Fin Report Analyzer:
Automated Spreading
Turn unstructured 10-Ks, Annual Reports, and Private Company PDFs into structured financial models. Our AI engine extracts 30+ KPIs with >95% accuracy in minutes.
The PDF Wall
Investment analysts spend 30% of their day just finding data. The manual extraction process is a bottleneck for deal flow.
Time Sink
Manually spreading a single 10-K takes 30-45 minutes. This limits the number of companies an analyst can cover.
Structural Chaos
Private company reports have no standard format. Tables span multiple pages, breaking traditional OCR tools.
Data Quality
Manual entry leads to "fat-finger" errors. Missing footnotes or adjustments can skew valuation models.
The Extraction Pipeline
We combine Computer Vision with LLMs to understand the document context before extracting data.
Ingest & Classify
Auto-detects document type (Public 10-K vs. Private CIM) to select the correct parsing model.
Locate Section
Context-aware narrowing finds "Financial Highlights" or "ESG" sections to reduce noise.
LLM Extract
Fine-tuned models pull 30+ KPIs (EBITDA, CAPEX) including footnote adjustments.
Structure
Data is normalized into JSON/CSV and pushed to your Data Lake or Excel plugin.
Context-Aware Intelligence
Standard OCR fails when tables are complex or implicit. Our engine uses a Semantic Understanding Layer that reads like an analyst.
- Table Re-construction: Stitches tables that span across pages automatically.
- Implicit Data: Calculates derived metrics (e.g., Gross Margin %) if not explicitly stated.
- ESG Modules: Specifically trained to hunt for non-financial disclosures like Employee Count or Carbon footprint.
ROI at Scale
Designed for high-volume research teams processing thousands of reports.