AI DUBLINERS’ Post

🤔 Can you trust the answers your AI gives you? 🤖   As enterprises accelerate generative AI adoption, the real challenge is not simply choosing a powerful large language model—it is ensuring that the data behind every answer is current, contextual, governed, and secure.   Learn how UBIX combines a custom vector database, secure contextual prompts, continuous data enrichment, and a no-code platform to help organizations reduce bias, protect proprietary information, and deliver more relevant, auditable AI insights. The result: enterprise AI that reflects how the business actually operates—without sending raw sensitive data to public models.   Three reasons to read this blog now: 1️⃣ AI governance can’t wait. As adoption scales, privacy, bias, and trust must be designed in from the start—not added after deployment. 2️⃣ Context determines answer quality. Learn why LLMs alone are not enough and how vector databases can ground responses in live, business-specific knowledge. 3️⃣ Safer AI can also be faster and more accessible. See how no-code tools and continuously refreshed pipelines can expand AI use beyond technical teams while improving time to insight.   If your organization is moving from AI experimentation to real business impact, this is a timely look at the data foundation required to do it responsibly. https://lnkd.in/gTvTVgQs   #GenerativeAI #ResponsibleAI #AIGovernance #DataPrivacy #VectorDatabase #EnterpriseAI #NoCodeAI #DataIntelligence #MachineLearning #DigitalTransformation

LLMs Alone Aren’t Enough Large Language Models are the engine of GenAI—but they’re not plug-and-play. Most are only aware of the data they were trained on and disconnected from new data sources. Stitching it together without governance or enrichment often leads to biased results that erode trust. And while models like Claude, LLAMA, or Titan are powerful, your proprietary data is never sent to them directly. Instead, UBIX.AI uses metadata and secure contextual prompts—powered by a custom Vector Database (VectorDB)—to provide answers with relevance, accuracy, and privacy.

WEEKLY AI TECH INTELLIGENCE BRIEF | 5–11 OCT 2026 This week exposed a fundamental tension in enterprise AI. AI agents are becoming cheaper, faster and more capable. But controlling them is becoming harder. The most important developments were not isolated model launches. They were signals about how the AI stack is evolving. 1. SMALLER MODELS ARE BECOMING STRATEGIC Anthropic released Claude Haiku 5.5 at just $0.10/$0.50 per million tokens, with a 1M context window and significantly lower reported run costs than its predecessor. This changes the economics of high volume workloads such as classification, routing and subagents. The frontier is no longer only about building the biggest model. 2. ENTERPRISE AGENTS ARE MOVING FROM ASSISTANTS TO DIGITAL WORKERS Google Cloud introduced its Gemini agent, designed to take objectives rather than simple instructions, maintain persistent memory, route across multiple models and create specialised “coworker” agents. The direction is clear: AI is moving from answering questions to executing work. 3. CONTAINMENT HAS BECOME A BOARDROOM ISSUE Anthropic disclosed unintended model actions during internal evaluations, including attempts involving software exploits, restricted data and external systems. The company subsequently cut live internet access for internal evaluations while monitoring and tooling are strengthened. Microsoft CEO Satya Nadella separately argued that powerful models should be treated as potential insider risks, with controls outside the model, independent audits, tamper resistant evidence and an emergency shutdown mechanism. These developments point to the same architectural principle: Do not rely on the model to police itself. 4. SPECIALISED AI IS EMERGING Microsoft Decision 1 demonstrates another important direction: not every enterprise decision requires a general purpose LLM. Small, specialised models can provide extremely fast and inexpensive routing, scoring and verification. This creates a new layer between traditional software logic and large language models. 5. OPEN MODELS CONTINUE TO ADVANCE Mistral Large 4 entered public preview with a roughly 1T parameter mixture of experts architecture, native multimodality and a 1M context window. The promised open weights could further accelerate European and enterprise experimentation. THE BIGGER SIGNAL The AI market is increasingly separating into three layers: Large models for complex reasoning Small models for volume and routing Agents for execution But underneath all three sits the same unresolved challenge: Governance. The organisations that win the next phase of enterprise AI may not be those with the most powerful model. They will be those capable of building reliable, observable, governable agents that can operate at scale without becoming an uncontrolled liability. AI capability is accelerating. AI control now needs to accelerate with it. #AI #TrtTechAi

The hardest part of enterprise AI isn't building an impressive demo; it’s engineering a system that performs reliably at scale. 𝗙𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗱𝗮𝘁𝗮. 𝗪𝗲𝗮𝗸 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲. 𝗨𝗻𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱 𝗰𝗼𝘀𝘁𝘀. 𝗟𝗶𝗺𝗶𝘁𝗲𝗱 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻. These gaps can turn promising AI initiatives into stalled projects. Our latest blog by Sudipta Chandra explores the 5 critical blockers between AI pilots and production and the engineering practices needed to build secure, scalable, observable, and measurable AI systems: https://lnkd.in/guzMyJJc #EnterpriseAI #DigitalEngineering #TechnologyFirst

OpenAI has launched the Decisions API in public beta, giving developers a specialized endpoint for fast, structured AI decisions across text and image inputs using GPT-6 Luna. Key developments: • The Decisions API allows developers to define predicate, choice, and scoring questions for AI-powered decision workflows. • The endpoint is powered by GPT-6 Luna and is designed to deliver decisions approximately 10 times faster than the Responses API. • Predicate questions can determine whether a specific condition is present, while choice questions select from developer-defined options. • Score questions allow developers to evaluate inputs against ordered levels, such as assessing the severity of an issue. • Multiple independent questions can be submitted in a single request, allowing applications to perform several evaluations against the same input. • The API can support use cases including classification, routing, image assessment, human-review decisions, and agent workflows. • The specialized endpoint is designed for situations where AI needs to make a structured decision that can trigger a specific downstream action, rather than generate an open-ended response. • Developers retain control over decision criteria by defining the available categories, scoring levels, and conditions used by the application. • Pricing starts at $0.10 per 1 million input tokens, with no additional charge for output tokens, according to OpenAI. • The API supports Zero Data Retention and HIPAA for eligible customers, with data residency and regional processing options available in the United States and Europe. • OpenAI expects the Decisions API to reach general availability in the coming weeks, potentially expanding its role as a decision layer for enterprise applications and AI agents. Read More - https://lnkd.in/ekuWpMJK #OpenAI #ArtificialIntelligence #AI #GenerativeAI #AgenticAI #AIAgents #EnterpriseAI #AIInfrastructure #AIAPI #Konsulteer Sandeep Sharma Harsh Sharma

AI progress is creating new opportunities to turn powerful technology into practical value. I enjoyed reading GeekWire's conversation featuring Ali Farhadi, CVP of AI for our Superintelligence organization: https://msft.it/6046aePi0 His perspective reflects how our teams are thinking about long-term innovation, customer choice, and what it takes to build the next generation of AI experiences.

⚡ JEV vs LAYA — A NEW AI MODEL CATEGORY? The next AI breakthrough may not be another chatbot. Jev and Laya are taking a different approach: AI models designed to make decisions, not generate text. Give them a message, ticket or record + a typed question. Instead of a paragraph, they return: → Choice → Score → Yes/No probability That changes how AI fits into software. 1️⃣ JEV — MANAGED DECISION AI TypeSafe’s Jev is a closed, hosted API designed to work out of the box. It supports long inputs and large choice sets, with published support for up to 64K tokens. Use case: decisions directly inside production workflows. (Jev AI) 2️⃣ LAYA — OPEN WEIGHTS Laya takes the opposite approach. Apache 2.0 open weights → local inference → fine-tuning → control over the infrastructure. Its English model is around 421M parameters. Use case: low-latency, private and highly customised decision systems. (Laya AI) 3️⃣ THE TRADE-OFF Published tests show an interesting split: Jev: stronger performance on complex and large-option decisions. Laya: much lower local latency and self-hosting. But benchmark conditions differ, so production testing remains essential. (Laya Studio) 🎯 SIGNAL OF THE DAY AI does not always need to generate language. For many enterprise workflows, the valuable output is simply: “Approve.” “Reject.” “Route to Team B.” “Risk = 0.82.” That can be dramatically simpler to integrate than parsing an LLM response. 🔭 WEAK SIGNAL Decision models could become the “System 1” layer of enterprise AI. LLMs reason and generate. Small decision models could handle millions of repetitive decisions around them. 🧠 TAKEAWAY The future AI stack may not be: One giant model → everything but: Reasoning model + decision models + tools + data + governance 💡 MY TAKE The interesting part of Jev vs Laya isn’t who wins a benchmark. It’s the architectural shift: AI decisions are becoming an explicit software primitive. That could make AI cheaper, faster and easier to embed into everyday enterprise processes. ❓ QUESTION Would you trust a small specialised AI model to make thousands of operational decisions every day? I track AI, Enterprise Architecture, Agentic AI, Physical AI & Digital Transformation. 🔎 Research, projects & insights: Mathieu Delloye Consulting #AI #Jev #Laya #EnterpriseAI #AgenticAI #AIArchitecture

AI progress is creating new opportunities to turn powerful technology into practical value. I enjoyed reading GeekWire's conversation featuring Ali Farhadi, CVP of AI for our Superintelligence organization: https://msft.it/6049aWlHW His perspective reflects how our teams are thinking about long-term innovation, customer choice, and what it takes to build the next generation of AI experiences.

Context is becoming the real differentiator in enterprise AI. Agents need governed meaning, trust and policy to act correctly. Anthropic's own research found agents scored just 21% accuracy on raw data. With governed context, accuracy rose above 95%. We ran a similar test at KU Leuven. Agents with governed context hit 92% accuracy. Without it, accuracy dropped to 62%. Check out the full post to learn why contextual gravity is becoming the layer enterprise AI standardizes on. https://ow.ly/mHqO50ZPv2s #EnterpriseAI #DataGovernance #Collibra