How Flexera uses AI and graph to unify IT data

Staying ahead in knowledge management can be demanding, especially when your data comes in many forms—text, images, and graphs. The right tools can dramatically enhance how teams build, search, and reason over large, multimodal information repositories. ApeRAG tackles these challenges head-on. By combining advanced GraphRAG retrieval, vector and full-text search, and vision features, it transforms how organizations process and query their knowledge. Built-in AI agents and Model Context Protocol (MCP) support allow users to interact naturally and intelligently with their data, while enterprise needs are met with audit logging, visualization, and robust agent management. For teams deploying at scale, ApeRAG’s Kubernetes integration—complete with Helm charts and KubeBlocks—simplifies moving from prototype to production, ensuring high availability and flexibility across infrastructure setups. Industries such as research, healthcare, and finance can particularly benefit from these capabilities, empowering more effective insights from vast or evolving information. Curious to see how ApeRAG’s blend of multi-modal retrieval and scalable deployment could support your work? Visit the project repository to dive deeper: https://lnkd.in/d8G48xFg What challenges have you faced managing complex data? Share your experiences or join the discussion. #ai #andai #&ai

🌪️ The AI paradox: more than 40% (!) of enterprise AI initiatives were reportedly discontinued in 2025, despite ever growing investments by companies ❌ Top obstacles appear to be data readiness, cost (and ROI), and security concerns ☁️ When it comes to data, it's more than 65% of AI projects that fail due to data readiness issues At Oliver Wyman, our experience with the AI transformation of our clients demonstrated the necessity to put as much effort in improving the AI-readiness of their data as in defining and deploying a roadmap of AI use cases This will allow to avoid some traps and adopt the right approach: 🪤 Beware of the POC trap! Successful proofs of concept are encouraging... but they come with a bias: they are often run on an AI-ready subset of data, giving the impression that scaling will be an easy next step. In reality, companies usually face limited data continuity across systems or business units, and variable data quality 🧭AI-readiness of data should even be one of the top criteria to prioritize use cases: on top of business impact and technology maturity, make sure to start your roadmap with use cases fueled by AI-ready (or easy-to-improve) data 🗺️ Deploy a structured and systematic approach to make your full data landscape AI-ready: initial mapping of data landscape, assessment of datasets on 3 main dimensions (accessibility, connectivity, quality), pragmatic action plans 🎆 To succeed in your AI transformation, discover our 7 key principles to successfully scale AI: https://owy.mn/4c91Rwb #AI #GenAI #transformation #digital #data

Agentic AI isn’t just another tech trend; it is redefining how data engineering scales and delivers impact. As data ecosystems become more fragmented and business needs evolve, rule-based automation falls short. What enterprises need now are intelligent, adaptive systems that go beyond static pipelines. Our latest blog explores how Agentic AI, powered by LLMs and multi-agent frameworks, is driving real transformation across the data lifecycle. From faster data ingestion to smarter governance, these AI agents collaborate with engineers, continuously learn, and respond to complexity in real time. What’s changing with Agentic AI: 🔹 Schema validation and lineage tracking that adapts 🔹 Context-aware data quality and governance 🔹 Conversational data discovery and metadata enrichment 🔹 Self-healing pipelines with built-in observability 🔹 Scalable, AI-powered Master Data Management Read the blog: https://lnkd.in/gemn_A-4 #EnterpriseAI #AgenticAI #GenAI #LLM #AIagents #DataEngineering #DigitalTransformation #Sigmoid

The Strategic Blind Spot. Why In-House AI Projects Fall Short of Impact.   As AI adoption matures, many enterprises are eager to go beyond off-the-shelf tools and build internal AI solutions. But for most businesses, especially those outside the tech world, this approach rarely delivers the intended business value.   The issue isn’t ambition - it is the execution. And more often than not, the gap lies in the data, not the AI model.   Data solutions truly transform your AI outcomes because they are only as good as the data foundation beneath them. Without unified, trusted, and timely data, even the most sophisticated models fall flat. This is where enterprise-grade data solutions become the differentiator. Predictive and agentic AI systems must be able to analyze and act on streaming or near real-time data. Traditional systems aren’t built for this. Enterprise data solutions enable the shift from passive reports to proactive intelligence.   At Infocepts, we help organizations bridge the gap between AI ambition and impact through intelligent data solutions.   Whether you're modernizing legacy environments, integrating cloud-native platforms like Databricks, or preparing for agentic AI, our data-first approach ensures your foundation is future-ready. In today’s AI-driven world, smart decisions start with smarter data. I'm happy to connect. Please feel free to reach out if you are struggling. #AITransformation #FutureOfAI #DataSolutions

💡 Many enterprises chase AI success, but fall short—not because of the models, but because of the data foundation. At Infocepts, we believe data-first is the only way to make AI truly impactful. This perspective from our CEO & Founder Shashank Garg highlights why enterprises must focus on building trusted, unified, and real-time data systems to unlock the promise of predictive and agentic AI.   Read on to learn how we’re helping organizations bridge the gap between AI ambition and AI impact. 👇   #AITransformation #FutureOfAI #DataSolutions

🚨 Everyone’s chasing GenAI, AgenticAI, and the next big AI breakthrough… But after spending 15+ years in Data & AI consulting, here’s what I’ve consistently seen: 🔍 The true value of analytics isn’t in flashy GenAI tools — it’s in good DATA. Every other company today wants to be at the forefront of AI-led transformation. GenAI POCs are popping up everywhere. But behind the scenes? Many are still struggling with foundational elements — no proper data lake, poor data quality, scattered governance, and no unified data model. 💡 You can’t run if you haven’t learned to walk. What I’ve learned is simple but powerful: ✅ One table — One grain ✅ All facts and dimensions interlinked A robust data model and strong data governance are the real engines behind any successful AI/analytics program. Without that foundation, no amount of AI layering will magically generate business value. Yet, too often, analytics teams are blamed when insights don’t deliver — when the root issue is actually the underlying data chaos. ✨ Want GenAI to work for your business? Start with your data. Clean it. Model it. Govern it. Then watch the magic happen — for real. #DataStrategy #Analytics #DataGovernance #GenAI #DigitalTransformation #DataFirst #AI #EnterpriseAI

Traditional data processing pipelines are dead. Or at least, fundamentally obsolete. For years, we've wrestled with data volume, slicing information into manageable chunks for analysis. This fragmented approach introduced significant overhead and often missed critical cross-contextual insights. Enter Gemini 1.5 Pro's 1-million-token context window. This isn't just an incremental improvement; it's a paradigm shift in how AI consumes and understands information. Imagine feeding an entire codebase, multiple analyst reports, or hours of video into a single model without summarization loss. This capability bypasses the need for complex pre-processing, allowing AI to identify patterns and relationships across truly massive datasets. The implication is profound: AI can now operate at a scale previously unimaginable for deep, singular analysis. This challenges every enterprise data strategy built on aggregation and indexing. Companies failing to adapt their workflows will drown in their own data, while others will unlock unprecedented efficiencies and insights by directly leveraging these expansive context windows. It redefines the architecture of intelligent systems. This isn't just about 'more data'; it's about radically different data interaction. The bottleneck has shifted from data ingestion to intelligent prompt engineering and strategic system design. Are your systems ready for a model that can 'read' your entire company in one go, or are you still relying on fragmented, pre-AI paradigms? #AI #GenerativeAI #LargeLanguageModels #DataStrategy #SystemDesign #Innovation

Last week, I shared a glimpse of my AI model designed to extract information from database while protecting data confidentiality. Today, I'm thrilled to share a major update! I've completely overhauled the architecture to create a more powerful and efficient solution. The new, detailed model structure features three specialized AI versions, each tailored for a specific task. This streamlined approach significantly expedites and enhances the accuracy of data execution, ensuring you get the right information, faster. But the most exciting development is the addition of an insights generation engine. This new capability allows the model to tackle indirect, strategic questions that require deep analysis—something a simple data query can't answer. Now, you can get detailed insights on questions like: - "What is the financial situation of the company?" - "Which region is more scalable in terms of ROI?" - "Evaluate company growth." This isn't just about pulling data; it's about generating actionable intelligence to drive better business decisions, all without ever compromising your confidential information. What are your thoughts on using specialized AI models to extract complex insights? #AI #DataAnalytics #MachineLearning #ArtificialIntelligence #DataScience #Innovation #TechForGood #BusinessIntelligence #DataPrivacy #DataConfidentiality #Analytics #Tech #FutureOfWork #AIforBusiness #DigitalTransformation #Automation #Insights #CustomAI #AIModels #EnterpriseAI #InformationSecurity

Only 5% of custom enterprise AI tools reach production. 🤯 Wow. I knew it was low, but not that low. Guess what the solution is to having a successful AI launch? No surprise here: - Clean data - Multiple POCs to test scenarios - Organizational readiness - Deploy iteratively and monitor - Continuous discovery and optimization Thank you to Minnestar for the community event, Jonathan Anderstrom, and Scott Litman for an excellent presentation and discussion on AI. You can find the stat and report here: https://lnkd.in/gj6APbbn

Data access shouldn't be a maze. 𝐂𝐨𝐠𝐞𝐧𝐭𝐢𝐪 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐒𝐭𝐨𝐫𝐞 reimagines data management with a 𝐬𝐞𝐜𝐮𝐫𝐞, 𝐩𝐞𝐫𝐬𝐨𝐧𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐦𝐚𝐫𝐤𝐞𝐭𝐩𝐥𝐚𝐜𝐞 where business users and producers can 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫, 𝐭𝐫𝐮𝐬𝐭, 𝐚𝐧𝐝 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭e on data products, all in one place. How it helps you:  🔸 Faster decisions with quick access to trusted, ready-to-use data  🔸 Less friction - no complex systems or confusing schemas  🔸 Self-service freedom to discover and collaborate without waiting on IT  🔸 Confidence in every action through secure, governed access  🔸 Time saved as producers publish and share data in one click    To learn more, click here: https://lnkd.in/d5XdKcQa #Cogentiq #EnterpriseStore #AgenticAI #AI