Unstructured

A few years back you could blame slow AI adoption on weak models. That excuse doesn’t work anymore. Models can reason. They can plan. They can use tools. What they can’t do is operate without high-quality context. In 2026, the real bottleneck is access to usable, trustworthy data, especially the 80–90% of enterprise knowledge trapped in PDFs, emails, scans, and decks. When AI initiatives stall today, the root cause nearly every time is that the data foundation for autonomous systems was never built. Today, unlocking unstructured data is no longer a “nice to have” but a prerequisite for building reliable, agent-ready data architectures. In our latest white paper, we explore: * Why 90% of enterprise knowledge is effectively invisible to AI agents * The technology stack required to convert messy documents into AI-ready context * How organizations can govern, structure, and serve unstructured data to power autonomous workflows For teams thinking about AI beyond chat, understanding the data layer has never been more critical. 🔗 https://lnkd.in/e73RfGqq

Operational GenAI fails without the right data foundation. The United States Air Force selected Unstructured to deliver it. This award establishes a vendor-agnostic AI data layer that enables GenAI without runaway costs or vendor lock-in. In partnership with DTO: DAF Digital Transformation Office, the work focuses on quickly and cost-effectively transforming complex, multimodal data for AI in environments where compute, bandwidth, and time are limited. The result is operational GenAI that scales across mission environments while preserving data ownership, performance, and cost discipline. "The greatest challenge to operationalizing GenAI across the United States Department of War isn't the model—it's the data. Our work with DAF DTO will establish the foundational data layer that allows the Air Force and the entire Joint Force to deploy GenAI at speed, maintain absolute control over their sensitive data, and intelligently manage costs by using the right model for the right task." Read more about the Air Force award here: https://lnkd.in/eeYvYKPf #Data #AIReady #GenAI #DoW #AIStrategy #AgentNetwork #DAFDTO #DAF

How do you actually *measure* GenAI data quality? In our upcoming webinar, we’ll walk through SCORE, an open framework for benchmarking data pipelines with real-world rigor. Register today! 🔗 https://lnkd.in/ewN39FtM

Document processing has quietly become one of the most overbuilt layers in enterprise AI. What starts as a simple POC quickly turns into production infrastructure with orchestration, monitoring, and ongoing maintenance.

🚨 70% of GenAI projects never make it to production. And the biggest reason? Fragile data pipelines. Too many vendors rely on cherry‑picked benchmarks that fall apart when exposed to real‑world data. Time for a better standard. 🎉 Introducing SCORE — an open‑source framework for evaluating GenAI data pipelines with transparency, consistency, and real‑world rigor. Join us for a live session on SCORE and why it matters: 🗓️ Next Wed, Jan 21 🕙 10 AM PT / 1 PM ET 🎙️ Kevin Krom & Lavanya Chockalingam 👉 Register: https://lnkd.in/ewN39FtM #GenAI #DataPipelines #AI #TechTrends #OpenSource #MLOps

Basic RAG was cute in 2023. In 2026, it breaks the moment retrieval gets messy. As enterprises move beyond chatbots and toward agentic systems that plan, reason, and take action, retrieval alone isn’t enough. Agents need data they can trust - data that’s structured, governed, fresh, and usable before and after retrieval. In our recent webinar, Unlock Your Data with Agentic RAG, we broke down what actually changes when RAG becomes agentic & what teams need to get right to make it work in production. Check out our 5 key takeaways from the session: 1️⃣ Our expectations of AI systems have skyrocketed. RAG that felt impressive in 2023 now feels… limited when the goal is reliable outcomes. 2️⃣ Naive RAG only has one shot to get it right. If retrieval misses, the system usually can’t recover. That’s why brittle pipelines show up fast in production. 3️⃣ Agentic RAG wraps retrieval in a cognitive loop. Planning, tool selection, self-correction: retrieval becomes one step in a larger “think + act” flow. 4️⃣ Agentic RAG isn’t one-size-fits-all. There’s a spectrum from simple routing to multi-agent systems. The right setup depends on the use case (and the tolerance for complexity). 5️⃣ Agentic systems tend to evolve over time. Many start with pure RAG, then grow into API calls, SQL queries, and web search as teams chase better reliability and coverage. If you’re thinking about moving from “RAG-powered answers” to AI systems that can actually act, this session lays out the foundation. Watch the full recording here → https://lnkd.in/euTdgCfT #AgenticRAG #RAG #GenAI #EnterpriseAI #Unstructured #TheGenAIDataCompany

Enterprises invested $18B in AI infrastructure across foundation models, training systems, and the data and orchestration layers. This market map shows the companies serving each part of that stack. We are three years into this wholesale transformation. It’s still early, but the first waves of leaders are emerging, and the value is clear. https://lnkd.in/gY4nirKH

a16z backed Airbnb, Coinbase, Stripe and other 100+ unicorns. Now they published 30 startup ideas for 2026. Key shifts you need to know: 1) Build the engine, not the car → infrastructure wins bigger than apps. 2) Design for agents, not humans → AI agents are becoming the primary users. 3) If your AI only reads text, you're already behind → multimodal is now baseline. 30 startups already building this future: 🔹 Multimodal data: Unstructured, Databricks, Glean, LangChain 🔹 Agent orchestration: Relevance AI, Adept, Apollo GraphQL, CrewAI 🔹 AI security: HiddenLayer, Vectra AI, Avathon 🔹 Industrial AI: Plus One Robotics, Bright Machines, Tulip Interfaces, Arzeda 🔹 Content creation: Runway, Pika, ElevenLabs, Synthesis AI 🔹 Vertical agents: Eve, Harvey, Voiceflow 🔹 Health & education: Levels, ŌURA, Minerva University, Synthesis, Primer 🔹 Fintech infra: Modern Treasury, Unit 🔹 Analytics: Amplitude Why these startups? ✓ They build infrastructure, not features ✓ They replace labor with 5-10× leverage ✓ They solve second-order AI problems (security, orchestration, observability) Full a16z list covers 30 startup ideas across infra, enterprise, fintech, health, and more: 👉 Part 1: https://lnkd.in/dpCCxpih 👉 Part 2: https://lnkd.in/dvcXKndG The future is bright for founders building for an AI-first world! Your thoughts ? -- Need help with design, dev or marketing? Subscribe to a creative team by AI-powered algorithm → https://www.awesomic.com/

Not sure which parsing strategy your documents need? Don't guess, let `auto` decide at runtime. The `auto` partitioning strategy analyzes each page and routes intelligently: simple text pages get lightweight extraction, complex layouts with tables and images get object detection + VLMs. Learn more: https://lnkd.in/efNeCZGa