How a Leading Private Equity Firm Reduced Financial Model Preparation From 10 Hours to Under 5 Minutes with Lyzr

For investment teams, the quality of analysis often depends on how quickly raw financial data can be converted into a model-ready format. 

Yet across private equity and M&A workflows, analysts continue to spend hours manually cleaning spreadsheets, reconciling categories, and wiring formulas before actual diligence can begin.

The private firm equity faced the same challenge. Each acquisition target arrived with financial data structured differently, creating significant manual effort before analysts could begin evaluating opportunities. 

To eliminate this bottleneck, the firm partnered with Lyzr to build an AI-powered Financial Extractor that automates financial preparation while preserving the firm’s existing LBO modeling process.

Why Financial Due Diligence Often Starts With a Spreadsheet Problem? 

Most acquisition targets don’t provide financial information in a standardized format.

A typical diligence package contains multiple Excel sheets spanning several years, with different naming conventions, inconsistent categorization, merged cells, subtotals, and varying chart-of-account structures. Before any valuation work can begin, analysts first need to transform this information into a format that aligns with their firm’s financial models.

The challenge isn’t gathering the data. The challenge is making that data usable.

Across private equity firms, this preparation process often includes:

• Consolidating financial data from multiple tabs
• Standardizing category names across periods
• Building historical financial views
• Mapping categories into modeling templates
• Creating formula dependencies that remain dynamic
• Validating outputs and formatting deliverables

While these activities are essential, they add little analytical value. They consume hours of analyst time while introducing opportunities for human error.

When Manual Financial Preparation Became a Bottleneck at the leading Equity Firm

This firm’s investment team reviews dozens of acquisition opportunities every year.

Each new diligence cycle began with a familiar process: receiving a client workbook containing multiple reporting periods and manually converting that information into the firm’s LBO template structure.

The process was highly dependent on analyst effort and institutional knowledge.

What analysts were spending time on

The issue wasn’t simply time.

The workflow created several operational risks:

Key challenges

By the time preparation was complete, analysts had already spent most of a day on spreadsheet engineering rather than investment analysis.

Why the leading Equity Firm Needed More Than Another Data Extraction Tool

Many automation solutions focus on extracting data.

This firm’s requirement was different.

The firm already had established modeling conventions, reporting formats, review processes, and LBO templates. Replacing those systems would create more disruption than value.

Instead, the goal was to eliminate manual preparation while preserving everything analysts already trusted.

The ideal solution needed to:

• Work with any client workbook structure
• Preserve existing LBO templates
• Standardize financial categories automatically
• Generate formula-linked outputs
• Maintain analyst oversight
• Support repeatable and auditable workflows

Most importantly, the final deliverable needed to look and behave exactly as if an analyst had built it manually.

Building an AI-Powered Financial Extractor Around Existing LBO Workflows

Lyzr worked with the firm to develop a purpose-built Financial Extractor designed specifically for investment analysis workflows.

Rather than introducing a new modeling environment, the platform acts as an intelligence layer between raw client financials and the firm’s existing LBO model.

The outcome is simple:

Upload a client workbook. Download a model-ready version of the same workbook with consolidated and mapped financials already prepared.

At the center of the solution is a multi-agent architecture that automates extraction, normalization, mapping, and workbook generation.

Core capabilities delivered

How the Workflow Moves From Raw Workbook to Model-Ready Output

Step 1: Upload and Analyze

Analysts upload a client workbook without making any modifications. The platform stores the file and initiates the processing workflow.

Step 2: Four Specialized AI Agents Process the Data

Instead of relying on a single extraction engine, the platform distributes responsibilities across multiple agents.

The result is a clean and normalized financial dataset ready for review.

Each agent run is persisted, with a SHA-based cache so the same file uploaded twice reuses the prior extraction.

Step 3: Blue-Marking for Analyst Review

The analyst reviews the consolidated output and selects the rows that should flow into the final model.

Selections are automatically persisted, allowing analysts to continue their work later without losing progress.

Step 4: Intelligent Financial Mapping

A dedicated Financial Mapper agent proposes how each extracted category should align with the firm’s LBO structure.

Analysts retain full control and can:

• Modify labels
• Change mappings
• Add rows
• Remove rows
• Regenerate recommendations

This keeps the analyst in control while dramatically reducing manual effort.

Step 5: Generate the Final Workbook

Upon approval, the platform creates a new workbook that:

• Preserves every original worksheet
• Adds a Combined (Extracted) sheet
• Adds a Financials sheet
• Generates live SUMPRODUCT formulas
• Applies firm-specific formatting conventions
• Opens correctly across Excel environments

The result is a model-ready workbook with no manual spreadsheet engineering required.

The Product Features Analysts Ended Up Using Every Day

While automation drove efficiency gains, several workflow enhancements played a critical role in adoption.

Features designed around analyst workflows

These capabilities helped the platform fit naturally into existing diligence processes.

What Changed After Deployment

The impact was visible almost immediately.

Instead of spending hours preparing spreadsheets, analysts could begin reviewing financial performance and building investment theses within minutes.

Knowledge that previously lived inside the heads of senior team members became embedded into the workflow itself.

Processes became repeatable, auditable, and consistent across the organization.

Before and after

More Time for Analysis, Less Time for Spreadsheet Engineering

The biggest outcome wasn’t faster spreadsheets.

It was giving analysts their time back.

By automating extraction, normalization, mapping, and workbook generation, the equity firm converted one of the most repetitive stages of financial diligence into a repeatable workflow that runs in minutes. 

Analysts can now focus on evaluating businesses, validating assumptions, and uncovering investment opportunities instead of manually preparing financial models.

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