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 In today’s data-driven enterprise, unstructured data —PDFs, scanned contracts, handwritten forms, or emails—makes up over 80% of all business information . And yet, much of it remains locked in unusable formats, buried across content silos. To unlock this hidden value, CIOs and compliance leaders are turning to Intelligent Data Extraction —a core capability of Document AI . What Is Intelligent Data Extraction? Intelligent data extraction uses artificial intelligence (AI), natural language processing (NLP), and computer vision to identify and extract relevant information from unstructured content —such as scanned PDFs, images, handwritten notes, or lengthy text files. It goes beyond OCR by understanding semantic context , identifying entities (like invoice numbers, contract dates, or payment terms), and converting them into structured data. Why It Matters for Enterprises Here’s why enterprises are prioritizing intelligent extraction as a critical component of their unstructu...

  The Content Management Crossroads There was a time when managing documents meant scanning files, naming them carefully, and praying you'd find them again. Enter Enterprise Content Management (ECM) —the hero of early digital transformation. It centralized documentation, enabled search, and supported basic compliance. But fast forward to today: we live in a world of unstructured content chaos. Emails, Zoom transcripts, PDFs, design files, chats—data is exploding across formats and systems. Traditional ECM platforms simply can’t keep up. That’s why businesses are shifting to Enterprise Content Services (ECS) —cloud-native, AI-ready, and governance-centric platforms designed for today’s fast-paced, regulated environments. What Is Enterprise Content Services (ECS)? Enterprise Content Services is not just a software upgrade. It’s a strategic shift in how content is stored, accessed, retained, and governed. Unlike legacy ECMs that act as static repositories, ECS platforms: Sup...

 In late 2023, the CIO of a Fortune 500 financial services firm found herself at a crossroads. The company had just been flagged in a routine GDPR audit. Sensitive customer data was found unclassified, scattered across data lakes, on-prem mainframes, and SaaS apps — all without consistent governance policies. The compliance team was overwhelmed. The risk team was on high alert. And the board demanded answers. “We had invested in cloud migration, AI, and data warehousing — but our data governance framework was duct-taped together,” she later admitted. “We didn’t just need to meet data governance and compliance objectives — we needed a transformation.” A Common Crisis Across Enterprises This CIO’s story is not unique. Around the globe, large enterprises are struggling to keep up with complex data regulations like GDPR , HIPAA , SOX , and CCPA . The explosion of data sources — combined with fragmented tools and siloed teams — makes consistent data compliance and security feel n...

 In the world of finance and brokerage, compliance is not a feature — it’s a requirement. One of the most important regulations you need to be aware of is FINRA Rule 17a-4 , especially if you’re dealing with legacy systems and scattered enterprise data. But what if your data is stored in Oracle databases, shared drives, or email servers from 2005? Is it even possible to meet modern compliance standards without ripping everything out? The answer: Yes. You just need the right data archiving strategy. Let’s break it down. What Is FINRA 17a-4 (And Why It Matters) FINRA Rule 17a-4 requires broker-dealers and financial firms to: Preserve electronic records in WORM (Write Once, Read Many) format Maintain those records for a fixed retention period (6+ years) Ensure records are searchable and auditable Keep records in a non-rewritable, non-erasable format If you don’t meet these requirements, you're not just at risk of fines and audit failures — you're risking ...

 In a world where data is more valuable than oil, enterprises are discovering a harsh truth: most of their data is chaotic, siloed, and underutilized. Years of mergers, legacy systems, departmental data hoarding, and compliance mandates have left organizations with fractured architectures. These silos not only increase costs and compliance risk—they block innovation. Generative AI , analytics, data science—all of it depends on unified, trusted, and governed data. This is exactly the problem the Solix Common Data Platform (CDP)  was built to solve. From Fragmented Systems to Unified Intelligence Enterprise data exists across multiple environments—cloud, on-premises, legacy databases, ERP systems, and modern SaaS tools. Without a common data layer, organizations struggle with: Duplicate and inconsistent records Difficulty enforcing retention and privacy policies Poor data quality and trust Slow AI and analytics adoption High costs for data storage and governa...

 In 2024, most CIOs have moved past the “should we use AI?” debate. Generative AI is now a boardroom mandate . But as enterprise leaders rush to deploy large language models (LLMs) and AI copilots, one question remains unanswered: How do we safely operationalize generative AI in enterprise data platforms — without losing control over our data, governance, and compliance? That’s where Solix Enterprise AI for Generative AI is making waves. From Experimental LLMs to Enterprise-Ready AI Generative AI has captured the imagination of the business world, powering everything from marketing content to customer service agents. But the leap from a prototype chatbot to a compliant, governed AI workflow inside an enterprise is massive. And here’s why: Most LLMs are trained on public data — not your business logic Prompt responses can’t be traced or audited Sensitive data may be exposed in third-party APIs Outputs lack context unless grounded in enterprise data Solix solv...