Why LLMs cannot be industrialized without a Cognitive and Context layer
Industrial AI requires cognitive context layers to scale LLMs reliably.
Nov 19, 2025
Success stories
LLM have reached an impressive level of maturity in text generation and conversational interaction. However, when it comes to meeting real industrial requirements: reliability, performance, cost control, and traceability… their limitations quickly become apparent.
The issue is not the power of the models themselves, but the lack of an intermediate layer capable of providing cognition, memory, and contextual understanding. Without such a layer, LLM-based systems consume excessive compute resources, produce responses that are difficult to explain, and struggle to maintain continuity over time.
Athénaïs Oslati - CEO of Ontbo
At Ontbo, we believe the future of industrial AI does not lie in ever-larger models, but in smarter architectures. Rather than replacing existing models, we enhance them by adding a Cognitive AI layer designed specifically for production environments.
This cognitive layer filters, structures, and reasons over context before invoking the model. It dramatically reduces the number of tokens processed, accelerates response times, and improves overall system reliability. By delivering only the most relevant information to the model, it fundamentally reshapes the economic equation of AI.
The benefits are concrete. Compute costs are significantly reduced without sacrificing performance. Responses become faster, more consistent, and more explainable. Persistent memory enables AI agents to maintain continuity, learn from real-world interactions, and adapt over time—making large-scale autonomous agents finally viable.
This approach is built to integrate seamlessly across industrial environments, whether cloud, on-premise, or hybrid. It enables immediate integration into existing systems and ensures controlled scalability without budgetary explosion.
Industrializing AI is not about stacking more models. It is about designing architectures that can reason, remember, and operate within real operational constraints. This is the cognitive layer Ontbo is building—to help enterprises move sustainably from experimentation to production.
Why LLMs cannot be industrialized without a Cognitive and Context layer
Industrial AI requires cognitive context layers to scale LLMs reliably.
Nov 19, 2025
Success stories
LLM have reached an impressive level of maturity in text generation and conversational interaction. However, when it comes to meeting real industrial requirements: reliability, performance, cost control, and traceability… their limitations quickly become apparent.
The issue is not the power of the models themselves, but the lack of an intermediate layer capable of providing cognition, memory, and contextual understanding. Without such a layer, LLM-based systems consume excessive compute resources, produce responses that are difficult to explain, and struggle to maintain continuity over time.
Athénaïs Oslati - CEO of Ontbo
At Ontbo, we believe the future of industrial AI does not lie in ever-larger models, but in smarter architectures. Rather than replacing existing models, we enhance them by adding a Cognitive AI layer designed specifically for production environments.
This cognitive layer filters, structures, and reasons over context before invoking the model. It dramatically reduces the number of tokens processed, accelerates response times, and improves overall system reliability. By delivering only the most relevant information to the model, it fundamentally reshapes the economic equation of AI.
The benefits are concrete. Compute costs are significantly reduced without sacrificing performance. Responses become faster, more consistent, and more explainable. Persistent memory enables AI agents to maintain continuity, learn from real-world interactions, and adapt over time—making large-scale autonomous agents finally viable.
This approach is built to integrate seamlessly across industrial environments, whether cloud, on-premise, or hybrid. It enables immediate integration into existing systems and ensures controlled scalability without budgetary explosion.
Industrializing AI is not about stacking more models. It is about designing architectures that can reason, remember, and operate within real operational constraints. This is the cognitive layer Ontbo is building—to help enterprises move sustainably from experimentation to production.
Why LLMs cannot be industrialized without a Cognitive and Context layer
Industrial AI requires cognitive context layers to scale LLMs reliably.
Nov 19, 2025
Success stories
LLM have reached an impressive level of maturity in text generation and conversational interaction. However, when it comes to meeting real industrial requirements: reliability, performance, cost control, and traceability… their limitations quickly become apparent.
The issue is not the power of the models themselves, but the lack of an intermediate layer capable of providing cognition, memory, and contextual understanding. Without such a layer, LLM-based systems consume excessive compute resources, produce responses that are difficult to explain, and struggle to maintain continuity over time.
Athénaïs Oslati - CEO of Ontbo
At Ontbo, we believe the future of industrial AI does not lie in ever-larger models, but in smarter architectures. Rather than replacing existing models, we enhance them by adding a Cognitive AI layer designed specifically for production environments.
This cognitive layer filters, structures, and reasons over context before invoking the model. It dramatically reduces the number of tokens processed, accelerates response times, and improves overall system reliability. By delivering only the most relevant information to the model, it fundamentally reshapes the economic equation of AI.
The benefits are concrete. Compute costs are significantly reduced without sacrificing performance. Responses become faster, more consistent, and more explainable. Persistent memory enables AI agents to maintain continuity, learn from real-world interactions, and adapt over time—making large-scale autonomous agents finally viable.
This approach is built to integrate seamlessly across industrial environments, whether cloud, on-premise, or hybrid. It enables immediate integration into existing systems and ensures controlled scalability without budgetary explosion.
Industrializing AI is not about stacking more models. It is about designing architectures that can reason, remember, and operate within real operational constraints. This is the cognitive layer Ontbo is building—to help enterprises move sustainably from experimentation to production.