Michael Tambe

WWW.ELIXIRDATA.co and the Value of its built in AI Agents: ElixirData’s built-in AI agents are like having a team of data-savvy analysts that never sleep. Each agent watches one cost-heavy process, spots leaks, and tells you (or the system) what to fix—before the money walks out the door. 1. Spend-Agent – scans every PO, invoice and contract line. If the same SKU is bought at two different prices, or a discount tier isn’t applied, it flags the variance and can auto-send a “claim credit” email to the supplier. Typical catch: 1–3 % of annual addressable spend recovered in 90 days. 2. Energy-Agent – ingests IoT meter data + utility tariffs. When a shift or building is trending above its kWh baseline, it recommends throttle-down schedules or peak-hour avoidance. Plant example: $420 k saved in year-one by shifting two production lines to off-peak. 3. Inventory-Agent – fuses MRP, sales forecast and freight lead-times. If safety-stock is higher than the 95 % service-level target, it proposes a transfer order instead of a new buy, cutting carrying cost. Retail case: freed $6.8 M cash and cut obsolescence write-offs 28 %. 4. Maintenance-Agent – reads sensor vibration, temperature, and past work-orders. Predicts failure 7–10 days out, letting you schedule minor repairs instead of emergency shutdowns. Chemicals client: unplanned downtime ↓ 32 %, saving $1.9 M in lost-batch margin. 5. Compliance-Agent – tracks regulatory changes and cross-checks them against current master data. Catches missing tax codes, expired certs or mis-classified hazardous goods—avoiding fines and rush rework. Bottom line: the agents surface waste you didn’t know existed, quantify the saving, and (with your one-click approval) can trigger the corrective action—turning raw data into real money, 24/7. Interested Parties,leave me a message here in LinkedIn and I will arrange a briefing Session to Present and Demonstrate

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Semantic layers are emerging as core infrastructure for agentic analytics. Why? Because agents need context for accurate analytics, and that context is the semantic layer: it tells agents how business metrics like revenue or MAU should be calculated. Without this, they fumble badly. The big vendor debate is: where should this layer live: in the BI tool (Looker’s LookML, PowerBI’s DAX), in middleware (dbt, cube), or in the database? I strongly believe in “semantic pushdown”: stick metadata and metric expressions in the database alongside table definitions. Make it easy to manage and access via SQL and let it inform optimizations like materializations. After all, what is a database if not a place to store and query data?

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AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful. This is why I increasingly try to select software that lets me control my own data, so I can make it available to my AI agents. Because of AI’s growing capabilities, the value you can now create from “connecting the dots” between different pieces of data is higher than ever. For example, if an email click is logged in one vendor’s system and a subsequent online purchase is logged in a different one, then it is valuable to build agents that can access both of these data sources to see how they correlate to make better decisions. Unfortunately, many SaaS vendors try to create a data silo in their customer’s business. By making it hard for you to extract your data, they create high switching costs. This also allows them to steer you to buy their AI agent services — sometimes at high expense and/or of low quality — rather than build your own or buy from a different vendor. Unfortunately, some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you (and your AI agents) to efficiently access it. One of my teams just told me that a SaaS vendor we have been using to store our customer data wants to charge over $20,000 for an API key to get at our data. This high cost — no doubt intentionally designed to make it hard for customers to get their data out — is adding a barrier to implementing agentic workflows that take advantage of that data. Through AI Aspire (an AI advisory firm), I advise a number of businesses on their AI strategies. When it comes to buying SaaS, I often advise them to try to control their own data (which, sadly, some vendors mightily resist). This way, you can hire a SaaS vendor to record and operate on your data, but ultimately you decide how to route it to the appropriate human or AI system for processing. Over the past decade, a lot of work has gone into organizing businesses’ structured data. Because AI can now process unstructured data much better than before, the value of organizing your unstructured data (including PDF files, which LandingAI’s Agentic Document Extraction specializes in!) is higher than ever before. In the era of generative AI, businesses and individuals have important work ahead to organize their data to be AI-ready. P.S. As an individual, my favorite note-taking app is Obsidian. I am happy to “hire” Obsidian to operate on my notes files. And, all my notes are saved as Markdown files in my file system, and I have built AI agents that read from or write to my Obsidian files. This is a small example of how controlling my own notes data lets me do more with AI agents! [Original text: https://lnkd.in/gYPUvZGT ]

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Just-in-Time UI: At Aligned Labs we frequently share reports about the data collection, fine-tuning and eval work that we do with clients.  They often need different views or refined analysis, which is why we built Whiz-bang Boom! Our new tool takes a CSV or JSON file and generates a shareable, interactive report. You can easily modify the report or focus the analysis with a simple prompt, and it creates a new revision. You can organize reports into spaces and customize those spaces with a simple prompt. Try it out. https://wizbangboom.com #businessIntelligence #AI

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AEO Visibility — Get Seen and Get Cited. Well structure websites make a difference... What is Schema.org, Structured Data, and Why It Matters for AI Search If you want your site to be seen, parsed, and quoted by AI tools like ChatGPT, Gemini, and Perplexity, structured data is no longer optional—it’s essential. Here’s the quick breakdown: 1) What is Schema.org? Think of Schema.org as a shared dictionary of labels for the web—covering pages, people, products, places, and more. ✅ Major search engines and AI systems read it. ✅ You add it via a small JSON block. 2) What is Structured Data? It’s machine-readable info layered on your page. Formats: JSON-LD (industry standard), Microdata, RDFa. It must match what users actually see. 3) Why it matters for AI Search… Structured data gives machines clean facts. It helps them: 🔹 Understand what your page is (Article, Product, Organization, Person). 🔹 Disambiguate names, prices, dates, and brands. 🔹 Link your content to trusted profiles. 🔹 Reduce false facts and increase citations. 4) Key concepts to get right @context → almost always "https://schema.org" @type → defines what the thing is (e.g., Article, Product, Organization) @id → a unique, permanent URL for the entity (often your canonical URL) Entity linking → connect related items (Article ↔ Author ↔ Organization) 5) Pro tips for AI visibility ✔ Use JSON-LD (clean, safe, flexible). ✔ Map schema fields to your CMS fields (avoid mismatched prices/dates). ✔ Link to verified profiles with sameAs. ✔ Validate with Schema Markup Validator + Google Rich Results Test. ✔ Monitor AI citations over time. 👉 Done right, structured data increases your odds of being surfaced correctly in AI search, reducing misquotes and building trust in your content. Want a quick pulse-check? Comment AEO or Message me and I will give you a complementary Answer Engine Optimization Analysis on our soon to be fully released AEO analysis tool. #AEO #AIVisibility #ThoughtfulAI #RevenueExperts

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Everyone talks about Agentic AI. Very few actually build it end-to-end. Most so-called “agents” today are just LLMs with tools bolted on. Real agentic systems are something very different. They require memory, reasoning, orchestration, learning, execution, and governance, all working together as a single system. That’s exactly how JanusAI was designed. Here’s how JanusAI natively implements all 10 pillars of Agentic AI, not as concepts, but as production-ready capabilities: 1. Goal Understanding & Intent Parsing Requests are translated into explicit goal hierarchies, constraints, ambiguity maps, and expected outputs, interpreted through persona-aware context (CFO ≠ SOC analyst). 2. Memory Systems (Short + Long Term) A 7-tier biomimetic memory fabric enables persistent learning, episodic recall, and sub-500ms retrieval, agents don’t reset every session. 3. Reasoning & Planning Multi-paradigm reasoning (graph, Bayesian, RL, quantum optimization) with self-evaluation and re-planning under deterministic orchestration. 4. Tool Use & API Integration Agents take real action across enterprise systems with governed permissions, multi-protocol support, and full auditability. 5. Workflow Orchestration Zeus coordinates long-running, multi-agent workflows with branching, retries, escalation, and human-in-the-loop control. 6. Knowledge Integration Private data, real-time telemetry, and external intelligence are fused into semantic knowledge graphs, enabling reasoning, not just retrieval. 7. Learning & Adaptation A self-aware Brain System continuously learns from outcomes, errors, and feedback, without retraining entire models. 8. Security, Safety & Guardrails Policy-enforced autonomy with RBAC, ABAC, Zero Trust, full audit trails, and regulator-ready governance. 9. Multi-Agent Collaboration Agents operate as teams, delegating, critiquing, negotiating, and coordinating at scale under a native agent-society architecture. 10. Execution & Real-World Action Agents generate reports, update systems, trigger workflows, and take bounded action, logged, reversible, and reliable. Bottom line: Agentic AI isn’t a model. It’s an operating system for intelligence. JanusAI delivers persistent memory, adaptive reasoning, collaborative agents, deterministic execution, and enterprise-grade governance, by design. This is why JanusAI isn’t a chatbot, a framework, or a demo. It’s a cognitive operating system for the enterprise. #AgenticAI #EnterpriseAI #CognitiveArchitecture #MultiAgentSystems #AIPlatforms #JanusAI

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Would be interesting to see how this shapes up and speeds up data engineering et al. Governance will be mich more important! #databricks #AI #datamanagement

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🌟 **Must Reads: Beyond the Hype - Palantir's Real Long-Term Strength** 🌟 In the ever-evolving landscape of technology and data analytics, Palantir Technologies stands out as a key player, often surrounded by both excitement and skepticism. As someone who is deeply invested in smart investing, I believe it’s vital to look beyond the headlines and understand the core strengths that truly drive a company's long-term success. Palantir's robust data integration capabilities and its focus on AI-driven solutions position it uniquely in the market. However, it’s essential to dissect the hype and focus on the underlying fundamentals. My recent exploration into Palantir’s strategic initiatives reveals a company that is not just riding a wave of trendiness but is laying a solid foundation for sustainable growth. I urge fellow investors and professionals to take the time to read the latest newsletter. It dives deep into Palantir’s business model, the value it brings to its clients, and how it can potentially reshape industries. This is a must-read for anyone looking to make informed decisions in today’s fast-paced investment environment. 🔗 Read more here: https://lnkd.in/exGdJkPZ Let's engage in a thoughtful discussion about the potentials and challenges that come with investing in innovative tech companies like Palantir. Your insights could be invaluable to others in our community. #Palantir #InvestmentStrategy #SmartInvesting #DataAnalytics #TechTrends #Finance

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