Using Tech Wisely In Industrial Applications
Artificial intelligence in industrial settings has always operated under a constraint that consumer technology largely avoids: the consequences of being wrong can be catastrophic. A process control system that misbehaves does not simply return a bad search result or suggest an irrelevant product. It can damage expensive equipment, halt production, and—in the worst cases—endanger people and the environment.
This is why determinism sits at the heart of everything we build at IntelliDynamics. Our neural networks, optimization algorithms, and rules-based systems produce the same result for the same inputs, every time, in a manner that engineers can audit, validate, and trust. That is not a limitation of our architecture. It is an intentional design principle rooted in the realities of industrial operations.
The Advent of LLMs
Large language models (LLMs) represent a genuinely new category of artificial intelligence. They are stochastic—meaning their outputs involve probabilistic processes that can vary even for identical inputs. They are generative, producing novel text, reasoning, and structured content rather than classifying or predicting within a fixed output space. And they are remarkably capable communicators, synthesizing complex information into natural, accessible language.
These properties make LLMs extraordinarily useful in many contexts. They also make them fundamentally unsuitable for others.
There are far too many “Sorry. You are right. That’s on me.” moments from LLMs to consider them appropriate for process control or any other vital interaction with machine systems.
Anyone who has worked with LLMs extensively has seen this pattern: a confident answer, followed by a graceful retraction when challenged. In a customer service context, that is acceptable. In a control loop managing a high-pressure reactor, it is not. The candid acknowledgment of error is, in fact, one of the things that makes LLMs useful in conversation—and one of the things that disqualifies them from deterministic responsibilities.
Our Position is Clear
IntelliDynamics will not use stochastic generative LLMs in contexts that require deterministic systems. We will not route safety-critical decisions, process control actions, or vital machine interactions through any system whose outputs cannot be fully validated and reproduced. This is a firm commitment, not a temporary posture while the technology matures.
Deterministic systems—neural networks, physics-informed models, rules engines, and constrained optimization—remain the foundational layer for all process modeling, prediction, optimization and control at IntelliDynamics.
LLMs are deployed exclusively as an enrichment layer above those deterministic systems: improving communication, usability, and knowledge access without touching core control and optimization logic.
Health, safety, and environmental integrity are non-negotiable. No capability gain justifies increasing risk in those areas.
Where LLMs Genuinely Excel in Industrial AI
Acknowledging what LLMs cannot do responsibly should not obscure the substantial value they can deliver. The same properties that make them unsuitable for control—fluency, flexibility, the ability to handle ambiguous natural-language queries—make them remarkably powerful as an interface to industrial knowledge.
Consider what deterministic AI already extracts from process, materials and production data: predictive models of equipment degradation, multi-dimensional optimization surfaces for product functional characteristics, production rates, yield and energy consumption. Quality signatures embedded in sensor streams create causal maps of how upstream variables influence downstream outcomes. This knowledge exists. It is accurate. It is validated. But accessing it has historically required experts who know exactly where to look, which query to run, and which model response surface to interpret.
LLMs change that. When deployed as a natural-language interface over a deterministic knowledge layer, they allow process engineers, operators, and production managers to ask questions in plain language and receive answers grounded in the AI systems’ validated understanding of the process. The stochastic layer handles communication. The deterministic layer handles truth.
The Hybrid Architecture in Practice
This is the architectural strategy that makes the most of both technologies. Deterministic systems do what they have always done: deliver precise, reliable intelligence about complex industrial processes. LLMs do what they do uniquely well: make that intelligence accessible, interactive, and communicable to a much wider range of people within the organization. The result is a system that is more capable than either technology alone—and more responsible than an approach that conflates the two.
Remaining at the Forefront, Responsibly
IntelliDynamics has always pursued the most capable AI technologies available, applying them where they genuinely serve our industrial customers. That tradition continues. We are actively integrating LLMs into our hybrid architectures, and we are seeing meaningful improvements in how quickly engineers can develop process understanding, how effectively operators can act on AI-generated insights, and how readily production management can engage with complex optimization recommendations.
None of that required compromising the deterministic foundation. The opportunity for LLMs is in the enrichment layer—and that is exactly where we are building.
Hybrid AI is not a transitional technology waiting to be replaced by something better. It is the right architecture for industrial environments where reliability and communication are both essential, and where the stakes of getting either one wrong are simply too high to treat as acceptable risk.