Most orgs don’t have a knowledge problem—they have a structure problem. Turning scattered PDFs, emails, and slides into a structured reference library is the key to turning piles of files into useful data.
PDFs, emails, and slides are where truth lives but these files are most often scattered around in shared drives, too overwhelming to explore and only useful if you know where to look.
Real-World Use Case: Medicaid Audit Reports
I built a web tool that compiles, extracts, and indexes one domain: Medicaid audit reports across the U.S. Although these reports share common elements, like objectives, scope, conclusions, findings, recommendations, every publisher formats them differently. That variation makes reading slow and comparison harder.
Check it out here: https://medicaidaudit.org
The core idea: AI → Structure → Library
AI extracts fields from each report into a schema defined to reflect common audit report information and it’s saved to a database.
This shifts PDFs from “files to hunt for and sift through” to a easily searchable reference library you can actually use for audit planning, policy updates, risk assessment, and more.
About the Tool
Medicaid Audit Intelligence is a web based tool that presents the extracted data from audits of the Medicaid program with links to the source report. You can selected reports through the dashboard map or search/filter reports on keywords, agencies, or year published.
At this point, the tool is still under construction, there could be errors in the AI output and some variation in the names of the entities extracted.
If you work with Medicaid oversight (or similar document-heavy domains), I’d love your feedback and ideas for the next iteration.
