data-research

Automate structured data extraction by defining YAML-based recipes that configure search queries, extraction schemas, and deduplication rules. This pipeline executes a 7-phase process to search, extract, archive, and deduplicate data from fragmented sources into canonical tracker pages.

Recipe-driven data extraction uses YAML configuration files to define search queries and extraction schemas for diverse workflows. This mechanism processes fragmented sources like emails and APIs, applying deduplication rules to reliably transform unstructured inputs into structured canonical tracker pages.

You can track investor update emails by initializing a recipe to monitor your inbox, extract key financial metrics like ARR and MRR, and update a canonical tracker page with backlinked sources. This supports diverse research workflows including company metric monitoring.

Set up a research pipeline by configuring a YAML recipe that defines your expense tracking or company metrics extraction schema. The pipeline then executes its 7-phase process to search sources, extract structured data, archive records, and deduplicate entries into a canonical tracker.

The pipeline works with APIs, web pages, and emails without manual entry by using recipe-driven configuration to automate the search and extraction process. It requires YAML recipes to define the specific extraction schemas and deduplication rules needed for reliable data management.

The best way to prevent data loss from fragmented sources is to use an automated research pipeline that archives and deduplicates extracted data into canonical tracker pages. This ensures consistent record-keeping and prevents the manual tracking errors that cause data loss.