Helping risk and policy teams accelerate policy coverage reviews with ai-powered virtual assistants
About the Company
The Problem Statement
Regulatory Interpretation Challenges
Regulatory language is complex and inconsistent across jurisdictions, making interpretation difficult.
Unstructured Gap Analysis
Teams lack a structured way to identify areas where internal documents may not fully cover regulatory expectations.
Traceability Challenges
Maintaining traceability and transparency in reviews is time-consuming and prone to oversight.
How LYZR Solved It ?
- Extracts metadata and interprets content from both regulatory and internal documents
- Breaks down regulation documents to generate review prompts and mapping cues.
- Analyses internal documents to assess coverage depth and identify unaddressed areas.
- Provides structured outputs such as evidence excerpts, explanation summaries, relevance scores, and section references, without making any decisions or determinations.
The Outcome
Faster Document Review Cycles
Yardstick’s internal policy and standards review processes were accelerated from days to hours, freeing up valuable analyst time.
Clearer Visibility into Regulatory Coverage
The system surfaces which regulatory themes are fully addressed, partially addressed, or missing in internal documents - enabling focused internal action.
Actionable, Explainable Insights for Review Teams
Outputs include supporting evidence, relevant document sections, and alignment scores that help review teams assess internal readiness without overstepping into judgment.
Behind the Scenes: How Lyzr Builds Enterprise AI on AWS
How Lyzr handled security?
Data Residency
All change management data remains securely within NTT Data’s Azure cloud, ensuring 100% ownership and compliance with regional data laws.
Compliance
The Lyzr Agent Platform is SOC2, GDPR, and ISO 27001 compliant, supporting enterprise-grade data handling and privacy.
Reflection Module
Lyzr’s reflection system minimizes model hallucination, ensuring high-accuracy, reliable recommendations in critical workflows.
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