Context

One of our hedge fund clients needed time-series data on production volumes from reports like those published by the National Cotton Council. The problem? This data was buried in PDFs and other messy formats that changed constantly.

Issues

Manual data collection presented significant challenges:

• Messy formats: PDFs, scanned documents, and unstructured web pages with no standardization
• Constant monitoring: Reports were published on irregular schedules (weekly, monthly, or ad-hoc)
• Years of history: They needed to backfill years of historical data
• Zero room for error: One bad data point could compromise their trading models
• Scaling issues: Expanding to multiple commodities across different countries would not scale with manual processes

sieve solution

We built them a system that:

• Handles any format: Our AI extracts data from PDFs, websites, and any source format without requiring custom workflows
• Backfilled everything: We processed years of historical reports to build their complete dataset
• Monitors automatically: When new reports are published, we extract the data immediately
• Human-verified accuracy: Every data point is verified by domain experts who understand financial data
• Scales easily: Adding new commodities or countries takes minutes, not months

Now they get clean, structured data automatically instead of spending hours copying numbers from PDFs. They can focus on actually using the data instead of collecting it.