© 2026 AI-Buzz. Early access — data updated daily.
© 2026 AI-Buzz. Early access — data updated daily.
ETL for unstructured data preprocessing
Founded by: Brian Raymond, Matthew Harrison
Metrics computed from HN discussion, GitHub activity, and funding data.
According to AI-Buzz, Unstructured with 34% positive developer sentiment (71 HN comments analyzed), with 4,626,134 PyPI downloads in 30 days.
Source: https://ai-buzz.com/companies/unstructured?utm_source=citation&utm_medium=referral&utm_campaign=cite_this_data
Metrics derived from public APIs (HN Algolia, GitHub, npm/PyPI). Sentiment classified by AI. See methodology for details →
Description
ETL for unstructured data preprocessing
Estimated Company Size
100 - 250 employees
Website
unstructured.ioFounded
2022
Description
Unstructured specializes in extracting and transforming complex unstructured data from diverse sources like PDFs, images, and emails into clean, structured formats. Their tools are critical for preparing high-quality input data for large language models (LLMs) and other AI applications, enabling organizations to build more accurate and effective AI solutions.
Community engagement metrics that indicate developer traction and interest.
Last updated: 1 day ago
Mentions in HN discussions. Source: Hacker News Algolia API.
Sentiment analysis of Hacker News comments only. Does not include Reddit, Discord, or other platforms.
Package download volume indicates real-world adoption and integration into production projects.
An open-source Python library for pre-processing unstructured data, extracting text and metadata from various document types for use with large language models.
A managed service that provides scalable and reliable data preprocessing, offering advanced features and integrations beyond the open-source library for enterprise use cases.
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Package downloads and ecosystem metrics — 30-day window
Insufficient data for trend chart
Need at least 2 data points (currently have 1)
Insufficient data for trend chart
Need at least 2 data points (currently have 1)
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