Inna Tokarev Sela, the CEO and Founding father of Illumex, is transforming how enterprises prepare their structured data for generative AI. Illumex enables organizations to deploy genAI analytics agents by translating scattered, cryptic data into meaningful, context-rich business language with built-in governance.
The platform robotically analyzes metadata to locate and label structured data without moving or altering it, adding semantic meaning and aligning definitions to make sure clarity and transparency. By creating business terms, suggesting metrics, and identifying potential conflicts, Illumex ensures data governance at the very best standards.
With Illumex, analytics agents can interpret user queries with precision, delivering accurate, context-aware, and hallucination-free responses. Under Inna’s leadership, Illumex is setting a brand new benchmark for AI readiness, helping businesses unlock the complete potential of their data.
What inspired you to found illumex, and the way did your experiences at Sisense and SAP shape your vision for the corporate?
The vision for illumex emerged during my studies, where I imagined information being accessible through mindmap-like associations slightly than traditional databases – enabling direct access to relevant data without extensive human consultation.
My time at SAP taught me the basics of constructing enterprise software and scaling operations. Working across product development with SAP HANA cloud platform and business initiatives just like the startup partnership framework gave me deep insights into enterprise customer needs. It revealed a big gap between how firms approach data practices and what end users really need.
At Sisense, constructing the AI practice from scratch demonstrated the immense value AI could bring to customers. Seeing this impact, combined with the rise of SaaS and GenAI technologies, convinced me the timing was right to launch illumex in 2021.
illumex focuses on Generative Semantic Fabric. Are you able to explain the core concept and what motivated you to tackle this specific challenge in AI and data analytics?
illumex pioneered Generative Semantic Fabric – a platform that automates the creation of human and machine-readable organizational context and reasoning. This platform unifies the experience of each LLM-based generative AI and business applications for technical and non-technical users around shared context.
This single fabric delivers two major advantages: it streamlines data management through the automation of as much as 80% of knowledge engineering tasks and enables non-technical users to access analytics with built-in governance, explainability, and accuracy. Each of those advantages address a multi-billion dollar marketplace for enterprise decision-making.
Consider it as a digital playground where machines, humans, and applications interact spontaneously without pre-programming. This aligns with our vision of an application-free future, where as an alternative of juggling multiple tools like sheets, analytics, financial systems, and customer amanagement, you just express your task, and it’s accomplished seamlessly. Generative Semantic Fabric is the muse for this future.
What were among the key challenges you faced within the early days of illumex, and the way did you overcome them?
In 2021, despite the proven fact that generative AI semantic models have existed since 2017, and graph neural nets have existed for even longer, it was a tricky task to elucidate to VCs why we’d like automated context and reasoning. Even defining it back then was a tricky task.
I might say the most important challenge was to essentially spring this excitement about this future technology and future market. And I used to be very fortunate to fulfill forward-thinking investors who believed in me.
How does illumex empower organizations to develop into AI-ready, and why is that this transition critical in today’s business landscape?
The business world is splitting into two camps: firms that recognize and capitalize on AI as a transformative force akin to the Web and those who miss or delay understanding this chance.
illumex meets organizations wherever they’re of their AI journey. We prepare their data for generative AI implementation, augment and govern organizational logic and context, and enable the deployment of agent analytics and orchestration.
Our full-stack GenAI implementation platform for structured data elevates any company’s landscape to effectively leverage these advanced technologies.
illumex emphasizes “hallucination-free” generative AI responses. How does illumex ensure deterministic and reliable outputs?
illumex builds on pre-existing business ontologies – knowledge graphs capturing industry-specific terminology, workflows, and processes across sectors like pharma, retail, and manufacturing, in addition to business functions like finance, HR, and provide chain.
When onboarding customers, we robotically retrain these ontologies on their metadata. Inside days, firms can search their data, validate results, and discover issues like duplicates or conflicts.
The agentic analytics chatbot provides complete transparency – showing how questions are interpreted and mapped to the client ontology after which to data. This transparency, combined with automated data validation, ensures deterministic, hallucination-free answers. Moreover, governance teams can pre-validate potential responses because the context embeds all possible questions and their permutations upfront.
How does illumex differentiate itself from traditional approaches like Retrieval-Augmented Generation (RAG)?
While RAG attempts to customize off-the-shelf AI models by feeding them organizational data and logic, it faces several limitations. It is a black box – you possibly can’t determine in case you’ve provided enough examples for correct customization or how model updates affect accuracy. It also relies on data scientists who may lack business context, making it difficult to totally capture organizational logic.
Moreover, RAG consumes around 80% of AI infrastructure and tokens only for fine-tuning slightly than actual use, raising ROI concerns. It also lacks built-in governance – there is not any way for compliance teams to validate training adequacy or ensure proper access controls.
illumex’s Generative Semantic Fabric (GSF) addresses these challenges through automated context constructing without consuming external AI tokens. It eliminates the necessity for specialised data scientists and provides complete transparency in mapping and reasoning through web, Slack, or Teams interfaces. GSF includes built-in governance and explainability, clear indicators of organizational coverage and data quality, and automatic quality assessment for question-answering capabilities.
Many businesses struggle with making data-driven decisions despite investing heavily in data infrastructure. Why do you’re thinking that this gap exists, and the way does illumex address it?
The gap between data investment and effective decision-making continues to widen as data volumes explode, each internally and externally. Organizations now face not only their very own growing data but in addition an array of external sources – from weather APIs to industry cloud platforms sharing healthcare data across European institutions, plus synthetic data for various use cases.
The challenge is that organizations still depend on humans for critical data tasks like modeling, quality assessment, and dashboard creation. Yet the dimensions and complexity of recent data environments make it increasingly unimaginable for human teams to effectively classify data, assess its quality, and ensure it’s suitable for AI-driven analytics and automation.
illumex bridges this gap by automating these traditionally manual processes, enabling organizations to effectively manage, validate, and utilize their expanding data landscape for meaningful business decisions.
What industries have been the quickest to adopt illumex’s platform, and what unique challenges or opportunities have you ever observed in these sectors?
We’re seeing the fastest adoption in industries that sit on the intersection of knowledge intensity and heavy regulation, where firms need robust automation of knowledge quality monitoring, usage tracking, and conflict detection. Financial services, pharmaceuticals, and retail/e-commerce are leading the charge, as these sectors aim to rapidly reinvent themselves using their existing data assets while navigating complex regulatory requirements.
With generative AI evolving rapidly, what advice would you give to enterprises seeking to integrate AI effectively and responsibly?
Start by developing a transparent strategic plan that identifies specific use cases and the business imperatives driving AI adoption. It’s crucial to avoid creating recent silos of AI technology that operate in isolation from existing systems.
As an alternative, construct a unified platform that integrates data management, analytics, and generative AI capabilities. Keeping AI initiatives disconnected from established governance practices not only creates significant risks but in addition results in increased costs. The bottom line is to create a shared infrastructure that supports all these functions while maintaining proper oversight.
With AI adoption accelerating, what trends do you see shaping the enterprise AI landscape over the following 3–5 years?
Two major trends are emerging within the AI landscape. First, agentic analytics is gaining momentum, allowing for more sophisticated data evaluation and insights. Second, we’re seeing a shift toward agentic orchestration, which enables workflows based on collaboration between multiple AI models with diverse functionalities.
This orchestration moves us beyond single-purpose applications toward more comprehensive solutions. For instance, in healthcare, as an alternative of isolated applications for specific tasks, take into consideration automation of all the physician office workflows – combining image scanning, prescription processing, and drug recommendations in a single seamless system.
These advancements depend on a strong generative semantic fabric to make sure accurate data access, shared context and coordination between AI agents. This foundation shall be crucial for enabling each agentic analytics and orchestrated AI solutions to achieve their full potential.
