Challenge
A FinTech company building a multilingual customer service chatbot for banking and insurance was caught in a dangerous gap: strong benchmark scores (91% intent accuracy) masking poor real-world performance (22% misclassification in production). The gap came from how customers phrase queries: code-switching mixing Hindi and English freely, typos and informal language, domain jargon customers use incorrectly or colloquially, and Tamil and Telugu segments barely represented in training data. Every misclassified intent meant either a wrong automated response or a costly agent escalation.
Solution
Phase 1 Realistic Data Collection: Crowdsourced 150,000 banking query utterances across 5 languages: Hindi, Hinglish, English, Tamil, and Telugu. Contributors instructed to phrase queries naturally. Topic coverage: account management, fund transfers, loans, insurance, card services, and more. Phase 2 Multi-Layer Annotation: Intent classification across 85 intent categories. Named Entity Recognition for financial entities: account numbers, amounts, dates, bank names. Sentiment labeling for escalation trigger identification. All annotation by domain-familiar native speakers. Phase 3 Real-World Model Testing via TuTrain: After client retrained NLU model, chatbot deployed to real users via TuTrain. Measured intent accuracy, entity F1, and user satisfaction in real banking interactions. Results fed back into second targeted collection cycle for highest-error intent pairs.
Results
Key outcomes: Intent misclassification dropped from 22% to 4.8% - 78% error reduction. Hindi and Hinglish entity extraction F1 improved from 0.71 to 0.93. 40% reduction in chatbot-to-human-agent escalation rate with direct operational cost savings. TuTrain real-world testing revealed specific failure patterns the internal test set had completely missed.