How semantic entity resolution boosts data accuracy and efficiency

What if your unstructured docs could be converted to structured data automatically? At Unstructured, our mission has always been high-fidelity document transformation. Now we’re getting ready to take the next big step: structured extraction. If you go high level, there are only a few broad use cases with your unstructured data. Parsing makes docs readable. RAG makes them searchable. Structured extraction makes them actionable. That’s the promise of our new Structured Data Extractor. Organizations don’t just want documents parsed—they need specific fields, consistently and reliably, mapped to their own data models. Until now, that’s been brittle, costly, and hard to scale. Here’s what’s coming: - Define your own structured data model (JSON schema, database fields, etc.) - Automatically extract matching fields from unstructured documents - Outputs delivered with consistency and reliability And the impact goes far beyond clean outputs: - Power process automation by feeding extracted data straight into workflows or databases - Enable agentic flows where AI agents can act directly on structured fields - Unlock faster, more reliable pipelines for compliance, analytics, and decision-making No more brittle regexes or one-off scripts. Soon, your documents will yield clean, structured data aligned to your business needs—and ready to automate. #DocumentAI #DataExtraction #StructuredData #EnterpriseAI #Automation #AgenticAI #StructuredDataExtraction #UnstructuredData #WorkflowAutomation #IDP #Agents

Supporting Our AI Overlords: Redesigning Data Systems to be Agent-First Large Language Model (LLM) agents, acting on their users' behalf to manipulate and analyze data, are likely to become the dominant workload for data systems in the future…We argue that data systems need to adapt to more natively support agentic workloads. We take advantage of the characteristics of agentic speculation that we identify, i.e., scale, heterogeneity, redundancy, and steerability - to outline a number of new research opportunities for a new agent-first data systems architecture, ranging from new query interfaces, to new query processing techniques, to new agentic memory stores… https://lnkd.in/dwfME4yq

𝗧𝗵𝗲 𝗚𝗿𝗲𝗮𝘁 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻: 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 = 𝗡𝗲𝘄 𝗩𝗲𝗻𝗱𝗼𝗿 𝗟𝗼𝗰𝗸-𝗜𝗻   CIOs celebrate platform consolidation solving "tool fatigue."   Reality: You're trading data silos for mega-silos controlled by Big Tech.   The Vendor Lock-In Playbook: 1. Create incompatible metadata standards 2. Build platform-specific dependencies 3. Make migration costs prohibitive  4. Trap enterprises in closed ecosystems   Timeline Reality Check: ↳ Year 1: Reduced complexity, faster integration  ↳ Year 3: Price increases, limited flexibility ↳ Year 5: Migration costs = $2M+ and 18 months   IT Leader Horror Stories: → "Switching platforms would bankrupt our integration budget" → "Their proprietary format locks us in completely"  → "We solved tool fatigue but created technical paralysis"   Smart InteGraphics Freedom Strategy: ☑ Open integration standards ☑ Vendor-agnostic architecture ☑ Multi-platform iMirAI intelligence ☑ Future-proof data migration  ☑ Unlimited dataset compatibility   Bottom Line: True integration enhances flexibility, never restricts it.   Ready to escape vendor lock-in?   Experience Integral Analytical Solutions with natural language processing that works across any platform.   Discover integration freedom: https://lnkd.in/emtnntba   → Swipe to uncover the vendor lock-in truth Share if you support open data standards #𝗗𝗮𝘁𝗮𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻 #𝗗𝗶𝗴𝗶𝘁𝗮𝗹𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 #𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻

The new game is unstructured data > structured data > pre-built analysis > action. The unstructured data portion before was very difficult and not too cost effective to parse and structure for effective use at scale. You typically had to be a large enterprise to even get useable structured data from the unstructured data. Even then, no guarantee you'd get positive ROI. Now the value line has shifted to the speed of action because the unstructured data can be parsed well enough with very little effort (AI with guardrails). This is fundamentally changing our business (since we've historically put more resources for clients toward the data side of things). Now the biggest levers are less data engineering and more process/strategy driven.

Financial firms are tackling an explosion of complex data - from structured trading records to unstructured research reports. Scaling data lakes that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 is now the critical next step. Is your organization facing these challenges? ⏳ Legacy systems slowing down under data growth 🐢 Lengthy analytics delaying insights 🔒 Managing compliance risks across complex data environments 🤯 Difficulty delivering trusted, unified data access at scale Discover how financial institutions can overcome these barriers with 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱, 𝗮𝗻𝗱 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀. Our latest guide, “𝗧𝗵𝗲 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀 𝗶𝗻 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗙𝗶𝗿𝗺𝘀,” offers: ✅ Real-world lessons and case examples ✅ A clear framework covering ingestion, storage, analytics, governance, and consumption ✅ Key trade-offs and how to navigate them ✅ A practical, step-by-step roadmap to build and mature your data lake ✅ A maturity model to benchmark your AI readiness progress 📥 Download the attached PDF to unlock actionable insights and accelerate your AI transformation journey. 💬 What’s the biggest challenge your data teams face at scale? Or which breakthrough moved your analytics forward? Let’s discuss in the comments! #DataLakes #AIinFinance #Fintech #CloudData #DataGovernance #MachineLearning #FinancialServices #BigData

Auto-generate extraction schemas without manual setup using LlamaExtract 🔄 Automatically create schemas for data extraction, making it easier to pull structured information from unstructured documents without needing to define extraction patterns upfront. Simply start off with a prompt and/or example files and: 🤖 Automatically infer the structure of your data for extraction 📊 Skip manual schema definition and let AI handle the heavy lifting 🎯 Focus on using extracted data rather than configuring extraction rules Check out the auto schema generation guide: https://lnkd.in/eFc9wZtq

Unstructured data often contains the most distinct and proprietary content that remains untapped within enterprises. When operationalized through Agentic AI workflows and Large Language Models (LLMs), this data becomes actionable, driving tangible business value. Yet, the challenge lies in managing the inherent complexity of unstructured data at scale. Piethein Strengholt highlights how the Medallion Architecture, traditionally focused on structured data, can be repurposed into a unified, layered framework designed to handle unstructured data effectively. He discusses how: ✅ Bronze, Silver, and Gold layers can be extended to ingest, validate, and contextualize unstructured data. ✅ LLMs and RAG patterns can transform raw documents into reliable, AI-ready inputs. ✅ Governance and new roles (context engineers, value engineers) will be essential to translate unstructured data into business value. Click on the link below to learn more https://lnkd.in/gwxcjgKR #UnstructuredData #MedallionArchitecture #DataQuality #AIready #Datatrust #DataArchitecture

I came across this article from Piethein Strengholt a few weeks ago, and have since brought this article up in multiple conversations. It’s such a simple yet powerful way to visualize the deep architectural underpinnings required for Agentic AI adoption. Often, discussions on this topic get lost in abstractions — but this framing makes it accessible while still capturing the complexity behind it. Highly recommend giving it a read if you’re exploring how to design data and system foundations for the Agentic AI era. #dataarchitecture #openarchitecture #lakehouse #dataobservability #dataquality #agenticworkflow

🔎 Let’s clear up the confusion around warehouses, lakes, and lakehouses. A real data warehouse isn’t just a fast database. It’s defined by: ✔️ Subject orientation (built around business concepts) ✔️ Integration (consistent keys and definitions) ✔️ Historic data persistence (true history, not overwrite) That’s the foundation for enterprise data integrity. Without it, AI and analytics run on shifting sand. Over time, the lines blurred. Analytical databases were called “warehouses.” Then came data lakes. Then lakehouses. All powerful technologies — but let’s not mistake them for the discipline of a true warehouse. 👉 A lakehouse on its own is not subject-oriented, integrated, historized persistence. The missing link? Data Vault modeling. By making your integration layer subject-oriented, deduped, and historized, you give the lakehouse the persistence and trustworthiness of a true warehouse. With this, AI and analytics can finally rely on it without compromise. 💡 The takeaway: Any modern platform can become reliable — but only when paired with a modeling approach like Data Vault. That’s when you unlock a real foundation for analytics and AI. At Sudar.io, we make adding Data Vault effortless.

Imagine being able to rewind your database history, examining any change made at any moment. For teams handling sensitive data or seeking robust audit trails, this level of transparency is often out of reach with traditional databases. DriftDB steps into this space as an experimental, append-only database built in Rust. By recording every action as an immutable event, it preserves a full chronicle of your data’s evolution. Time-travel queries allow users to view the state of their data at any historical point, which can be invaluable for compliance, debugging, or tracking business metrics week by week. Features like CRC-verified segments, secondary indexes for quick lookups, and atomic operations make DriftDB not just a novel idea but a practical solution for ensuring data reliability. Its snapshot and compaction tools help sustain performance, while the simple DriftQL language keeps interaction accessible. Whether you work in finance, healthcare, or any environment where data lineage is crucial, DriftDB offers a fresh way to approach data auditability and recovery. Interested in seeing how append-only, time-travel databases could impact your process? Take a look at the repository and explore more: https://lnkd.in/daYvg-z8 #ai #andai #&ai