Naomie Halioua
Co-founder & CRO, AI Research
How Multi-Agent AI Scores Regulatory Risk: Inside Cleo's Pipeline
A single LLM call cannot reliably assess regulatory risk. Cleo uses a 5-stage pipeline with 30+ specialized agents, achieving 98.5% accuracy and a 0.81 F1 score across 12,000+ determinations.
The 5-stage pipeline
Profile: AI agents analyze the company's domain, products, markets, and data practices
Discover: Specialized agents scan 3,500+ sources to identify applicable frameworks
Score: Risk scoring agents evaluate severity × likelihood for each regulation
Enrich: Context agents add obligation details, deadlines, and enforcement history
Assess: Synthesis agents generate executive-ready risk reports with recommendations
Why multi-agent beats single-model
Each scan involves 30+ specialized LLM calls and 420-1,200 web search calls. Purpose-built agents reduce hallucination risk because each agent operates within a narrow, well-defined scope. Cross-validation between pipeline stages catches errors early. The result: 98.5% accuracy across 12,400+ regulatory determinations, with full source traceability on every output.
Frequently asked questions
How does multi-agent AI work for regulatory scoring?
Multi-agent AI for regulatory scoring uses a pipeline of 30+ specialized agents, each handling a distinct task: company profiling, jurisdiction identification, framework discovery, risk scoring, and report generation. Each agent is purpose-built and fine-tuned for its specific task, reducing hallucination risk. The agents collaborate in a 5-stage pipeline (Profile → Discover → Score → Enrich → Assess) with cross-validation between stages.
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