data-research

Automated structured data extraction is executed through customizable YAML recipes that search emails, web sources, APIs, and attachments to collect specific data points. This streamlines capturing information from multiple sources into a unified dataset.

Data deduplication is best handled by running an automated research pipeline that removes duplicate entries during the archiving process. This ensures your canonical tracker pages maintain accurate and clean data records without manual review.

Yes, custom YAML recipes support specific structured data collection tasks. You can define recipes to track investor updates, donations, and company metrics, tailoring the search, extraction, and tracker update processes to your exact requirements.

Canonical tracker pages are updated automatically by the research pipeline after searching and deduplicating new entries. The process backlinks the tracker pages with the newly archived data to ensure your records stay current.

Yes, structured data research pipelines require access to local data sources and structured data formats to execute the search, extraction, and archiving steps. This ensures the YAML recipes can accurately query and process your target information.

Archiving raw data sources during extraction preserves the original information for future reference and ensures data integrity. This allows you to verify the extracted structured data against the source material later if discrepancies arise.