The solution starts with event-driven ingestion of plant-level PDF reports. Each new report triggers a Databricks Job that parses the document and applies LLM‑based extraction. Extracted elements are serialized as JSON and stored in Delta Lake, which maintains full version history for audit and replay.

To address the fundamental problem that maintenance information lives almost entirely in unstructured PDFs, Plenitude uses Databricks Document Intelligence AI Functions — specifically ai_parse_document, to extract multiple types of elements from each page, including text blocks, tables, figures and metadata. Each element is enriched with attributes such as plant, reporting period, page number and content type, and every record maintains a direct link back to the original report for traceability.

Instead of static files, maintenance reports become a persistent data layer ready for advanced analytics and agent reasoning.

The architecture is organized into three main layers: ingestion and parsing, data structuring and agent-based interaction.