The client was struggling with fragmented metadata across research, clinical, and regulatory teams. Data lineage was largely tribal knowledge, with teams maintaining isolated Excel trackers, Visio diagrams, and scattered wikis. This fragmentation led to repeated validation cycles, compliance risk, and delayed onboarding of new R&D tools.
Manual metadata documentation couldn’t scale — especially as regulatory scrutiny around data provenance continued to rise.
Seawolf AI deployed Structura, a modular AI-driven framework that combined:
OpenMetadata integration to act as the metadata backbone
Python SDK and Airflow DAGs for automated ingestion and lineage capture
- LLM-based enrichment to infer missing documentation, classifications, and glossary terms.
Key solution components:
Automated Lineage Mapping
Pipeline and system metadata was captured from tools like Snowflake, dbt, and Airflow to auto-generate end-to-end lineage diagrams with no manual upkeep.AI-Generated Glossaries
LLMs parsed table names, fields, and query patterns to recommend business definitions and align them to domain-specific terms.Human-in-the-Loop Validation
Analysts could review, accept, or flag AI-suggested metadata in a friendly interface, creating a scalable feedback loop.Unified Metadata Search
All metadata — technical, business, and operational — was centralized in a single search experience with natural language querying.
For the first time, we can track how data flows across our organization — not just technically, but semantically. Seawolf gave us both the engine and the intelligence to make metadata usable.
Global Head of R&D Data Strategy
Reduced manual metadata entry by 70%
Enabled real-time lineage visualization across 5+ systems
Accelerated compliance audit preparation by weeks