Enterprise adoption of large language models (LLMs) is growing fast. But for most organizations, the challenges of privacy, scalability, and control remain unresolved.
Dr. Hussein Al-Natsheh, CTO, Unstructured Data & GenAI at Beyond Limits, leads a team that has spent years preparing for this moment—long before generative AI became a trend. Drawing from Beyond Limits experience rooted in NASA’s Jet Propulsion Laboratory and Caltech, the team built a secure, no-code LLM platform now deployed across major energy enterprises.
This platform is not just theory. It’s already running at scale for one of the largest national oil companies in the Middle East, supporting thousands of users and complex, high-risk decisions across secure, sovereign infrastructure.
This interview with Hussein explores how Beyond Limits built a secure, scalable LLM platform tailored for enterprise enabling private deployments, distributed cloud agnostic computing unstructured data curation and LLM fine-tuning, and real-time Agentic Retrieval-Augmented Generation (RAG), providing full control over sensitive data, moving AI from proof of concept to secured production-ready operational reality.
Many enterprises want to leverage large language models (LLMs) but hesitate for one big reason: data privacy. They need full control.
Using public models like ChatGPT means risking that private data could leak or be used to train models accessible to others. This is unacceptable for companies handling critical infrastructure.
Beyond Limits tackled this by developing a private, cloud-agnostic platform. Companies can deploy their own LLMs, tailored to internal needs, while keeping data secure.
Beyond Limits began building this kind of solution before LLMs hit mainstream attention. While most of the world discovered LLMs in late 2022, Beyond Limits had already invested years into developing enterprise-ready, secure AI infrastructure. The team anticipated the market’s need for private, controllable AI systems, and this early focus provided a clear head start in high-stakes industries like energy.
This foresight draws from deep technical roots. Beyond Limits evolved from technology developed at NASA’s Jet Propulsion Laboratory and Caltech. What began as AI for space missions is now driving critical decision-making on an industrial scale. The platform’s strength lies in handling complexity, risk, and scale—requirements that mirror the challenges of industrial sectors like energy.
The platform is built to help enterprises deploy and manage their own large language models securely and at scale—without relying on public cloud services or external APIs.
It supports multiple LLMs and is fully cloud-agnostic, meaning it can run on local servers, private cloud environments, or hybrid infrastructure. This flexibility gives organizations full control over where and how their models operate.
It’s designed to work with the types of data that enterprises use every day—structured and unstructured. That includes documents like PDFs, Word files, PowerPoints, Excel sheets, meeting transcripts, project reports, and more. According to Gartner over 80% of enterprise data is unstructured, , the platform focuses on enabling LLMs to understand and retrieve insights from these formats.
There’s no need for in-house data scientists or AI engineers. The platform comes with a no-code interface that allows subject matter experts to build and configure their own workflows. Users can set up LLMs, connect their data, and define how results are generated—all without writing a single line of code.
The experience of administrating the system is intentionally simple. as simple as managing your personal email: Users select options from a menu, configure parameters as needed, manage who can access what, and get instant access to intelligent outputs.
Enterprise-grade permission controls are built in. Access to content can be restricted based on user roles, departments, or document types—ensuring sensitive information stays protected across teams and regions.
The result is a secure, scalable, and easy-to-use system that puts the power of LLMs directly in the hands of business users.
One of the biggest barriers to AI adoption inside large organizations isn’t the technology, it’s the talent gap.
Most enterprises don’t have teams of machine learning engineers or AI researchers sitting idle, ready to implement complex models. Even if they do, those teams are often overstretched, focused on critical initiatives, and unable to support the day-to-day needs of business units. That’s where no-code becomes a game-changer.
The platform is designed so that subject matter experts—people who know the business, not the backend—can directly configure, manage, and deploy AI use cases. They don’t need to understand the architecture of a large language model or write a single line of code. They just need to know what they want the system to do.
With pre-configured settings, guided workflows, and simple dropdown logic, users can set up ingestion pipelines, define access rules, compare outputs, and even test different LLMs side-by-side. This self-service model speeds up implementation, reduces costs, and keeps projects moving without bottlenecks.
It also means AI becomes embedded into real processes. Not as a siloed project, but as a functional tool that can be used by teams across operations, engineering, legal, or HR.
No-code removes friction. It puts AI in the hands of the people who need it—without waiting months for IT or external vendors. When enterprises can move that fast, the ROI follows.
Most LLMs can only respond based on the data they were trained on. They don’t know if your org chart changed last week or that you issued a new policy yesterday. This is where Retrieval-Augmented Generation (RAG) comes in.
RAG allows LLMs to access fresh, enterprise-specific data before generating responses. This keeps output accurate and up to date. Each generated answer is grounded with the references from the company private documents. This helps users to get trust on the AI generated answers checking the sources. These sources might be several and distributed across various sources from different knowledge repositories. What makes it more enterprise-grade secure, is that the answers are personalized based on the user document access rights.
A key use case is updating job roles or organizational changes. With RAG, the model retrieves real-time information and uses it to answer user questions. Without it, users get stale or irrelevant answers.
The platform configures Agentic RAG pipelines through the same no-code interface. It’s usable by business analysts or operations leads—no engineering required.
Running Gen AI at scale on local infrastructure isn’t easy.
Our platform for example, now supports over 8000 users at one of the largest energy companies, at its organization-wide pre-launch phase. It runs on distributed infrastructure using NVIDIA’s DGX SuperPOD systems as an in-country enterprise-private cloud infrastructure.
This proves it can scale—and growth continues.
This deployment is among the most advanced implementations of a private LLM and agentic RAG-powered AI assistant on scale. The platform allows the organization to fine-tune its own models, maintain full control of its data, and operate in a completely sovereign infrastructure environment.
The impact has been substantial:
This deployment is a blueprint for sovereign AI in the energy sector. It shows what’s possible when security, scale, and usability come together.
Beyond RAG, the future lies in Agentic AI. The goal is to move beyond search and retrieval—to reasoning and decision-making.
Within Beyond AI Platform, the AI design studio is being developed where subject matter experts can draw workflows with no code—just configured logic and design parameters to better fit the use case. These agent-based systems don’t just retrieve data. They compare documents, make recommendations, and trigger actions. Imagine comparing two vendor proposals for procurement—automatically.
Our AI platform functions like a team of collaborative workers: multiple agents working together, each with a role, aligned to a common business objective. Think of it as a virtual workforce automation. Each agent handles a task, and a coordinator ensures alignment.
The platform will support these agents through a no-code interface, powered by hybrid AI. This combines symbolic reasoning (based on rules) with LLMs (based on data). The result: more transparent decisions and better explainability.
Agentic AI sets the stage for autonomous operations. But it’s not just about full automation. Sometimes, the AI recommends actions. Other times, it acts alone.
Enterprises can choose. Workflows can be set to autonomous or semi-autonomous modes.
For example, an agent might notice a market change overnight and suggest action by morning. Or it could flag anomalies in sensor data before a human even sees them. This is only possible because the system retains a full audit trail. Every decision has an explanation.
That’s essential in sectors like energy where trust, compliance, and risk management are non-negotiable.
Plenty of companies claim to offer enterprise AI. But very few have the foundation that Beyond Limits brings to the table.
The company’s roots trace back to NASA’s Jet Propulsion Laboratory and Caltech—two institutions known for solving problems under extreme complexity, with zero room for error. The same core technologies once used in space missions have been adapted and extended to serve industries where precision, reliability, and trust are non-negotiable.
For over a decade, the team has been building and refining symbolic and hybrid AI systems. Unlike purely statistical models, hybrid AI combines data-driven learning with structured, rule-based reasoning. This approach delivers explainable, auditable outcomes—essential for sectors like energy, where high-value assets and regulatory oversight make blind automation a non-starter.
But the foundation isn’t just technical—it’s collaborative.
Rather than delivering a fixed product and walking away, Beyond Limits works closely with clients to co-develop AI systems around their actual workflows, policies, and goals. The result is not an off-the-shelf solution, but a deeply integrated platform tailored to how each organization thinks, operates, and decides.
This model has delivered measurable results. In the energy sector alone, companies using the platform have reported significant savings through more accurate forecasting, operational optimization, faster decision-making with full audit trails, and increased efficiency in daily operations.
For clients managing national infrastructure, critical operations, or large-scale industrial systems, the choice is clear. They need AI they can trust—and a partner who understands the stakes. That’s why they choose Beyond Limits.
The real leap is happening in autonomous multi-agent systems. Workflows are emerging with thousands of agents, not just ten. The challenge is coordination. Agents must stay aligned—to each other and to human expectations.
Alignment across goals, ethics, and company policies becomes harder as you scale.
Combining symbolic AI with reinforcement learning will help. The goal is to create intelligent teams of agents that can learn, reason, and explain themselves—all while being cost-efficient.
The long-term goal is enterprise systems that think, learn, and act on their own—or work with human teams to accelerate results.
Industrial Enterprises aren’t looking for experiments. They’re looking for solutions that deliver real outcomes—securely, reliably, and at scale. They need AI that fits into their operations, protects their data, and produces answers they can trust.
Our offering combines private LLM deployment, agentic Retrieval-Augmented Generation (RAG), No-code Agentic AI platform, and full data control in a secure cloud-agnostic environment. It enables subject matter experts—not just engineers—to extract value from vast, unstructured data without compromising on governance or performance.
What sets this solution apart is its readiness. This isn’t a lab prototype or a future roadmap. It’s a proven fully deployed platform, already supporting thousands of users inside one of the largest energy companies. It’s been tested against real business problems, in production environments, with measurable results.
The foundation is strong. The demand is growing. And the capability is proven.
For enterprises ready to move beyond experimentation and start operationalizing LLMs and Agentic AI with confidence—this is already happening. It’s not the future of AI. It’s the present.
Follow Dr. Hussein Al-Natsheh on Linkedin for further insights.
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