Yggdrasil

Compliance teams manually interpret PDF policies and run SQL checks, which is a slow, error-prone process with no automation bridge between policy documents and database enforcement. SMEs and startups face mounting GDPR, SOC2, and AML audit pressure without dedicated compliance teams or tools to continuously monitor violations.

Yggdrasil is an autonomous compliance engine that bridges the gap between PDF policy documents and database enforcement. It automatically extracts compliance rules from policies using Gemini AI and scans databases/CSV files for violations that allows for eliminating manual interpretation and SQL testing.

Deterministic Rule Extraction: Gemini AI parses policy PDFs into structured JSON rules. For every extracted rule, the system explicitly captures the corresponding policy excerpt, creating a verifiable, end-to-end trace from legal language to executable enforcement logic.
Semantic Schema Mapping: Upon data upload, the system proposes mappings between dataset columns and rule parameters. These mappings require explicit user review and approval. This glass-box workflow eliminates black-box ambiguity and ensures the AI’s interpretation aligns with the user’s actual business context.
Transparent Enforcement: The enforcement engine applies deterministic checks to the dataset. Each detected issue generates a violation packet containing: (a) the violated rule, (b) the exact data record used as evidence, (c) the originating policy excerpt, and (d) a clear, human-readable explanation of why the violation occurred.
Actionable Explainability: The system goes beyond risk scores by providing concrete remediation guidance for every violation, including suggested SQL-level fixes where applicable. Each compliance decision is therefore not only detectable, but verifiable, defensible, and audit-ready.