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Goal: go from a target’s financial statements to an executive-ready comparable transactions valuation, with a recommended enterprise value range and the reasoning behind it.

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Setup

Load a Brain with whatever deal documentation you have:
  • Financial statements for the target: the minimum viable input. The App runs with only these.
  • Comparable transaction extracts from private databases, if you have access. The walkthrough adds a transaction list exported from one.
In the demo, the target is a fragrance company with financial statements and a private-database transaction list loaded. Select the documents, save the selection, and run the App.
No private database access? The App’s deep web research blocks screen for comparable transactions from publicly available information, so you still get a usable comp set.

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How it works

The App runs as a sequence of blocks, each building on the last:
1

Build the target profile

The App extracts key information from your documents to give every later step business context.
2

Extract the financials

The next block pulls all financial data from the target’s documents: income statement, balance sheet, and supporting tables. This is the foundation of the valuation.
3

Analyze comparable transactions

The App reviews the transactions consolidated from your private-database extracts and structures them for you.
4

Screen for external transactions

Additional deep web research finds comparable transactions beyond your documents, combining paid-database data with public information.
5

Analyze the deal context

A later block combines internal documentation and external research to consolidate the remaining transaction elements.
6

Generate the final report

The final block compiles every prior step into a comprehensive valuation report.

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What you get

An executive-ready summary delivering a recommended enterprise value range, grounded in three pillars: the target’s business model, its financial fundamentals, and the comparable transactions. Each section includes the detailed reasoning supporting the final investment recommendation; nothing arrives as an unexplained number. You can also customize the output format (more visual or more tabular) to match how your team consumes valuation work.

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Pro tips

  • Verify the comp set before the number. The EV range is only as defensible as the transactions behind it. Review the comparable list and its sources before presenting; every data point traces back to a document or a web source.
  • Richer inputs, tighter range. Financial statements alone work, but adding private-database extracts gives the App a verified comp set to anchor on instead of relying solely on web screening.
  • Pair with a DCF. Triangulate the comps-based range against the DCF valuation App on the same Brain.

Valuation: DCF

Cross-check the range with an intrinsic valuation

Data Room Gap Analysis

Confirm the data room supports the analysis first

App Blocks

How sequential blocks chain context from step to step

Refining and Exporting

Turn the report into a client-ready deliverable