Specialised AI + structured regulatory data: what it means for RegTech pipelines
General-purpose LLMs are remarkable, but in compliance workflows reliability beats novelty.
New research from RegGenome shows that a specialised, domain-tuned approach outperforms generalist models on the metrics that matter in production: accuracy, stability, cost, and end-to-end review time.
In like-for-like tasks, the specialist model delivered a 38% relative accuracy gain at roughly
1/80th the cost and 1/200th the energy, while cutting human review time by around
40% in a realistic human-in-the-loop pipeline. In “turbulent” domains (e.g., crypto), the gap widens further
(up to +21 percentage points at a granular classification level).
For solution providers, the implication is clear: reliable automation starts with
high-fidelity, source-linked structured data feeding your models and applications – not with generic models
over unstructured, scraped content.
At-a-glance results from the study
- Accuracy: +38% relative lift vs. general LLMs
- Stability: far fewer answer swings across provider versions
- Efficiency: ~1/80th cost and ~1/200th energy
- Throughput: ~40% less human review time in HITL workflows
- Domain stress-test: bigger gains in fast-changing areas (e.g., crypto)
What this means for solution providers
- Stabilise pipelines: Reduce rework by pairing models with source-linked inputs that don’t drift with provider updates.
- Scale confidently: Lower unit costs unlock more frequent re-processing and coverage expansion.
- Ship faster: Free expert time from extraction/clean-up to higher-value assurance and product features.
- Stay defensible: Maintain audit-ready lineage back to exact clauses and authoritative sources.
Our role
This research validates why specialised approaches over structured, source-linked regulatory data outperform
“just use a general LLM.” RegGenome provides the data backbone – AI-optimised, machine-consumable regulation
your products can trust.
