The largest U.S. rural-lifestyle retailer had data its teams could not trust the meaning of. We built a RAG architecture that generates definitions, lineage, and business context and writes them into the catalog, then modernized the governance and AI operating model around it.
A catalog that works as a living knowledge layer rather than static documentation, with governance and Responsible-AI controls to keep it trustworthy.
Growth had outpaced understanding. The data existed, but trust in its meaning, ownership, and readiness for AI was inconsistent.
A RAG architecture with LLMs generating contextual metadata directly into the catalog, paired with an enterprise AI roadmap and a modernized governance operating model.
A catalog that works as a living knowledge layer rather than static documentation, with governance and Responsible-AI controls to keep it trustworthy.
A Retrieval-Augmented Generation architecture with LLMs automating contextual metadata generation. Sources included catalog metadata APIs, collaboration systems such as SharePoint and Confluence, the business glossary and usage logs, and directory and graph services. The RAG engine synthesizes definitions, lineage, sensitivity, and business context, then writes the enriched metadata directly into the catalog.
An enterprise AI roadmap aligned to the company's growth strategy: capability assessment across governance, architecture, analytics, and model operations; formalized MLOps and LLMOps practices; a Responsible-AI framework aligned with existing data governance; and steward training and role enablement.
A refined governance charter and policy framework, accountable roles for data stewards and owners, lifecycle controls for intelligent models, and data-quality KPIs with governance metrics — so governance enables growth rather than only protecting against risk.
The retailer sits at the crossroads of retail, agriculture, animal care, and DIY land use, serving farmers, ranchers, land stewards, pet owners, and hobby-agriculturists. Under a multi-year growth plan it is targeting a total addressable market above $225 billion, expansion toward roughly 3,200 stores, and accelerated growth in pet and animal-care services.
That growth created a structural bottleneck of a specific kind. The data existed. What was inconsistent was trust in its meaning, its ownership, and its readiness for AI — and none of those gaps are visible until someone tries to build on top of them.
Hand-tagging was never going to close it. The volume of assets grows with the business, and manual documentation degrades the moment attention moves elsewhere. So we automated the generation of meaning rather than the collection of it. A RAG architecture reads from catalog metadata APIs, collaboration systems, the business glossary, usage logs, and directory and graph services, then synthesizes definitions, lineage, sensitivity, and business context and writes them straight back into the catalog. The catalog stops being documentation about the data estate and starts being part of it.
Generated metadata still has to be governable, which is why the second half of the work was the operating model — a governance charter, accountable stewards and owners, lifecycle controls for intelligent models, and data-quality KPIs. Alongside it, an enterprise AI roadmap with formalized MLOps and LLMOps practices and a Responsible-AI framework aligned to the existing governance rather than bolted beside it.
The point of all of it is what comes next: high-value AI use cases like fraud detection, supply-chain optimization, and animal-care services, built on assets with traceability and trust behind them.
Where to next
