AI For Real-Time Data Processing

One term that keeps coming up lately in the world of AI is AI Fabric. But what does it actually mean, and why does it matter? In most organizations, one of the biggest challenges with AI isn't just building the models — it’s handling the data itself. Many companies are dealing with tons of data scattered across different systems, which slows down projects and creates unnecessary complexity. AI Fabric steps in to make sense of all that, creating a more streamlined, easy-to-understand view of the data for everyone involved. Here’s the twist: with generative AI gaining momentum, it’s not just about organizing data but describing it in ways AI can actually understand. Instead of just tables and columns, AI Fabric allows data to be labeled in terms we all recognize, like “sales” or “machine data,” making it easier for AI models to work with. Here is an example - https://bit.ly/4elRiFp Why it matters: - Cuts down on data complexity across systems - Helps businesses get AI models up and running faster - Provides clear, accurate insights without the “hallucinations” generative AI can sometimes produce - Makes it easy to switch between different large language models (LLMs) as AI tech evolves AI Fabric is all about taking data complexity and making it simple and actionable. If you’re looking to step up your data strategy and start seeing real results with AI, it’s worth checking out! What I love most about this approach is that it's not trying to rip and replace existing systems. It's about making what you already have work better for the AI age. Curious to learn more? Visit  - https://bit.ly/4elRiFp

What if one of the most important medical decisions of your life came down to five rushed minutes, and incomplete data? In a recent conversation with Fidji Simo, CEO of Applications at OpenAI, she shared a moment that should give every healthcare leader, operator, and technologist pause. While hospitalized, she was about to be given a standard antibiotic for a routine infection. On the surface, it was the correct protocol. But by quickly cross-referencing the drug against her full medical history using AI, she uncovered a critical risk. It could have reactivated a serious past C. diff infection. The physician’s response was telling. “I have five minutes to make rounds. I can’t review years of records.” Modern healthcare is still operating on fragmented data, siloed specialties, and time-constrained decision-making. Even the best clinicians are forced to make high-stakes calls without full context. And this is where the opportunity becomes clear. We are entering a new era where AI is not replacing clinicians, but augmenting their ability to see the whole picture. By connecting longitudinal health data, labs, genomics, wearables, and medical history, we can move from reactive care to truly informed, real-time decision-making. In our full discussion, we explore what this shift means at scale: • Why most clinical errors are not about knowledge gaps, but missing context • How fragmented health systems create unnecessary risk and inefficiency • What it looks like when AI becomes a layer of intelligence across the entire patient journey • So much more This is a systems design problem, a data problem, and ultimately, a leadership problem. The organizations that solve for context, not just care delivery, will define the future of health. Listen to our full conversation here: https://lnkd.in/g_2FsR2q

Every CEO feels it — decisions can’t wait. 📉 The pressure: Strategy, investor updates, and operations now move faster than your data. When metrics live in silos, blind spots multiply and decisions slow. 🤖 How AI is changing the game: AI copilots connect systems, summarize insights, and generate real-time dashboards in plain English—turning data chaos into clarity. ⸻ 8 AI tools redefining the CEO workflow: • Mosaic — A financial planning copilot that connects your ERP, CRM, and HR data into one dynamic dashboard. It builds rolling forecasts and scenario plans automatically, letting you stress-test strategies in seconds. Mosaic helps CEOs replace static spreadsheets with continuous, forward-looking visibility. • Pigment — A collaborative FP&A platform that unifies financial, sales, and operational data. It enables real-time “what-if” modeling and board-ready reporting without Excel chaos. Pigment turns complex planning into a shared, living process for leadership teams. • Microsoft Power BI + Copilot — Microsoft’s analytics suite now includes generative AI that narrates dashboards in natural language. You can ask questions like “What’s driving revenue variance this quarter?” and get instant, visual explanations. It helps CEOs see and understand key trends across every business unit. • Notion AI — More than a workspace, Notion AI drafts meeting summaries, strategy docs, and executive notes automatically. It centralizes company knowledge, connects projects to goals, and produces clear action items. CEOs use it as their digital chief of staff for information synthesis. • ChatGPT Enterprise + Slack Integration — Combines the reasoning power of ChatGPT with real-time Slack access. It retrieves internal data, answers operational questions, and drafts communications instantly. The result: instant, secure intelligence across every department—right in your workflow. • Perplexity Pro — An AI research assistant that provides live, source-cited answers from across the web. It tracks macro trends, competitor updates, and industry moves in real time. CEOs rely on it for fast, verifiable insights when preparing for board meetings or press briefings. • Kore.ai — An AI platform that listens to voice and text interactions across your enterprise to uncover operational signals. It builds conversational analytics layers for service, HR, and customer ops. For CEOs, Kore.ai reveals friction points and efficiency opportunities hiding in daily operations. • Broadwalk .ai — A next-generation copilot that transforms unstructured data—news, filings, sentiment, and market signals—into actionable insights. It helps leaders move from data to direction, detecting early sentiment shifts across portfolios, markets, and competitors. Broadwalk equips CEOs and fund managers with clarity before the market reacts. ⸻ 💡 The best CEOs don’t wait for reports anymore — they converse with their data.

For database developers, one time-consuming activity is SQL stored procedure optimization, which requires manually analyzing the procedure, identifying bottlenecks, and suggesting improvements when SQL code fails performance tests. That's why the engineering team at Agoda shared their LLM-based solution to tackle this challenge: plugging GPT directly into their CI/CD pipeline. Here’s how it works. When a SQL stored procedure fails its performance test, instead of escalating the issue to a database developer immediately, the system automatically sends the code to GPT along with rich context — including the table structures, existing indexes, and the performance report. GPT then does three things: it pinpoints what’s slow, rewrites the procedure with improved logic, and suggests better indexes. The result is automatically benchmarked against the original version and posted as a side-by-side comparison in the merge request — ready for the developer to review and apply with one click. The key design principle here is human-in-the-loop. Nothing gets applied without the engineer's sign-off. GPT handles the first pass; humans make the final call. And while GPT’s suggestion acceptance rate sits at around 25%, that’s still enough to dramatically reduce the manual burden. Instead of starting from scratch on every failure, developers now have a ready-made diagnosis and a performance-tested starting point. The broader takeaway is that you don’t need a perfect AI system to generate real business value. A tightly scoped, well-integrated use of a large language model — even with a 25% hit rate — can save hundreds of hours and meaningfully accelerate a team’s work. This is what practical AI adoption looks like in engineering: not a moonshot, but a smart, measurable solution to a specific pain point. #MachineLearning #LLM #HumanInTheLoop #Optimization #Database #Productivity #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gq4emDzq

Unlocking the Next Generation of AI: Synergizing Retrieval-Augmented Generation (RAG) with Advanced Reasoning Recent advances in large language models (LLMs) have propelled Retrieval-Augmented Generation (RAG) to new heights, but the real breakthrough comes from tightly integrating sophisticated reasoning capabilities with retrieval. A recent comprehensive review by leading research institutes in China systematically explores this synergy, laying out a technical roadmap for building the next generation of intelligent, reliable, and adaptable AI systems. What's New in RAG + Reasoning? Traditional RAG systems enhance LLMs by retrieving external, up-to-date knowledge, overcoming issues like knowledge staleness and hallucination. However, they often fall short in handling ambiguous queries, complex multi-hop reasoning, and decision-making under constraints. The integration of advanced reasoning-structured, multi-step processes that dynamically decompose problems and iteratively refine solutions-addresses these gaps. How Does It Work Under the Hood? - Bidirectional Synergy:    - Reasoning-Augmented Retrieval dynamically refines retrieval strategies through logical analysis, query reformulation, and intent disambiguation. For example, instead of matching keywords, the system can break down a complex medical query into sub-questions, retrieve relevant guidelines, and iteratively refine results for coherence.  - Retrieval-Augmented Reasoning grounds the model's reasoning in real-time, domain-specific knowledge, enabling robust multi-step inference, logical verification, and dynamic supplementation of missing information during reasoning. - Architectural Paradigms:    - Pre-defined Workflows use fixed, modular pipelines with reasoning steps before, after, or interleaved with retrieval. This ensures clarity and reproducibility, ideal for scenarios demanding strict process control.  - Dynamic Workflows empower LLMs with real-time decision-making-triggering retrieval, generation, or verification as needed, based on context. This enables proactivity, reflection, and feedback-driven adaptation, closely mimicking expert human reasoning. - Technical Implementations:    - Chain-of-Thought (CoT) Reasoning explicitly guides multi-step inference, breaking complex tasks into manageable steps.  - Special Token Prediction allows models to autonomously trigger retrieval or tool use within generated text, enabling context-aware, on-demand knowledge integration.  - Search-Driven and Graph-Based Reasoning leverage structured search strategies and knowledge graphs to manage multi-hop, cross-modal, and domain-specific tasks.  - Reinforcement Learning (RL) and Prompt Engineering optimize retrieval-reasoning policies, balancing accuracy, efficiency, and adaptability.

AI Agents: Turning Possibility Into Action 🚀 Imagine you have a digital colleague who not only understands your requests but also plans the next steps and executes them - no micromanagement required! That’s exactly what AI Agents can do: bridging the gap between data and decisions by integrating real-time tools and orchestrating complex tasks. Here’s why they’re a game-changer: • Reason & Act: Traditional Large Language Models can chat, but AI Agents go further. They manage external APIs, knowledge bases and workflows to deliver real, tangible outcomes. • The Power of Tools: From fetching flight deals to updating databases, these Agents connect to Extensions, Functions or Data Stores for fresh, up-to-date information. • Intelligent Orchestration: Methods like ReAct or Chain-of-Thought keep Agents on track. They “think” out loud, plan the next action and then switch to the right tools - just like a seasoned project manager. • Flexible Integration: Whether you need on-the-fly solutions or a rigorous client-side workflow, AI Agents adapt. That means faster prototyping and more confident decisions. • Data At Scale: Vector databases, RAG setups and dynamic data retrieval push the limits of what your system can learn and execute on the spot. With AI Agents, you don’t just brainstorm - you build, automate and iterate. Think of it as having your own tech-savvy sidekick who never sleeps and always hustles. On that note, I’ll let my Agent handle my paperwork now. It’s apparently more dedicated than I am on a Monday morning. 😏 #Leadership #Mindset #DataManagement #AI #Automation #GenerativeAI #Innovation #Tech #Business

Your pipeline has 47 steps. You built them all by hand. AI can maintain them for you. I work with data pipelines daily. Most of the work is repetitive. Schema changes. Data validation. Transformation logic. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐞𝐫𝐞 𝐀𝐈 𝐡𝐞𝐥𝐩𝐬 𝐦𝐨𝐬𝐭: → Write SQL transformations from plain English. → Generate data validation checks automatically. → Detect schema drift before it breaks production. → Document pipeline steps you never documented. 𝐓𝐡𝐞 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐟𝐢𝐫𝐬𝐭 𝐬𝐭𝐞𝐩: 1. Take your messiest SQL query. 2. Paste it into Claude. 3. Ask it to optimize, document, and add error handling. You will save hours on your first try. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐬𝐡𝐢𝐟𝐭: - Data engineers who use AI don't write less code. - They write better code, faster. - They spend time on design, not debugging typos. If you have a pipeline trick using AI, share it below so others can benefit.

Access to a real-time AI decision support tool during primary care visits in Nairobi cut diagnostic errors by 16% and treatment errors by 13%, with no added harm reported. 1️⃣ This study tested “AI Consult,” an LLM-powered tool integrated into EMRs at 15 Penda Health clinics in Kenya. 2️⃣ The tool ran in the background, issuing alerts only when needed (green/yellow/red), preserving clinician autonomy. 3️⃣ Across 39,849 visits, clinicians with AI support made 16% fewer diagnostic errors and 13% fewer treatment errors, as judged by blinded physician review. 4️⃣ Estimated annually, AI Consult could prevent 22,000 diagnostic and 29,000 treatment errors at Penda alone. 5️⃣ The largest error reductions were in history-taking (32% relative risk reduction) and treatment safety (NNT = 13.9). 6️⃣ Clinicians with the tool gradually made fewer mistakes even before receiving alerts, suggesting it helped build better habits. 7️⃣ All clinicians surveyed said AI Consult improved care; 75% said the improvement was “substantial.” 8️⃣ No safety events were attributed to AI Consult, and alert fatigue was mitigated through careful interface and threshold design. 9️⃣ Uptake increased after targeted deployment strategies: coaching, peer champions, and performance feedback. 🔟 The study underscores that success came not just from the model itself, but from aligning tech design with clinical workflow. ✍🏻 Robert Korom, Sarah Kiptinness, Najib Adan, Kassim Said, Catherine Ithuli, Oliver Rotich, Boniface Kimani, Irene King’ori, Stellah Kamau, Elizabeth Atemba, Muna Aden, Preston Bowman, Michael Sharman, Rebecca Soskin Hicks, MD, Rebecca Distler, Johannes H., Rahul K. Arora, Karan Singhal. AI-based Clinical Decision Support for Primary Care: A Real-World Study. 2025. DOI: 10.48550/arXiv.2407.12986

From raw sensor readings to intelligent automation - this 15-step pipeline shows how IoT data evolves into real-time insights and actions. I've seen teams miss steps here, and it always costs them. ➞ Data Capture: Sensors collect raw environmental and machine data such as motion, pressure, and temperature. ➞ Device Connectivity: Devices securely transmit this data through reliable IoT networks. ➞ Edge Filtering: Redundant and noisy data is filtered at the edge to reduce latency and bandwidth use. ➞ Data Aggregation: Sensor streams are merged and structured for consistent downstream processing. ➞ Gateway Management: IoT gateways securely handle data routing, device validation, and communication. ➞ Stream Processing: Tools like Kafka or MQTT process real-time data for instant insights. ➞ Cloud Storage: Clean data is stored in data lakes or databases for long-term access and analytics. ➞ Data Transformation: Standardizes, cleans, and enriches data for AI or predictive modeling. ➞ Visualization Layer: Dashboards and BI tools reveal real-time patterns and performance trends. ➞ Security & Compliance: Implements encryption, authentication, and regulatory compliance to protect sensitive data. ➞ Predictive Modeling: AI models forecast trends and automate decisions before issues occur. ➞ Edge AI Execution: Lightweight models run directly on devices for low-latency, offline intelligence. ➞ Automated Workflows: System triggers automate alerts, adjustments, and responses in real time. ➞ Self-Healing Systems: AIoT frameworks detect, diagnose, and fix problems with minimal human intervention. ➞ Continuous Optimization: Feedback loops improve performance, reliability, and efficiency over time. Building an AI-powered IoT system? Save this roadmap and use it to design smarter, data-driven pipelines. 🔁 Repost if you're building for the real world, not just connected demos. ➕ Follow Nick Tudor for more insights on AI + IoT that actually ship.