Forms are one of the harder things to parse correctly. Most tools see a table with checkboxes and field labels and flatten it into a wall of text, losing all the structure that makes it useful. Unstructured's VLM partitioner outputs text_as_html metadata that preserves it automatically. Tables stay as <table> elements. Form fields keep their relationships. Multilingual and RTL formatting works out of the box. The rendered HTML on the right is what your downstream models actually receive. Try it yourself: create a workflow, add a partitioner node with VLM strategy, and check the text_as_html metadata in your output.
Unstructured
Data Infrastructure and Analytics
San Francisco, CA 28,915 followers
Stop dilly-dallying. Get your data.
About us
Unstructured is the data infrastructure company solving the most critical bottleneck in enterprise AI: making unstructured data accessible to AI applications. Trusted by 87% of the Fortune 1000, we transform the 80–90% of enterprise information trapped in inaccessible formats—PDFs, Word docs, PowerPoints, emails, HTML, and 70+ other file types—into clean, AI-ready data with industry-leading accuracy and performance benchmarks. Companies that try to build and maintain custom data pipelines in-house find it's a significant and ongoing engineering drain. Unstructured replaces that entirely, enabling enterprises to move from experimental workflows to AI applications that execute real business value. Recognized by Forbes AI50, Fast Company's Most Innovative Companies, and CB Insights AI 100, Unstructured is the data foundation that makes enterprise AI work.
- Website
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http://www.unstructured.io/
External link for Unstructured
- Industry
- Data Infrastructure and Analytics
- Company size
- 51-200 employees
- Headquarters
- San Francisco, CA
- Type
- Privately Held
- Founded
- 2022
- Specialties
- nlp, natural language processer, data, unstructured, LLM, Large Language Model, AI, RAG, Machine Learning, Open Source, API, Preprocessing Pipeline, Machine Learning Pipeline, Data Pipeline, artificial intelligence, and database
Locations
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Primary
Get directions
San Francisco, CA, US
Employees at Unstructured
Updates
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Across most large enterprises, there isn't one AI initiative. There are dozens. Different teams, different frameworks, different RAG pipelines, different document parsers. Each one built in isolation. Each one creating its own compliance exposure, its own cost center, its own set of assumptions about what good data looks like. This is AI sprawl. And most CIOs are already feeling it. The fix isn't better prompts or a different model. It starts at the data layer — how you ingest documents, prepare them, and orchestrate that process at scale across teams. That's what we're digging into with IBM on April 21st. Join us Tuesday, April 21 at 10am PT / 1pm ET for a live webinar on standardizing your AI data infrastructure. 🎙️ Speakers: - Austin Eovito, Senior AI Engineer, IBM Client Engineering - David Donahue, Head of Strategy, Unstructured We'll cover: - Why fragmented data pipelines are the most expensive AI problem most enterprises are ignoring - How Unstructured + IBM watsonxdata + watsonx Orchestrate centralizes AI foundations across teams - Reducing TCO and scaling RAG and agentic use cases without rebuilding from scratch 🔗 Register: https://lnkd.in/esfP-gbv #AI #GenAI #EnterpriseAI #RAG #DataEngineering #UnstructuredData #Unstructured #IBM #IBMwatsonx
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New! A hands-on guide to building AI chat apps with IBM watsonx Orchestrate 🚀 Learn how to connect Unstructured's data ingestion pipeline to vector databases (Astra DB or Milvus) and deploy intelligent agents that can answer questions about your organization's documents: * Ingest & process documents with Unstructured * Generate embeddings & store in Astra DB or Milvus * Build a chat app in watsonx Orchestrate that queries your vector DB * Deploy an AI agent that answers questions based on YOUR data Link to the full walkthrough: https://lnkd.in/e-py93Fm #IBM #watsonx #AI #GenerativeAI #EnterpriseAI #RAG #DataEngineering
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✈️ Headed to the Wright-Patterson Air Force Base Expo next week? Stop by our booth to learn how Unstructured transforms complex, multimodal data into clean, structured, AI-ready outputs. 📅 When: Tuesday, April 14 📍 Where: Wright-Patterson AFB, Ohio 🔗 Book time with our team: https://lnkd.in/gSr222cs #AI #GenAI #GovTech #DefenseTech #DataEngineering #UnstructuredData #RAG #DocumentAI #Unstructured #TheGenAIDataCompany
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Most enterprise GenAI projects don't fail because of the model. They fail because the data was never ready to begin with. We partnered with IBM to break down what a production-grade RAG pipeline actually looks like — from raw, messy documents sitting across dozens of systems, all the way to agent-ready data that AI can reliably act on. 5 key takeaways from the session 👇 #EnterpriseAI #RAG #DataPipeline #GenAI #IBMwatsonx #Unstructured
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Unstructured reposted this
Cassie Pless, Christopher Maddock and I spent yesterday with CxO leaders talking AI. A few things came up over and over: Most companies are dealing with 200+ systems that don’t like to talk to each other. Their data remains incredibly fragmented, both in terms of file formats and permissions. Taken together, they're struggling to turn their raw data into context for their agentic systems. That’s the challenge. That’s what we’re solving at Unstructured. Ping us if you want help figuring out your AI data strategy. We’d be happy to come meet you in person and trade notes.
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Are you using Unstructured in a Pay-As-You-Go capacity and looking for additional security and control? Unstructured's dedicated instances are a great option! Dedicated instances are hosted within a virtual private cloud (VPC) running inside Unstructured’s cloud infrastructure. Dedicated instances are isolated from all other Unstructured accounts. You get additional benefits such as enabling multiple users and workspaces, role-based access control, and much more! Learn more here: https://lnkd.in/e68bHfNH
Unstructured Dedicated Instances Overview
https://www.youtube.com/
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It's almost time! Join us today at 1 PM ET / 10 AM PT. Christopher Maddock, Head of Product & Engineering at Unstructured, will be speaking. DZone is bringing together industry experts (including Unstructured) to unpack how teams are moving from GenAI experimentation to production-grade systems. This isn’t theory - it’s about execution: - What’s working (and what’s not) - How teams are scaling LLMs - Where governance and cost control fit in 📅 When: TODAY 4/8 @ 1p ET 🔗 Register: https://lnkd.in/eSf2DyX5 #ArtificialIntelligence #EnterpriseAI #AITransformation
What does it actually take to operationalize Generative AI? Join us next Wed, April 8 for DZone's 2026 Generative AI Virtual Roundtable where experts break down how organizations are scaling AI from experimentation to production. Christopher Maddock, Head of Product & Engineering, will be joining the panel to share insights on building scalable, production-ready AI systems. You’ll walk away with: - How to structure AI programs for scale - What it takes to operationalize LLMs - How to manage governance, compliance, and cost Built for engineering leaders and AI practitioners driving real-world adoption. 👉 Register now to join the conversation: https://lnkd.in/e42b6Nxk #GenAI #AIEngineering #LLMOps #TechLeadership #DataInfrastructure
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Unstructured reposted this
Hiring alert: Unstructured is looking for a GTM Engineer to focus on Sales Operations. I'm only a few weeks in, but I already know the RevOps team here is exceptional. Genuinely cutting-edge GTM engineering, and some of the best collaborators I've worked with. If that sounds like your kind of environment, check it out! If we've worked together before, reach out directly and I'll make a warm intro.
While I hunt for our newest team member, I want to post a few examples of solutions we've deployed thus far. Here's the first: The Problem: Head of Sales, Cassie Pless, flagged two pain points: 1. It's hard to track AE discovery meetings across the company in HubSpot. 2. AEs spend 30 minutes before each disco meeting digging through disparate databases and the web to prep for the call. Enter: DISCOBot. Hosted on Railway w/ Supabase in the backend. Deployed on the Unstructured domain post security-reviews. Protected via Google Cloud OAuth. n8n workflow orchestrating all the HubSpot data ingestion and data merging. (Up-front caveat: It does not replace the CRM. It does not replace proper cross-channel lead conversion tracking across the marketing funnel. It DOES solve specific problems for our leadership and sellers.) Primary features: - provides a comprehensive view of all disco mtgs in the company, ingested directly from Google Calendar API, with meeting analytics and attribution funnels anchored on the mtgs. - Makes it easy to see if each rep met their weekly Initial Meeting goal. Completely replaces slides for that portion of our Weekly Pipe Gen Review. - 24 hours before each mtg, it triggers Claude Opus 4.6 to prep a brief with data ingested from: a. HubSpot (company data, attendee info/LinkedIn accts, deal history, engagement timeline) b. Gong (past call transcripts) c. 1st-party intent signals (open-source usage, docs activity, product usage) d. Web research (company initiatives, funding rounds, etc.). Bonus features: - A built-in LLM context layer that contains our ICPs, personas, use cases, product info, and automatically-ingested Closed Won deals to ID lookalike opportunities. - Pipeline calculator that uses existing conversion rates to forecast C/W $ revenue based on weekly initial meetings. - Attribution funnel auto-labeled and ingested from the CRM. - Chat bot running Perplexity Sonar Pro for live web lookups while chatting with disco mtg briefs. - Groovy retro theme 🕺. We're hiring someone who problem-solves like this. Message me if you've ever built something similar! Link to JD in the comments. Note: video uses dummy data for demo purposes
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Turning raw documents into AI-ready data just got a whole lot easier. 😎 Unstructured's new on-demand jobs feature allows you to quickly and easily have Unstructured transform your raw, messy documents into structured, AI-ready data. With just a few simple Python commands, you can call on a full range of Unstructured's features for rapid GenAI data prototyping. And with just a few more Python commands, you can move your validated prototypes into production at scale. Follow along with our notebook in Google Colab: https://lnkd.in/eQVTJZVf