How I Built the Financial Statement Analyzer
Schema-first AI extraction, ratio computation and competitor benchmarking — architecture and lessons from building an automated financial analysis pipeline.
The Financial Statement Analyzer started from a simple problem: annual reports contain the information leaders need, but extracting it manually is slow, inconsistent and hard to compare across companies. I wanted a tool that could turn a PDF into a structured financial view with ratios, commentary and competitor benchmarks.
How I Built It
The first design decision was to make the pipeline schema-first. Instead of asking an AI model for a free-form summary, the system extracts specific statements, line items, periods and notes into a structured format. That makes every downstream calculation easier to audit.
The backend handles PDF parsing, section detection, financial table extraction and ratio computation. The AI layer is used where interpretation is useful: mapping messy labels to standard accounts, explaining movements and generating business-readable commentary from the computed results.
What It Does
The tool supports annual report upload, extraction of income statement, balance sheet and cash flow data, ratio analysis and peer benchmarking. The output is designed for review, not blind automation: users can inspect the extracted numbers before relying on the analysis.
What I Learned
The biggest lesson was that structured extraction beats clever prompting. Once the data contract was stable, the rest of the system became much easier to improve: calculations were repeatable, errors were visible, and the AI could focus on interpretation rather than guessing the shape of the data.