FDD Case Study
Reimagining Financial Modeling for a $40B Private Equity Firm

Overview

A $40B private equity firm specializing in franchising faced a bottleneck: their analysts and junior investors, earning six-figure salaries, were bogged down by a dated, manual modeling process that consumed hundreds of hours. Pathlit stepped in to automate and consolidate this tedious workflow, transforming how the firm interfaces with data and freeing up talent for higher-value work.

Challenges

  • Time-Intensive Modeling: Extracting data from 300-page franchising Confidential Information Memorandums (CIM) and inputting it into financial models took hours per deal.
  • Overburdened Analysts: Highly paid professionals were stuck on monotonous data entry instead of strategic analysis.
  • Inefficient Scaling: The manual process couldn’t keep pace with the firm’s growing deal volume.
Before

Solution

Using Pathlit’s no-code AI automation platform, our team built a workflow that fills out complex data spreadsheets tailored to the firm’s needs, seamlessly integrating their existing tools and automating the modeling process. Key features included:

  • Rapid Data Extraction: AI agents parsed 300-page CIMs and populated financial models in minutes, not hours.
  • Workflow Automation: Eliminated manual data entry, reducing repetitive tasks by over 90%.
  • Real-Time Connectivity: Linked unstructured franchising data to actionable outputs with no custom coding.
  • Scalable Design: Adapted to the firm’s high-volume deal pipeline with enterprise-grade reliability.
After

Results

With Pathlit, the firm slashed modeling time from hours to minutes per CIM, enabling large-scale efficiency across their portfolio. Analysts now spend less time on brain-numbing data transfers and more time dissecting business mechanics and focusing on high-impact priorities. The solution not only saved hundreds of hours but also unleashed the firm’s talent to drive smarter investment decisions, proving Pathlit’s ability to turn AI into a strategic game-changer.