45,000 instruction pairs written by financial professionals, not scraped
Domain-specific instruction-tuning data created by active practitioners — not web-scraped patterns.
Client Context & Operational Challenge
An AI company developing a domain-specific language model for the financial services industry needed high-quality instruction-response pairs that reflected real professional workflows — not generic web-scraped patterns. The training data required regulatory awareness, professional register accuracy, and task-specific formatting that no existing dataset provided.
Execution & Governance Model
Recruited financial professionals across 8 sub-domains as instruction authors. Each author created instruction-response pairs reflecting authentic professional tasks — report generation, regulatory interpretation, risk assessment, client communication. A separate review layer verified factual accuracy, regulatory compliance, and professional register. Production operated in themed sprints — one sub-domain per sprint — to enable deep calibration.
Scale & Velocity Constraints
- Instruction sets spanning 8 financial sub-domains from compliance to portfolio analysis
- Responses required professional-grade accuracy verifiable by domain experts
- Regulatory language varying by jurisdiction — requiring 5 market-specific variants per topic
- Training data format requiring structured metadata for curriculum-style model training
- Strict IP constraints — no copyrighted financial content permitted in training samples
What Was Delivered
Asset Outputs & Deliverables
- Delivered 45,000+ verified instruction-response pairs across 8 financial sub-domains over a 6-month engagement. Post-review revision rate under 5%. Model fine-tuned on this dataset outperformed the generic baseline on domain-specific benchmarks by a significant margin. Dataset structure adopted as the template for subsequent vertical expansion.
Operational Footprint
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