When I interviewed Inna Tokarev Sela, the CEO of Illumex, I wanted to understand what "Agentic AI" actually looks like in the modern enterprise. What I got was a much broader takeaway—one that speaks to the risks, tradeoffs, and opportunities of using AI inside complex organizations.
She said: "Your AI should only be using your data."
Simple. But it rewires how you think about trust, hallucination, and infrastructure.
Trust Isn’t a Feature. It’s a Foundation.
Inna’s point is deceptively simple—and quietly radical. Most enterprise AI deployments rely on large language models trained on generalized, publicly available data. Then, they attempt to "fine-tune" these systems to operate within company contexts. But those generalized systems are prone to error. Especially when context is lacking.
Take Air Canada. In February 2024, the airline was forced to honor a refund policy invented by its own chatbot. The AI offered bereavement discounts that didn’t exist—an offer the company later tried to disown. In court, they argued the chatbot was a separate legal entity. The judge disagreed, and Air Canada lost the case.
These hallucinations happen when AI lacks internal grounding. It doesn't understand the rules, the workflows, or the responsibilities tied to its responses. Illumex’s response to that: build AI that is constrained by design. Grounded in the data that already defines your business.
Hallucinations Happen When Context Fails
Illumex calls this approach “semantic context.” It doesn’t rely on more training data—it relies on smarter pipelines. Their system integrates directly with company metadata, roles, permissions, and common data access patterns. It tailors responses based on who is asking and what they’re allowed to see.
“We reduce the degrees of freedom the AI has,” Inna explained. “The model knows what department you work in, which tools you use, what data you normally access. It’s not free to improvise.”
This trust-by-design architecture reframes the goal of enterprise AI. Instead of pushing for chatbots with personality or unlimited flexibility, Illumex emphasizes safety, traceability, and transparency.
From Black Box to Glass Box
The company’s recent Microsoft Teams integration is a case in point. Rather than forcing users into new interfaces, Illumex brings AI into the conversation tools employees already use. It’s less about novelty, and more about trust—meeting people where they work.
“About 80% of our enterprise customers already spend their day in Teams,” said Inna. “So instead of adding another interface to learn, we brought the AI to them.”
Responses are explainable and auditable. Users can ask a question, see how the model arrived at its answer, and verify the source trail—all without leaving the flow of conversation.
Context Is the New Moat
The real innovation Illumex is championing isn’t bigger models—it’s smarter systems. They’re betting on a future where the most trusted AI doesn’t just talk—it understands.
“Every organization should own their context,” Inna told me. “Right now, they don’t. They’re outsourcing it to black-box copilots.”
That might be the most important idea of all. Trust isn’t something you add on top. It’s something you build from the inside out.
And increasingly, it’s not the model that makes the difference. It’s the context that surrounds it.
🎙️ Podcast Transparency: The voices in this episode are AI-generated using Google DeepMind NotebookLM. This podcast was created from the original interview transcript with Inna Tokarev Sela, this article, the Illumex website, and the Forbes article on the Air Canada chatbot case.
📚 Vocabulary Key
Hallucination (AI) When an AI system generates false or made-up information. In enterprise AI, hallucinations are considered serious risks.
Semantic Layer An intermediate structure that defines meaning and relationships across data sources, making it easier for AI to interpret and respond with context.
Contextual Grounding The process of anchoring AI outputs in the real-time facts, structure, and metadata of an enterprise—so responses are relevant and accurate.
Agentic AI A type of AI designed to take purposeful action based on awareness of the user’s goals, environment, and constraints.
Black Box vs. Glass Box “Black box” models offer answers without transparency. A “glass box” approach shows how answers are generated, increasing trust and auditability.
🤖 FAQs: Agentic AI, Trust, and Illumex
What is Agentic AI in an enterprise setting? Agentic AI refers to systems that can make informed decisions and take action within a defined enterprise context—without needing constant human prompting. Think of it as a co-worker that knows your team, tools, and company policies.
Why are hallucinations such a big deal in enterprise AI? In creative applications, a little imagination is charming. But in enterprise settings—where accuracy matters—hallucinations can result in bad recommendations, legal risks, or costly errors (like refunding airline tickets that were never part of company policy).
What makes Illumex different from traditional AI copilots? Illumex doesn’t just layer a chatbot on top of existing data. It builds a semantic layer tailored to your business context—who you are, what department you work in, what tools you use—so the AI stays grounded.
Why integrate with Microsoft Teams? Most enterprise conversations already happen in Teams. By embedding directly into that workflow, Illumex meets users where they are—and builds trust by giving them relevant, explainable answers in real time.
Can this replace my existing LLM deployment? Illumex is model-agnostic. It’s not trying to be the model—it’s the intelligence layer that makes the model safe, explainable, and enterprise-ready.
#AgenticAI #EnterpriseAI #HallucinationFree #TrustworthyAI #AIContextMatters #SemanticLayer #MicrosoftTeams #Illumex #AIInfrastructure #FutureOfWork
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