How a Fintech LLM Reached Production in 6 Weeks Using Nextura.ai OTS Datasets
A global LLM platform building an AI financial assistant needed production-grade, multilingual, domain-specific conversational data — fast. Nextura.ai's OTS library plus a targeted hybrid layer compressed months of data work into days.
Client Profile
A global LLM platform developing a model that helps banking end users across personal loans, credit scoring, and investment advisory. The product team had built a proprietary LLM-powered financial assistant preparing for a public beta launch across South and Southeast Asia — covering Hindi, Tamil, Telugu, Bengali, Marathi, Kannada, Bahasa Indonesia, Thai, Vietnamese, Tagalog, Sinhala, and English across Tier 1 and Tier 2 cities in India, Indonesia, Vietnam, the Philippines, and Sri Lanka.
A production-ready model, held back by data gaps
The fintech company had developed a strong model architecture for their AI financial assistant. They had access to their own transaction data and user interaction logs. But they faced a critical gap: they did not have the volume, diversity, or quality of conversational training data required to make the model safe, accurate, and genuinely useful for real users.
Existing dialogue data was almost entirely in English. Target users are predominantly Hindi-English code-switchers — the model performed well in standard English but hallucinated or gave irrelevant answers in Hinglish or formal Hindi.
Training a general-purpose LLM to be an accurate financial assistant requires thousands of financial dialogues — EMI calculations, mutual fund recommendations, credit score interpretation, GST compliance. Internally generated data covered fewer than 800 unique prompts, far below the 5,000+ required for reliable generalization.
The model generated responses but the team had no mechanism to steer it toward preferred outputs. Building preference pairs in-house would have required a team of financial domain experts and 6–8 weeks of annotation work.
Public beta was scheduled in six weeks. Building a custom data pipeline from scratch was ruled out — the team needed a data solution deployable immediately without sacrificing quality or compliance.
A rapid-response OTS data package
The fintech team engaged Nextura.ai's OTS dataset team for a rapid-response data package. Within 48 hours of the initial briefing call, Nextura.ai identified three matching datasets from the OTS library and initiated a hybrid curation layer for the one remaining gap.
| Dataset Component | Details |
|---|---|
| Hindi-English Code-Switching Dialogue Corpus | 12,400 multi-turn conversational exchanges in natural Hinglish covering financial queries, complaint handling, and product inquiries. Includes speaker intent labels, sentiment scores, and entity tags for financial terms. |
| Financial Domain Instruction-Tuning Set | 6,200 instruction-response pairs across personal finance, investment, lending, insurance, and regulatory queries. Expert-authored by certified financial domain annotators. Covers 40+ financial task categories. |
| RLHF Preference Pair Dataset — Finance Vertical | 3,800 ranked response triplets (prompt + chosen response + rejected response) built specifically for financial assistant alignment. Rejected responses include hallucinated figures, non-compliant advice, and vague deflections. Chosen responses demonstrate accurate, clear, and appropriate financial guidance. |
| Custom Hybrid Layer — RBI Regulatory Dialogue | 750 additional instruction pairs covering RBI guideline queries, loan moratorium questions, and digital payment compliance — curated by Nextura in 10 business days as a targeted extension to the OTS package. |
Delivery & Integration
All datasets were delivered in JSON and HuggingFace-compatible Parquet format within 3–5 business days of agreement sign-off. Each dataset shipped with a structured data card covering annotation methodology, quality metrics, inter-annotator agreement scores, and licensing documentation. The client's ML engineering team integrated the datasets into their fine-tuning pipeline within one day, using the format specifications provided.
Measured results in six weeks
| Metric | Result |
|---|---|
| Time to training-ready data | 3–5 business days from agreement (vs. 8–10 weeks estimated for a custom pipeline) |
| Model accuracy on financial queries (Hindi) | Improved from 51% to 84% after fine-tuning on OTS + hybrid dataset |
| RLHF alignment score (internal benchmark) | Preference win-rate increased from baseline 38% to 73% post-alignment training |
| Regulatory compliance queries — hallucination rate | Reduced from 29% to 4.2% after RBI regulatory dialogue integration |
| Beta launch timeline | Met on schedule — 6 weeks from data delivery to public beta |
| User satisfaction (beta feedback) | 4.3/5 rating on response accuracy from 8,400 beta users in first two weeks |
| Cost vs. custom data pipeline estimate | 68% reduction in data acquisition cost compared to a custom collection and annotation quote |
"We were four weeks from a public launch with a model that wasn't ready. Nextura.ai's OTS team understood our domain, matched us to the right datasets immediately, and filled our only remaining gap with a targeted custom layer. The data quality was audit-ready and the integration was seamless. We launched on time."
Off-the-shelf datasets are not a compromise when you are building a production AI product — they are a strategic accelerant. The right OTS data, matched precisely to a model's domain and language requirements, can compress a custom data pipeline that would have taken months into a matter of days, without sacrificing the quality, diversity, or compliance standards that enterprise AI demands.