illumex

Ah, Generative AI—what can’t it do? It reads, interprets, transforms, and uses data to make decisions… and sometimes, it even hallucinates. Without proper data governance, this can very quickly turn into madness. Think compliance nightmares, misleading insights, incorrect decisions, and lost user trust. Here's why: → Traditional audits work in cycles. By the time your audit is done, the data—and the risks—have already changed. You need continuous monitoring to make sure you're on top of things. → Your GenAI model is only as good as the data it uses. Fragmented, siloed systems and inconsistent, meaningless data? That's a recipe for your model to miss critical business context. Leading to incomplete or misleading insights. → GenAI is basically a black box. But if you can’t explain how your model reached its decision, regulators (and your stakeholders) won’t trust it. And neither should you. To use and scale GenAI responsibly, you need augmented governance that’s adaptive and explainable. Here's how: https://lnkd.in/dr9XJ52B

GenAI is powerful, but let’s be real: it can only deliver when it actually understands your data and your business. Want the secret sauce behind GenAI that works for your organization and does it well? Meet Generative Semantic Fabric (GSF). It automates context and reasoning, weaving meaning (semantic vector embeddings) and relationships (knowledge graphs) into every response. In other words? No more guesswork. No more black-box mystery. You get accurate, governed answers your teams can trust. GSF comes pre-trained on industry-specific ontologies (and not generic internet noise). So it knows your field inside out. But that's just the beginning! It then automatically retrains on your metadata, creating a custom framework unique to your business. Perfectly aligning with your unique workflows, rules, and goals. With GSF you: ? Scale with ease ? Eliminate manual data prep ? Slash token costs by up to 80%. Whether it’s handling massive data sources or delivering traceable, explainable answers, GSF has got you covered. It turns your enterprise GenAI into a decision-making partner that speaks your business language fluently. Finally, GenAI you can trust to work for you. Get the full story here: https://lnkd.in/ebVfY2Gv

Your business decisions aren’t based on bad data… right? Actually, they might be. And that’s a little terrifying. Gut instinct can only get you so far. But structured data riddled with inconsistencies, duplicates, and missing context? That’s a recipe for poor decisions you don’t even know you’re making. Especially if you combine that with GenAI. You don’t need better instincts (or better prompts). You need better answers. The kind that only come from properly mapped, semantically aligned, and meaningful data. If that strikes a chord, it’s time to dig deeper into the power of structured data and GenAI: https://lnkd.in/dyS3yuQh

If you've ever wondered how illumex came to be, Inna Tokarev Sela recently shared our journey from a startup to raising $13 million to creating our own new category in enterprise AI. Inna describes illumex as: “This playground where humans can interact with data, machines can interact with data, and machines can interact with machines.” Our vision? An application-free future where business users can access any data through natural conversation. Tune in for the full story: https://lnkd.in/dTqMKeDT

Ever feel like you’re drowning in hidden costs just to keep your AI initiatives moving? If you’re working with LLMs, you know it’s not just the price tag on the model. It’s the endless cycle of data prep, constant fine-tuning, security risks, and manual oversight (to name a few). Each new “adjustment” feels like pouring more resources into an already unpredictable system, with costs adding up faster than anyone anticipated. It’s frustrating to keep re-justifying the budget for something that should be delivering clear results. What if you could make much of this process simpler and faster? What if your data was automatically AI-ready from day one, and governance was baked-in right from the start? What if you could ensure your LLMs performed reliably without the guesswork? For those of us managing complex LLM deployments and implementation projects, there’s a smart way to get the results we’re after—without the endless trade-offs. Here’s how: https://lnkd.in/deJ44TjY

"Let’s bring the human into GenAI implementation." ?? Inna Tokarev Sela shared this insight on the #TrueDataOps podcast with Kent Graziano. GenAI might seem like it’s ready to solve it all, but without human context, it falls short. Here’s why: ? Semantics are the Missing Link for GenAI GenAI can’t understand your business if it doesn’t speak its language. Adding human-driven semantics to your data is like giving GenAI a fluent translator. Without it, GenAI just hears gibberish. And that's hardly the foundation for smart decisions. ? Governance That Makes Sense AI governance is not so much about who gets access to the data, as it is about creating a system where everyone can actually use AI without stressing over bad insights or messy accountability. When GenAI “gets” the meaning behind the data, it stops making random guesses and starts delivering trustworthy insights your business actually needs. ? Human Context Drives Meaning Your GenAI model can be powerful, but without human context, it still misses the mark to provide insights that are both accurate and meaningful. When AI understands the “why,” it aligns with your company goals, creating trust and clarity for decision-makers. ?? Listen to the full episode here: https://lnkd.in/ePJX8gxv

POV: GenAI trying to "learn" from data with zero context (good luck, buddy). Let’s face it: making GenAI work for your business is tricky. Most GenAI projects fail not because of the tech but because the structured data behind it is fragmented, misaligned, and not meaningful. GenAI, for all its brilliance, can’t work miracles with tangled data silos and missing context. So what's your ticket out of this mess? Aligning data across systems, breaking silos, and adding proper semantics and business context can take your data from “meh” to “magic.” If that sounds like too much work, don't worry. It’s not about starting over or drowning in manual processes. There's a way to automatically get your structured data AI-ready—without the uphill battles. Ready to learn more? See how meaningful structured data transforms GenAI from guesswork to trustworthy insights: https://lnkd.in/dFHrCKtV

?? What’s the real cost of DataOps for GenAI? Inna Tokarev Sela and Kent Graziano break it down on DataOps.live—and why, for data leaders, skipping DataOps is like skipping leg day. Here’s what you need to know: ? DataOps: Non-negotiable Think cutting DataOps saves you? Nope! Without it, AI projects topple under inconsistent metrics, governance gaps, and unreliable data. Inna spells it out: the cost of doing it right is nothing compared to fixing the chaos without it. ? Semantic data: GenAI’s GPS GenAI can’t read your mind (yet). So, no semantics? No direction. To work well, AI needs semantically meaningful, context-rich data. Without it your GenAI ends up winging it—fun, until it gets everything wrong. ? Governance: the AI Lifesaver Governance and well-structured ontologies keep GenAI outputs in line with your company's standards. It's a must-have foundation for trustworthy decisions. ? AI evolves jobs, doesn’t take them Let’s get one thing straight—AI isn’t here to take over; it’s here to handle the mundane, tedious stuff so you can focus on big wins. But you might need to add new skills in validating outputs, data integrity, and keeping automation in check. Ready to up your GenAI game? ?? Catch the full episode here: https://lnkd.in/ePJX8gxv

Picture this: you're moving 115,000 tables and 1.5 million columns of data to the cloud, all labeled in cryptic code with no clear context or meaning. And the people who’d initially worked on the system? They had moved on. So there's no one to ask about the business context either. Kind of like playing 3D chess with your data—each move has layers and ripples you can't always see. That’s the challenge a major retail conglomerate faced when migrating from SAP ECC to S/4 HANA. Then, they brought in illumex—and turned randomness into clarity. Here’s what the company achieved: ? Rapid Data Gap Resolution: Two years of work condensed into one week. ? Simplified Data Landscape: Focused on critical assets, cutting unnecessary costs. ? Better Stakeholder Engagement: Complex data is now accessible to all. ? Efficient Data Management: Faster access to quality data for smarter decisions.

Your AI may be looking into the future, but your data habits could be stuck in the past. Avoid these 7 sins from holding your organization back: ?? Pride: Assuming your data is perfect without ongoing reconciliation and validation (Spoiler: it's not.) ??? Gluttony: Hoarding endless data but restricting access to data experts only. ? Wrath: Letting chaos reign by skipping data and AI governance altogether—because who wants another failed manual documentation project? ?? Lust: Chasing shiny new GenAI models without cleaning up your data and governance game first. ?? Greed: Keeping data locked in silos across platforms and departments—why share when you can keep it all stacked away? ?? Envy: Blindly copying other organizations’ data and AI strategies, instead of building one tailored to your unique needs. ?? Sloth: Failing to update your documentation and governance policies because “if it ain't broke..." Want to turn your data into your most powerful asset? Focus on these key pillars: ? AI-Ready Data: Automated semantic mapping, reconciliation, and labeling break down silos, making your data accurate, accessible, and enriched with meaningful business context. ? Augmented Governance: Automated documentation of terms, metrics, relationships, and sensitive data tagging keeps your data aligned and transparent—preventing chaos and keeping everything in check. ? Reliable GenAI Deployment: Automated context and reasoning create the foundation for GenAI that delivers trustworthy, hallucination-free insights—helping your team make smarter decisions. P.S. Want to learn more? Check out the link in the comments.