Generic AI scoring tools fail multilingual customer service teams not because of flawed scoring logic, but because they lack the vocabulary. When a fintech agent in Jakarta writes mutasi rekening (account transaction history) or a travel support rep in Manila references a rebooking fee waiver in mixed Tagalog-English, a scoring engine trained on general language patterns may misread the intent, miss the policy context entirely, or flag correct responses as non-compliant. The result is inaccurate QA scores, coaching noise, and eroded trust in the QA programme itself.
- Industry-specific terminology in Bahasa Indonesia, Thai, and Tagalog is a known failure point for generic AI scoring engines.
- The problem is not just translation - it is domain context, code-switching, and evolving local vocabulary that generic models miss.
- QA scorecards built on custom policies and SOPs, retrieved at scoring time, are the structural fix - not language patching.
- Enterprise teams running high-volume multilingual QA need a scoring engine that distinguishes a compliant response from a policy miss, regardless of language.
- RevelirQA scores Bahasa Indonesia, Thai, and Tagalog conversations in production at scale, against each client's own policies.
About the Author: Revelir AI is an AI quality assurance platform headquartered in Singapore, with enterprise clients including Xendit and Tiket.com running multilingual QA scoring across thousands of conversations per week. The team specialises in automated QA for high-volume, digitally-native businesses across Southeast Asia and globally.
Why Does Terminology Specifically Break AI Scoring - Not Just Translation?
The distinction matters because most teams frame this as a translation problem when it is actually a domain-knowledge problem. Translation tools handle language conversion; QA scoring engines handle meaning evaluation. Those are different tasks, and terminology failures damage the second far more than the first.
Consider three real patterns:
- Bahasa Indonesia domain vocabulary: Indonesian language continues to absorb new technical and financial terminology at pace, with thousands of new entries formalised in recent years [2]. Terms like rekening virtual (virtual account), tarik tunai (cash withdrawal), or limit transaksi vary in meaning across fintech, banking, and e-commerce contexts. A scoring engine that has not been trained or grounded in these domain meanings will misinterpret agent responses [3].
- Thai transliterated jargon: Thai customer service in fintech and travel regularly blends transliterated English terms into Thai script. A phrase like สลิปโอน (slip-on, a payment slip reference) looks foreign to a general model but is standard vocabulary in Thai payment support. Without domain grounding, the model cannot assess whether the agent handled the query correctly.
- Tagalog-English code-switching: Filipino support agents routinely switch between Tagalog and English mid-sentence - a pattern called Taglish. A generic AI may parse the English segments accurately but lose coherence across the code-switched boundary, missing whether the full response was policy-compliant.
Research into large language model performance in Southeast Asian languages consistently highlights this gap: models trained primarily on high-resource languages handle regional languages superficially and domain-specific registers even more poorly [4]. Machine translation glossaries partially address consistency for human translators [1], but they do not solve the real-time scoring problem.
What Makes a QA Scorecard Fail in a Multilingual Environment?
Building on the terminology problem above, the harder question is structural: even when a team has a well-designed QA scorecard, multilingual environments expose weaknesses that would be invisible in an English-only operation.
| Scorecard Failure Mode | What Goes Wrong | Language Impact |
|---|---|---|
| Generic benchmarks, not your SOPs | Scoring engine evaluates against industry averages, not your refund policy or escalation rules | Correct local-language responses get marked down |
| No domain vocabulary grounding | Fintech or travel terms are misread or ignored | Policy-compliant Bahasa Indonesia responses appear non-compliant |
| Rigid QA scorecard criteria | Binary pass/fail on nuanced responses | Code-switched answers penalised for form, not substance |
| No audit trail on scores | QA managers cannot diagnose why a ticket was scored incorrectly | Multilingual errors are invisible, not fixable |
The common thread is that generic scoring tools are built for the majority case: English, standard register, universal policies. Multilingual enterprise support is structurally different and requires a scoring engine built to accommodate that difference.
How Should Enterprise QA Teams Actually Close the Glossary Gap?
Stepping back from the technical detail, the practical question is: what does a QA team actually do about this? There are three interventions that make a measurable difference, and they work in sequence.
1. Anchor Scoring to Your Own Policies, Not Generic Language Models
The most direct fix is to ensure that every scoring evaluation retrieves your actual SOPs before assessing the conversation. This is what retrieval-augmented generation (RAG) enables in practice. Rather than asking a general language model to infer what "correct" looks like, the scoring engine pulls the relevant policy document and evaluates the agent's response against it. Domain terminology is handled implicitly because the policy document itself uses your business's language.
2. Build Glossary Coverage Into Your Knowledge Base, Not as a Separate Layer
Many teams try to solve terminology gaps by maintaining a separate glossary or translation lookup. This creates maintenance overhead and still fails at scoring time. A more robust approach is to embed domain definitions directly into the SOPs and knowledge base that the scoring engine ingests. When mutasi rekening appears in both the policy document and the agent transcript, the engine can evaluate it coherently [1].
3. Require a Reasoning Trace on Every Score
Without an audit trail, multilingual scoring errors are invisible. When a QA manager can see exactly which document was retrieved, what the scoring model considered, and why it awarded a particular score, incorrect evaluations become diagnosable and fixable. This is especially important for regulated industries like fintech, where a QA score may need to withstand compliance review.
What Does Production-Grade Multilingual QA Actually Look Like?
A related but distinct question is what separates a multilingual QA capability that works in a pilot from one that holds up at enterprise volume. The difference comes down to three things: language coverage, scoring consistency, and the ability to handle code-switching without degradation.
RevelirQA scores conversations in Bahasa Indonesia, Thai, and Tagalog in production at scale. Xendit and Tiket.com run thousands of tickets per week through the platform. Each evaluation retrieves the relevant SOP from a vector database before scoring, applies a consistent QA scorecard regardless of the agent's language choice, and produces a full reasoning trace. When an Indonesian fintech agent handles a virtual account query entirely in Bahasa Indonesia, the score reflects whether the response matched the policy, not whether it matched a generic English-language benchmark.
Frequently Asked Questions
Does AI scoring work reliably for Bahasa Indonesia fintech terminology?
It depends entirely on how the scoring engine is built. Generic models struggle with domain-specific Indonesian vocabulary [3]. Scoring engines that retrieve your own fintech SOPs before evaluating each conversation handle this correctly because the policy document provides the domain context the model needs.
What is code-switching and why does it affect QA scoring?
Code-switching is the practice of alternating between two languages within a single conversation or sentence. In Filipino customer service, Taglish (Tagalog-English mixing) is standard. Generic AI models often lose coherence across the language boundary, making it impossible to accurately assess whether the full response was compliant.
Is a translation glossary enough to fix terminology gaps in AI QA?
No. Translation glossaries improve consistency for human translators [1] but do not solve real-time scoring. The scoring engine needs to understand domain meaning at evaluation time, which requires grounding in your actual policies, not a static word list.
Why does manual QA sampling fail multilingual teams specifically?
Manual QA typically reviews 1-5% of tickets, and reviewers tend to pull familiar-language tickets. Multilingual or code-switched conversations are underrepresented in that sample, meaning systematic errors in Thai or Tagalog responses go undetected for longer.
What is a QA scorecard in the context of AI quality assurance?
A QA scorecard is a defined set of criteria used to evaluate customer service conversations. Criteria can be binary (pass/fail), multi-option, or scored on a scale. In AI QA, the scorecard is applied automatically to every conversation rather than to a manual sample.
How does retrieval-augmented generation (RAG) help with multilingual QA?
RAG allows the scoring engine to retrieve relevant policy documents from a vector database before evaluating each conversation. This means the engine assesses agent responses against your actual SOPs, written in your business's language and terminology, rather than relying on generic language model knowledge.
Do enterprise QA teams need separate scoring configurations for each language?
Not if the scoring engine is built to handle multilingual input natively. The goal is a single consistent QA scorecard applied across all languages, so a Bahasa Indonesia ticket and an English ticket are evaluated on the same criteria. Separate configurations create inconsistency and maintenance overhead.
Revelir AI is an AI quality assurance platform that scores 100% of customer service conversations against each client's own policies and QA scorecard, using RAG to retrieve the relevant SOP before every evaluation. The platform produces a full reasoning trace on every score, giving QA and compliance teams an auditable record. Enterprise clients including Xendit and Tiket.com run RevelirQA at scale across Bahasa Indonesia, English, Thai, and Tagalog, with proven support for high-volume, multilingual operations. Revelir is built for global enterprise teams running multilingual support operations, with engineering and product expertise rooted in Southeast Asian market needs.
Ready to close the glossary gap in your multilingual QA programme?
Learn how RevelirQA scores 100% of your conversations in Bahasa Indonesia, Thai, and Tagalog against your own policies.
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References
- Machine translation glossaries for international businesses - GAI (www.gaitranslate.ai)
- In the Year 2025, the Indonesian Comprehensive Dictionary (KBBI) was Expanded to Include 3,259 New Entries - (tercetar.kemendikdasmen.go.id)
- Indonesia Translation Market: 2025 Industry Analysis | TRANSLIFE (www.translife.co)
- Speaking in Code: Contextualizing Large Language Models in Southeast Asia | Carnegie Endowment for International Peace (carnegieendowment.org)