AI For Real-Time Data Processing

The initial gold rush of building AI applications is rapidly maturing into a structured engineering discipline. While early prototypes could be built with a simple API wrapper, production-grade AI requires a sophisticated, resilient, and scalable architecture. Here is an analysis of the core components: 𝟭. 𝗧𝗵𝗲 𝗡𝗲𝘄 "𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗼𝗿𝗲": The Brain, Nervous System, and Memory At the heart of this stack lies a trinity of components that differentiate AI applications from traditional software:  • Model Layer (The Brain): This is the engine of reasoning and generation (OpenAI, Llama, Claude). The choice here dictates the application's core capabilities, cost, and performance.  • Orchestration & Agents (The Nervous System): Frameworks like LangChain, CrewAI, and Semantic Kernel are not just "glue code." They are the operational logic layer that translates user intent into complex, multi-step workflows, tool usage, and function calls. This is where you bestow agency upon the LLM.  • Vector Databases (The Memory): Serving as the AI's long-term memory, vector databases (Pinecone, Weaviate, Chroma) are critical for implementing effective Retrieval-Augmented Generation (RAG). They enable the model to access and reason over proprietary, real-time data, mitigating hallucinations and providing contextually rich responses. 𝟮. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗚𝗿𝗮𝗱𝗲 𝗦𝗰𝗮𝗳𝗳𝗼𝗹𝗱𝗶𝗻𝗴: Scalability and Reliability The intelligence core cannot operate in a vacuum. It is supported by established software engineering best practices that ensure the application is robust, scalable, and user-friendly:  • Frontend & Backend: These familiar layers (React, FastAPI, Spring Boot) remain the backbone of user interaction and business logic. The key challenge is designing seamless UIs for non-deterministic outputs and architecting backends that can handle asynchronous, long-running agent tasks.  • Cloud & CI/CD: The principles of DevOps are more critical than ever. Infrastructure-as-Code (Terraform), containerization (Kubernetes), and automated pipelines (GitHub Actions) are essential for managing the complexity of these multi-component systems and ensuring reproducible deployments. 𝟯. 𝗧𝗵𝗲 𝗟𝗮𝘀𝘁 𝗠𝗶𝗹𝗲: Governance, Safety, and Data Integrity. The most mature AI teams are now focusing heavily on this operational frontier:  • Monitoring & Guardrails: In a world of non-deterministic models, you cannot simply monitor for HTTP 500 errors. Tools like Guardrails AI, Trulens, and Llamaguard are emerging to evaluate output quality, prevent prompt injections, enforce brand safety, and control runaway operational costs.  • Data Infrastructure: The performance of any RAG system is contingent on the quality of the data it retrieves. Robust data pipelines (Airflow, Spark, Prefect) are crucial for ingesting, cleaning, chunking, and embedding massive volumes of unstructured data into the vector databases that feed the models.

AI breaks the data stack. Most enterprises spent the past decade building sophisticated data stacks. ETL pipelines move data into warehouses. Transformation layers clean data for analytics. BI tools surface insights to users. This architecture worked for traditional analytics. But AI demands something different. It needs continuous feedback loops. It requires real-time embeddings & context retrieval. Consider a customer at an ATM withdrawing pocket money. The AI agent on their mobile app needs to know about that $40 transaction within seconds. Data accuracy & speed aren’t optional. Netflix rebuilt their entire recommendation infrastructure to support real-time model updates1. Stripe created unified pipelines where payment data flows into fraud models within milliseconds2. The modern AI stack requires a fundamentally different architecture. Data flows from diverse systems into vector databases, where embeddings & high-dimensional data live alongside traditional structured data. Context databases store the institutional knowledge that informs AI decisions. AI systems consume this data, then enter experimentation loops. GEPA & DSPy enable evolutionary optimization across multiple quality dimensions. Evaluations measure performance. Reinforcement learning trains agents to navigate complex enterprise environments. Underpinning everything is an observability layer. The entire system needs accurate data & fast. That’s why data observability will also fuse with AI observability to provide data engineers & AI engineers end-to-end understanding of the health of their pipelines. Data & AI infrastructure aren’t converging. They’ve already fused. References Netflix Technology Blog. (2025, August). “From Facts & Metrics to Media Machine Learning: Evolving the Data Engineering Function at Netflix.” https://lnkd.in/g7XhVf2u ↩︎ Stripe. (2025). “How We Built It: Stripe Radar.” https://lnkd.in/gXtkcWjq ↩︎

‼️Ever wonder how data flows from collection to intelligent action? Here’s a clear breakdown of the full Data & AI Tech Stack from raw input to insight-driven automation. Whether you're a data engineer, analyst, or AI builder, understanding each layer is key to creating scalable, intelligent systems. Let’s walk through the stack step by step: 1. 🔹Data Sources Everything begins with data. Pull it from apps, sensors, APIs, CRMs, or logs. This raw data is the fuel of every AI system. 2. 🔹Ingestion Layer Tools like Kafka, Flume, or Fivetran collect and move data into your system in real time or batches. 3. 🔹Storage Layer Store structured and unstructured data using data lakes (e.g., S3, HDFS) or warehouses (e.g., Snowflake, BigQuery). 4. 🔹Processing Layer Use Spark, DBT, or Airflow to clean, transform, and prepare data for analysis and AI. 5. 🔹Data Orchestration Schedule, monitor, and manage pipelines. Tools like Prefect and Dagster ensure your workflows run reliably and on time. 6. 🔹Feature Store Reusable, real-time features are managed here. Tecton or Feast allows consistency between training and production. 7. 🔹AI/ML Layer Train and deploy models using platforms like SageMaker, Vertex AI, or open-source libraries like PyTorch and TensorFlow. 8. 🔹Vector DB + RAG Store embeddings and retrieve relevant chunks with tools like Pinecone or Weaviate for smart assistant queries using Retrieval-Augmented Generation (RAG). 9. 🔹AI Agents & Workflows Put it all together. Tools like LangChain, AutoGen, and Flowise help you build agents that reason, decide, and act autonomously. 🚀 Highly recommend becoming familiar this stack to help you go from data to decisions with confidence. 📌 Save this post as your go-to guide for designing modern, intelligent AI systems. #data #technology #artificialintelligence

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.

𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗜𝘀 𝗮 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Much of today’s conversation around AI agents focuses on #graphs, #models, #prompts, #context, or orchestration #frameworks. These topics matter, but they rarely determine whether an AI system succeeds once it moves from prototype to enterprise production. The real challenges appear when AI systems operate inside long-running business workflows. Consider a workflow that analyzes documents, retrieves data from multiple systems, calls APIs, and produces a structured decision. Such processes may run for twenty or thirty minutes and involve dozens of steps. Now imagine something routine happens: a network call fails, an API times out, or a container restarts. No problem, the agent says. It starts the workflow again. That may be acceptable for chatbots. It quickly becomes impractical for enterprise processes such as financial analysis, document processing, underwriting, or claims review. These workflows are long-running, resource-intensive, and deeply connected to operational systems. In these situations, the limitation is rarely the model’s intelligence. More often, the challenge lies in the #engineering #discipline around the system. At Cognida.ai, our focus is on building practical enterprise AI systems rather than demos or PoCs. We consistently find that several principles from #distributedsystems engineering become essential once AI moves into production. Here are three such constructs: 𝗗𝘂𝗿𝗮𝗯𝗹𝗲 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 Agent workflows should not be treated as temporary requests. Each step should persist its state so that if a failure occurs, the system can resume from the last successful step rather than restarting the entire process. In practice, this means workflow orchestration with checkpointed state, deterministic execution, and event-driven recovery. For long-running processes, this is often the difference between a prototype and a production system. 𝗜𝗱𝗲𝗺𝗽𝗼𝘁𝗲𝗻𝘁 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 AI agents increasingly trigger real-world actions: sending emails, calling APIs, updating records, moving files, or initiating financial transactions. Retries are inevitable in distributed systems. If actions are not idempotent, retries can create duplicate or inconsistent results. Reliable AI systems must ensure the same action cannot run twice unintentionally. 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗦𝘁𝗮𝘁𝗲 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 Large language models operate within limited context windows rather than durable memory. Enterprise workflows often run longer and across many stages. The system managing the workflow must maintain its own persistent state instead of relying on the model’s temporary context. It means treating AI workflows as structured state machines, not simple prompt-response interactions. Are you treating AI workflows more like state machines, event-driven systems, or traditional #microservices? #PracticalAI #EnterpriseAI

Looking at the latest research from the Universität Innsbruck and CASABLANCA hotelsoftware GmbH on RAG implementation in industry - this study offers critical insights into the real-world deployment challenges we're all facing. Key Technical Findings: The research reveals that most industrial RAG systems are still operating at prototype stages (12/13 companies below TRL 7), primarily focused on domain-specific question answering rather than the six application categories outlined in academic literature. Under the Hood Challenges: Data preprocessing emerges as the biggest technical hurdle. The study identifies four critical challenge categories: - Data Management: Handling unstructured data variety across PDFs, images, and documents requires substantial preprocessing effort. Identity recognition becomes complex when the same abbreviation represents different concepts across documents. - Retrieval Component: Determining optimal chunking strategies proves challenging - chunks must be large enough for context but not so large they overwhelm the generator. Embedding strategy selection significantly impacts retrieval quality. - Generator Issues: Hallucination remains a persistent problem, with LLMs failing to accurately convey retrieved information or introducing erroneous details not present in source documents. - System-wide Concerns: Right scope selection and access management across departments create architectural complexity. Industry vs Research Gap: Surprisingly, evaluation remains predominantly manual rather than automated. While academic research has developed frameworks like RAGAS, industry practitioners rely heavily on human assessment due to the lack of domain-specific test datasets. Requirements Reality Check: Security and data protection rank highest (8.5-8.9/10), while ethical considerations and bias mitigation score surprisingly low (5.6/10) - revealing a focus on immediate technical concerns over longer-term AI governance. The bottom line: successful RAG implementation requires modular architecture, significant data preparation investment, and careful chunking optimization. Each use case demands tailored approaches rather than one-size-fits-all solutions.

Yesterday's Arm announcement is not just a chip story, it is shaping data strategy and AI 💙 What’s being introduced is a compute designed for continuous AI and agentic systems. These are workloads that fundamentally reshape how data needs to flow, persist, and be accessed; they make us, as data people, stop and think! As a data industry practitioner and academic, this is a data and AI infrastructure pivotal moment, and here’s why: 1. From model-centric to system-centric AI This isn’t about accelerating individual models. It’s about enabling systems of agents that continuously reason, act, and adapt. →Think of a customer service platform: not a single chatbot answering queries, but multiple agents handling detection, resolution, escalation, and follow-up, sharing context in real time. This requires persistent memory and coordinated data access, not isolated model calls. 2. Always-on AI changes the data lifecycle We are moving from episodic workloads to continuous execution. Data pipelines can no longer be batch or even event-driven; they must become stateful, streaming, and context-aware by design. →Think fraud detection: instead of flagging anomalies hours later, systems now evaluate transactions as they happen, using live behavioural context to block risk instantly. 3. Data gravity becomes the architecture driver These workloads don’t tolerate latency. Compute must move closer to where data is generated across edge and cloud. → Consider smart manufacturing: AI models running on factory floors analyse sensor data in real time to prevent defects. Sending everything to the cloud is simply too slow and costly. 4. We are entering the era of AI operating on data continuously This is infrastructure built not just for humans querying models, but for AI systems interacting with data in real time, at scale. → Think of supply chain optimisation: AI agents continuously adjusting inventory, routing, and demand forecasts not based on static reports, but on live signals across the network. And that leads to a more important question: 👉 Is your data strategy designed for static models… Or for autonomous systems that will continuously operate on your data? Have I got you excited about the announcement as I am? This is pivotal for us working in the data and AI space! #AI #DataStrategy #AgenticAI #EmergingTech #DataArchitecture #DigitalTransformation