From Days to Hours: Automating Valuation Reports

The Challenge

A boutique investment bank was drowning in manual work. Their valuation team—skilled analysts with deep financial expertise—spent the bulk of their time on tasks that didn't require that expertise:

• Data extraction: Reading through PDF financial statements, manually copying numbers into spreadsheets
• Model population: Transferring data point-by-point into Excel valuation templates
• Report generation: Building PowerPoint decks slide-by-slide
• Research: Manually searching for industry comparables and market benchmarks

Each report took 2-3 days of focused analyst time. The bottleneck wasn't thinking—it was typing.

The Process Before & After

Before: Manual Workflow

1. Receive client financials (PDF)
2. Analyst reads and extracts key figures (2-3 hours)
3. Data entered into Excel model manually (1-2 hours)
4. Analyst researches industry benchmarks (2-3 hours)
5. PowerPoint deck built from template (2-3 hours)
6. Review and revisions (1-2 hours)
7. Total: 10-15 hours per report

After: Automated Workflow

1. Upload client financials to dashboard
2. AI extracts all financial data (minutes)
3. Excel model auto-populated, flagged for review
4. Industry benchmarks pulled and compared
5. PowerPoint generated from data
6. Analyst reviews final output (1-2 hours)
7. Total: 2-3 hours per report

What We Built

The Extraction Agent

The first piece: an agent that reads financial statements like an analyst would. It understands the structure of income statements, balance sheets, and cash flow statements—extracting not just numbers, but context.

The extraction dashboard shows exactly what was pulled, with confidence scores for each field.

The Research Agent

While extraction runs, a parallel agent gathers industry data. It pulls comparable company metrics, industry standard ranges, and market benchmarks—then flags any client numbers that look unusual.

The Model Population System

Extracted data flows directly into the firm's existing Excel templates. No new tools to learn—the same models they've used for years, now populated in seconds instead of hours.

The Report Generator

Finally, the PowerPoint agent assembles client-ready decks. Charts, tables, and narrative sections—all generated from the underlying data, following the firm's exact style guidelines.

The custom dashboard lets the team see every job's status, retry failures, and review outputs before delivery.

The Results

After 3 weeks of development and testing:

• 100x Throughput: The team can now process batches of 100+ reports, reviewing only the final outputs
• 85% Time Saved: Analyst time per report dropped from 10-15 hours to under 2 hours
• $0 New Tools: Everything integrates with their existing Excel and PowerPoint workflow

What the Team Says

"I was skeptical at first—we've seen 'AI automation' demos before. But this actually works with our real documents, our real templates. The first time I saw it populate a model correctly from a messy PDF, I knew this was different."

— Senior Analyst

Key Takeaways

1. Automation doesn't mean replacement: Analysts still do the thinking. They just don't do the typing.
2. Existing tools matter: We didn't ask them to learn new software. The AI adapts to their workflow, not the other way around.
3. Visibility is everything: The custom dashboard means nothing is a black box. They see exactly what the AI did and can intervene at any point.