A PE-backed federal healthcare staffing company places clinicians at military treatment facilities and VA hospitals nationwide, pricing roughly 100 government RFPs a year across about 390 labor categories.
Each RFP response ran ~70 pages with custom Excel pricing sheets, consuming six to seven people per bid across FP&A and program management. Years of bid history sat unstructured across decks, documents, spreadsheets, and PDFs in SharePoint, and bids were being disqualified for pricing outside the government's acceptable band before they were ever read.
AI extraction agents read every historical bid file into a queryable database of 4,000+ opportunities and 37,000+ pricing line items. A custom ML model trained on that history predicts the winning price for each new federal RFP and screens it for compliance before submission, with the structured data delivered into Salesforce and a pricing workbench the team uses daily.
In production, the model cut the firm's bid pricing error by 42%.
One in three recent debriefed losses traced to prices below the government's compliance floor, now caught before submission.
About 100 government RFPs a year now priced against the model and screened before submission.
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