How we built an intelligent chart of accounts mapper that encodes firm-specific judgment into code, reducing deal mapping from 40-80 hours to under 4 hours of review.
Interactive Demo
Walk through how the acquisition intelligence platform maps, classifies, and consolidates financial data across portfolio companies.
The Client
A private equity-backed platform executing a roll-up strategy. Every acquisition arrives with its own chart of accounts, revenue categorization, and accounting hygiene. Consolidating them into the parent's standardized financial model is a prerequisite for every deal and every month-end close that follows.
The client needed to scale deal throughput without scaling headcount, and they needed the consolidated numbers to be consistent across acquisitions so board reporting, covenant compliance, and audit readiness didn't degrade as the portfolio grew.
The Challenge
For each target, an analyst reviewed hundreds of GL accounts line by line, mapped each to the parent's revenue and expense categories, cross-referenced prior deals to stay consistent, and redid the whole process every time the target's books were re-pulled. That consumed 40-80 hours per deal before any real analysis could begin.
The Solution
Rather than a generic rules engine, we built a classifier trained on two dozen completed acquisitions' worth of ground truth. The engine encodes the firm's specific judgment calls. Every correction an analyst makes feeds back into the training set. The system gets smarter with every deal.
Results
Beyond the Mapping
Because the mapping engine sits at the front of the pipeline, everything downstream inherits its structure automatically. The same foundation unlocks hours of downstream analyst work across the full deal lifecycle.
Normalized P&L, revenue and MRR views, churn staging. Generated in the firm's own template with formulas preserved.
The same clean, mapped data feeds an AI-assisted deck generator that drafts the investment write-up with commentary on revenue concentration and material findings.
"The system encodes our judgment. Every correction makes it smarter."
Senior Analyst, PE-Backed Platform
Key Takeaways
When a senior staff member makes a judgment call, that decision becomes part of the engine's training set. Expertise becomes a durable asset, not a personnel dependency.
Every classification has a confidence score and a rationale. No more reconciling scattered workbooks after the fact.
Every correction your team makes improves the next classification. By month 3, the engine is meaningfully more accurate than go-live.
Same transaction type gets coded the same way, every time. Audit findings from inconsistent coding go away.
Whether it's GL accounts, work orders, transactions, or documents, the same AI classification pattern applies. Let's talk about yours.