data-research

Extracting structured data from emails without duplicates requires a pipeline that parses fields, applies exact and fuzzy deduplication logic, and updates a canonical tracker while archiving raw inputs with backlinks. This eliminates manual matching errors.

Deterministic-first extraction is a method that applies strict parsing rules to emails and documents before using an LLM as a fallback. You need it for recurring data research pipelines to prevent batch hallucination errors and ensure structured field accuracy.

Automating an investor update tracker involves running a recipe-driven research job that searches sources, extracts schema fields from emails or attachments, deduplicates entries, and updates the tracker page while archiving raw emails with backlinks to the canonical record.

Yes, you can build a recurring tracking pipeline for expenses and donations by configuring recipe-defined queries and extraction schemas. The pipeline processes emails, web sources, and APIs together, deduplicates entries, and updates the canonical tracker automatically.

Yes, structured data research supports knowledge graph backlinking by archiving raw sources immediately and linking extracted entities like people and companies back to their respective knowledge pages within the updated canonical tracker.

LLM fallback limitations in email-to-data extraction include potential batch hallucination errors, which require integrity safeguards like re-reading saved raw files when summarizing to ensure extraction accuracy and prevent double-counting in the canonical tracker.