Here’s how we built an automated pipeline for extracting data from financial statements (10-Ks, 10-Qs, annual reports) into our analysis models.\n\n## Architecture\n\n1. Source: SEC EDGAR filings or direct uploads from analysts\n2. Processing: Qomplement API with custom templates\n3. Storage: Results → PostgreSQL → dbt models\n4. Output: Analyst dashboards in Grafana\n\n## Key Templates\n\nWe created templates for:\n- Income Statement: Revenue, COGS, operating expenses, net income\n- Balance Sheet: Assets, liabilities, equity line items\n- Cash Flow Statement: Operating, investing, financing activities\n\n## API Call Example\n\nbash\ncurl -X POST https://api.qomplement.com/v1/parse \\n -H 'Authorization: Bearer $API_KEY' \\n -F 'file=@annual_report.pdf' \\n -F 'template_id=fin_income_statement'\n\n\n## Tips\n\n- Table extraction is key: Financial statements are all about tables. The table extraction feature handles multi-page tables well.\n- Footnotes matter: We extract footnotes separately using a second pass with a different template.\n- QC check: We validate that balance sheets actually balance (assets = liabilities + equity) as a sanity check on extraction quality.\n- Historical comparison: By processing 5 years of filings we can automatically populate trend analysis models.\n\n## Results\n\nProcessing time went from 2 hours per filing (manual) to about 5 minutes (automated + review). Our team of 3 analysts can now cover 3x more companies.
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The balance sheet validation check is such a smart QA step. We do something similar for tax returns.
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