policy changes. Upload the updated SOP, and the next ticket is scored against the new version automatically.
- A fintech compliance requirement affects only one product line. The engine retrieves that specific policy only for relevant tickets, not across the board.
- Multilingual environments (English, Indonesian, Thai, Tagalog) are handled natively, because the scoring logic sits in the model layer, not in a language-specific rules engine.
What is inside the audit trail on each score?
A related but distinct question for compliance-sensitive teams is accountability: if a score is disputed, or a regulator asks how an evaluation was reached, what evidence exists? This is where many AI QA tools are weakest. They produce a number with no explanation of how it was derived.
Every RevelirQA evaluation includes a complete reasoning trace:
- The model used for that evaluation
- The exact prompt sent
- The specific SOP documents retrieved by the RAG step
- The model's stated reasoning for each criterion score
For fintech teams operating under regulatory scrutiny, this is not a nice-to-have. Xendit, an Indonesian fintech, runs RevelirQA on thousands of tickets per week precisely because auditability at that volume is a hard requirement, not a preference.
Does RevelirQA score AI chatbots as well as human agents?
Stepping back from the technical detail, a separate concern is emerging for support ops teams deploying AI chatbots alongside human agents: quality measurement fractures across two different tools, or worse, the chatbot goes unmeasured entirely.
RevelirQA evaluates both. The scoring engine processes tickets closed by humans and conversations handled by AI chatbots identically. The same QA scorecard, the same SOP retrieval, and the same reasoning trace apply to both. CX leaders get a unified view of quality across their entire operation, which matters when an AI chatbot handles a growing share of volume but a policy miss there carries the same business risk as one by a human agent [3].
Frequently Asked Questions
Does connecting RevelirQA require changes to our helpdesk configuration?
No. RevelirQA reads from your helpdesk via API using a read-scope token. Nothing is written back to your helpdesk, and no workflow rules or triggers need to be created on the helpdesk side.
What if we use a helpdesk that isn't Zendesk or Salesforce?
Any helpdesk that exposes conversation data via a documented REST API can be connected. The specific setup steps vary by platform, but the architecture is the same. Contact Revelir's team to confirm compatibility with your specific stack.
How long does it take before we see scored conversations?
For most teams, the onboarding process from credential setup to first scored tickets takes less than a day, assuming your QA documentation is already available to upload.
What happens when we update our SOPs or QA scorecard?
Upload the updated document to RevelirQA's knowledge base. The vector index is refreshed, and subsequent evaluations automatically retrieve the updated policy. There is no retraining cycle and no delay.
Is RevelirQA suitable for teams handling multiple languages?
Yes. RevelirQA has been validated in English, Indonesian, Thai, and Tagalog in high-volume production environments, making it well-suited for regional or global support operations running multilingual queues.
How is scoring consistency maintained across a large agent team?
Every ticket is scored against the same retrieved SOP documents and the same QA scorecard criteria. There is no reviewer fatigue, no shift-to-shift variation, and no sample bias. The QA scorecard is applied identically to ticket one and ticket ten thousand.
What deployment options are available?
RevelirQA is available as a standard SaaS deployment or as a dedicated tenant for teams with stricter data residency or isolation requirements. Enterprise plans include dedicated tenant options.
About Revelir AI
Revelir AI is an AI customer service QA software company founded in 2025, headquartered in Singapore, and backed by the YC W22 network. Its scoring engine, RevelirQA, evaluates 100% of customer service conversations against a company's own policies and QA scorecard, eliminating the sampling bias that limits manual review to 1-5% of tickets. RevelirQA runs in production at Xendit and Tiket.com, scoring thousands of conversations per week across English, Indonesian, Thai, and Tagalog. The platform is built for global enterprise teams in fintech, travel, and e-commerce that need auditable, policy-grounded quality assurance at scale.
Ready to see RevelirQA running on your helpdesk data?
Visit Revelir AI at www.revelir.ai to book a demo or speak with the team about your QA setup.