Enterprise AI Meets Structured Data Challenges

If you can't explain how GenAI calculated that number, you shouldn't be making decisions off it. More leaders are catching on to this. Probabilistic models will never be 100% correct. Well written Shashi Bellamkonda and Igor Ikonnikov.

💬 Nearly a decade of work has gone into building language models that are structurally tied to each customer's schema and business logic. The goal has always been the same: when a finance team, compliance officer, or operations lead looks at a number, they should trust it and be able to trace it all the way back — every time, without exception. We appreciate Shashi Bellamkonda, Igor Ikonnikov, and Info-Tech Research Group taking the time to examine what #deterministicAI actually means in practice. This kind of analysis helps the market ask better questions every time they explore AI solutions.

Chata.ai: Deterministic AI That Does Not Lie Here's a stat that should concern anyone deploying LLM-based analytics in production: A 95% accuracy rate sounds manageable — until you compound it. Run a three-step analytics workflow and accuracy drops to 85.7%. A ten-step workflow? You're down to 59.9%. In regulated environments, that's not a rounding error. It's a compliance risk dressed up as automation. I wrote this article with my colleague Igor Ikonnikov at Info-Tech Research Group exploring why you don't always need a large language model when you need accurate, auditable answers from your data. That's where Chata.ai stands apart and they've been building this for nearly a decade. Here's the key architectural difference: Chata.ai does not use a general-purpose LLM. It builds a structurally separate, deterministic custom language model from each customer's database schema and business logic. It translates natural language into precise database queries. The database returns the answer. No prediction. No hallucination. No guessing. What that means in practice: 🎯 No hallucinations — The system executes defined logic against actual data. There is no mechanism by which it fabricates a figure. 🔒 Zero data movement — Customer data never enters the training process. Nothing leaves your environment. 📋 Full audit trail — Every query is logged. Every result traces back to the exact logic that produced it. Compliance teams can reproduce any output on demand. 💰 CPU-based inference — Runs at roughly 0.2% of a comparable generative AI deployment cost. No GPU infrastructure required. One critical distinction: this is a precision analytics tool for structured data in defined domains — not a general reasoning system. If you need auditable answers from your financial, operational, or regulatory data with a full audit trail, this is exactly what it's built for. The question every stakeholder should ask their AI analytics vendor: "When I look at a number this system produced, can your team reproduce exactly how it was computed?" For most LLM tools, that question has no satisfying answer. For Chata.ai, it does. Read the full article: https://lnkd.in/ea7e7gtR #AI #DeterministicAI #DataAnalytics #BusinessIntelligence #Compliance #RegTech #AIStrategy #StructuredData

This is one of the best visual roadmaps for understanding modern AI systems. Most people only learn how to call an LLM API. Very few learn how to build a complete RAG ecosystem. And that difference changes everything. From this cheat sheet, you can clearly see that production-grade AI is not just: “Upload PDF → Ask Question → Get Answer” Real-world RAG systems involve: Data ingestion Metadata tagging Chunking strategies Embeddings Vector databases Hybrid search Reranking Prompt engineering Memory systems Evaluation metrics Observability pipelines The biggest lesson? A powerful AI application is not built by one model alone. It is built by the orchestration of multiple intelligent components working together. The companies winning in AI today are not necessarily the ones with the biggest models. They are the ones building: • Better retrieval pipelines • Smarter context systems • Faster search architectures • Reliable evaluation frameworks • Strong observability and monitoring Because in production AI: Retrieval quality > Prompt tricks Context quality > Model size System design > Hype One thing I loved about this cheat sheet is how it breaks down every critical layer of modern RAG and Agentic AI systems in a practical way — from ingestion to observability. The future AI engineers will not just “use AI.” They will engineer intelligent systems end-to-end. #AI #RAG #AgenticAI #LLM #MachineLearning #DataScience #ArtificialIntelligence #AIEngineering #GenerativeAI #SystemDesign

AI WILL EXPOSE WEAK FOUNDATIONS AI systems amplify whatever data foundation already exists. 🤖 If your architecture is: ⚠️ fragmented ⚠️ duplicated ⚠️ inconsistent ⚠️ loosely governed AI inherits all of it. Now instead of: ❌ humans debating dashboards You risk: ❌ AI systems acting on fragmented context That’s why modern infrastructure conversations are shifting. The focus is no longer only: “Can we build AI?” It’s becoming: “Can our data architecture support it reliably?” 💡 AI readiness starts long before models. It starts with infrastructure. #Klaritics 🚀 #WarehouseNative 🏗️ #AnalyticsOnWarehouse ⚡ #AI 🤖 #DataArchitecture 🏗️ #ProductAnalytics 🚀 #ModernDataStack ⚡ #DataInfrastructure 🧠 #AnalyticsEngineering 📊 Image generation using flash 3

One common mistake I see tech teams make when building AI solutions: They focus on the tool first instead of the workflow. The conversation usually starts with: “How can we use LLMs here?” when the better question is: “Where is the actual operational bottleneck?” If you want to move beyond basic AI experiments and create real business value, this simple 3-step framework helps a lot: 1️⃣ Isolate the Friction Find repeatable, rules-based tasks that consume a lot of engineering or operations time — things like parsing messy vendor logs or standardizing unstructured data. 2️⃣ Define the Boundary Don’t ask AI to handle the entire workflow. Clearly define the inputs and the exact structured output you expect, such as a fixed JSON schema. 3️⃣ Implement the Guardrails Wrap deterministic validation around probabilistic AI outputs. An LLM should never directly interact with a production database without automated validation checks in place. The strongest AI systems don’t feel like “magic chatbots.” They behave more like highly optimized, reliable data pipelines. Build for reliability first. Scale second. 👉 What’s the biggest challenge your team faces when taking an AI feature from development to production? Would love to hear different perspectives. #SoftwareEngineering #TechArchitecture #GenerativeAI #ProductManagement #DataEngineering

Managing hallucinations: RAG vs fine-tuning tradeoffs in enterprise applications As AI agents become more pervasive in our daily lives, it's becoming increasingly clear that hallucinations – the tendency for models to generate false or misleading information – are a major challenge. In this post, we'll explore the tradeoffs between RAG (Retrieve-Augment-Generate) pipelines and fine-tuning in enterprise applications. When it comes to managing hallucinations, RAG pipelines have gained popularity due to their ability to leverage large language models (LLMs) and external knowledge graphs. By retrieving relevant information from these graphs and augmenting it with LLM-generated text, RAG pipelines can produce high-quality output. However, this approach also introduces new challenges, such as the risk of propagating errors or biases from the underlying knowledge graph. Fine-tuning, on the other hand, involves training a model on a specific task or dataset to adapt its performance to new contexts. While fine-tuning can be effective in reducing hallucinations, it requires significant computational resources and can be time-consuming. In reality, most enterprise applications don't have the luxury of retraining models from scratch. They need to balance the tradeoffs between RAG pipelines and fine-tuning while ensuring that their AI agents don't compromise the accuracy or reliability of their output. This is where the nuances of AI engineering come into play. By understanding the strengths and weaknesses of RAG pipelines and fine-tuning, developers can make informed decisions about how to manage hallucinations in their applications. This might involve implementing additional checks and balances, such as context compression or hybrid search, to reduce the risk of false positives. Ultimately, the key to managing hallucinations lies in finding the right balance between RAG pipelines and fine-tuning. By embracing the complexities of AI engineering and investing in the development of robust and reliable AI agents, we can unlock the full potential of these technologies while minimizing the risk of catastrophic failure. #AIEngineering #LLM #RAGpipelines #FineTuning #Hallucinations #EnterpriseAI

💡 Why "Smaller" is the Next Big Thing in Agentic AI 🤖 For the past two years, the tech world has been obsessed with "bigger is better" in generative AI. But as we transition from basic chatbots to Agentic AI—where models must reason, plan, and execute multi-step workflows—the reality of the balance sheet is hitting hard. The truth? Deploying Large Language Models (LLMs) for complex agentic loops is financially unsustainable for most enterprise use cases. When an agent needs to make 10 to 20 sequential API calls, reason through tools, and self-correct just to solve a single user task, the compounding token costs and latency of a massive LLM can break a product before it even scales. Enter the rise of Small Language Models (SLMs). Forward-thinking engineering teams are shifting from monolithic LLMs to specialized, orchestrator-agent architectures using nimble SLMs. Here is why: Optimizing costs at scale: Running fine-tuned SLMs can reduce inference costs by 80% or more compared to proprietary frontier models. Speed & Latency: Agentic workflows require rapid iteration. SLMs provide the low-latency response times critical for real-time decision-making loops. Hyper-Specialization: You don’t need a model that knows the history of 17th-century art to extract JSON from an invoice. SLMs fine-tuned on specific tasks (like function calling or coding) often match or exceed LLM performance. Privacy & Edge Deployment: SLMs can be hosted locally or on private clouds, ensuring enterprise data security without massive cloud compute bills. The Future is Hybrid: The winning architecture isn't about ditching LLMs entirely. It’s about using an LLM as the high-level "brain" or planner, while routing the heavy lifting and execution to a fleet of highly efficient, domain-specific SLMs. Bigger isn't always smarter. In the world of Agentic AI, efficiency is the ultimate competitive advantage. 👇 Are you experimenting with SLMs in your production stacks? I’d love to hear your thoughts on the balance between model size and cost efficiency in the comments. #AI #GenerativeAI #AgenticAI #MachineLearning #TechStrategy #EnterpriseAI

A year ago AI answered questions. Today it's executing workflows, calling APIs, querying databases, and making decisions — autonomously. I'm building exactly this — agentic systems that don't wait for a human to hit a button. They react to events, pull data, and take action in real time. The data layer has to be ready for this. Clean schemas. Reliable APIs. Clear governance. Because when an agent makes a bad decision at scale, it's not one mistake. It's thousands. Are you building with AI agents yet? #AgenticAI #DataScience #AI #MLOps #Automation

Most AI systems today are still built around one idea: Search → Retrieve → Generate. That worked well for the first wave of enterprise AI. But the next generation of systems is moving beyond retrieval alone. The shift is happening from: “finding information” to: “understanding relationships and reasoning through them.” This is where the difference between RAG and KAG becomes important. RAG, Retrieval-Augmented Generation RAG connects LLMs to external knowledge sources such as: • PDFs • databases • APIs • enterprise documents • vector stores Instead of relying only on pretrained knowledge, the system retrieves relevant information before generating a response. Core flow: Query → Embedding → Vector Search → Context Retrieval → LLM → Response Why enterprises adopt RAG: • fast implementation • real-time knowledge updates • scalable enterprise search • strong document intelligence workflows But there is still a limitation. RAG retrieves information well. It does not deeply understand how information connects together. KAG, Knowledge-Augmented Generation KAG adds structured reasoning into the architecture. Instead of retrieving isolated chunks, the system understands: • entities • relationships • dependencies • semantic context This is enabled through: • knowledge graphs • reasoning engines • memory layers • structured relationship mapping Core flow: Query → Knowledge Graph → Relationship Mapping → Reasoning Layer → LLM → Decision This changes the system from retrieval-focused to reasoning-focused. Why KAG matters: • deeper contextual understanding • stronger consistency • multi-hop reasoning • better support for agentic systems The trade-off is complexity. KAG systems require significantly more architectural design, governance, and maintenance. The real future is not RAG versus KAG. It is systems that combine: • RAG for dynamic retrieval • KAG for structured reasoning • AI agents for execution and autonomy That is where enterprise AI is heading. The competitive advantage will not come from the model alone. It will come from the architecture surrounding the model. Modern AI systems are becoming: • context-aware • memory-driven • tool-integrated • reasoning-capable • agentic by design The companies that understand this shift early will design systems others cannot easily replicate. #ArtificialIntelligence #GenerativeAI #RAG #KAG #LLM #AIArchitecture #AgenticAI #KnowledgeGraphs #AIEngineering #KansasCity #KansasCityTech