text2structured-summary generates structured summaries from unstructured text using an LLM.

A new package is designed to analyze and summarize business strategy narratives, investor communications, or crowdfunding campaign descriptions to extract structured insights about startup funding act

This package solves the problem of extracting structured, domain-specific insights from unstructured text inputs—like historical articles, research papers, or summaries—without requiring manual parsin

vitae-parser extracts and structures biographical details from unstructured text using pattern matching.

📝 Extract and structure biographical information from unstructured text with this easy-to-use Python package for researchers and historians.

📝 Generate structured summaries from unstructured text effortlessly with an LLM for enhanced clarity and organization.

🔍 Extract insights from startup funding narratives with Stratix-Summarizer, simplifying analysis for investors, entrepreneurs, and analysts alike.