data-research

To extract structured data from emails and update a tracker, use parameterized YAML recipes to define search queries, classification rules, and extraction schemas. The pipeline searches sources, extracts fields, archives raw inputs, deduplicates results, and backlinks entries to a canonical tracker page.

Deduplication during company metric consolidation applies predefined rules within YAML recipes to identify and merge overlapping records from web, API, or attachment sources. This ensures only canonical, unique entries populate the final structured tracker without redundant data points.

To prevent hallucination during data extraction from document attachments, the system saves raw source files first and re-reads those archived files during the summarization phase. This deterministic-first parsing approach with LLM fallback ensures strict extraction integrity.

Yes, you can automate investor update tracking by configuring built-in YAML recipes. These recipes drive the structured research pipeline to search inputs, classify items, extract relevant metrics, and push deduplicated updates directly to your canonical tracker pages.

To archive raw inputs and backlink them, the research pipeline saves source files locally before extraction. Once structured data is extracted and deduplicated, every resulting tracker entry is automatically backlinked to its original archived raw source file.

Yes, this data extraction approach works with API responses, web pages, emails, and document attachments. The pipeline applies deterministic-first parsing with an LLM fallback to classify items and extract structured fields across all these input types.