Encore Compliance, a leader in AI-driven compliance solutions, developed a platform to automatically transcribe and analyze expert network calls, aiming to detect potential risks such as the disclosure of Material Non-Public Information (MNPI). As regulatory scrutiny intensified, ensuring the accuracy and reliability of their LLM-based system became paramount.
PressW Labs partnered with Encore Compliance to refine their LLM architecture and enhance the system's performance. The collaboration focused on several key areas:
Photo Capture: Advanced vision model accurately identifies key facial features
Trait Extraction: Developed structured prompts to guide the LLMs in identifying compliance risks more effectively
Chain-of-Thought Reasoning: Implemented techniques to enable step-by-step processing, improving interpretability
Model Selection: Assessed various LLMs for optimal performance and computational efficiency
Testing Frameworks: Established robust protocols to evaluate outputs against compliance benchmarks
Key Insights
The project demonstrated that:
Tailored prompt engineering significantly enhances the model's ability to detect nuanced compliance issues
Incorporating chain-of-thought reasoning improves the transparency and reliability of AI-driven analyses
Strategic model selection is crucial for balancing performance with resource constraints