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24. June 2025
Aerospace Giants Take Off With Revolutionary Generative Ai System
Articul8, a spinout of Intel, has unveiled a cutting-edge generative AI system designed to tackle the complex challenges faced by the aerospace industry. The platform combines three specialized AI agents – a supplier agent, a modeling agent, and a process agent – to solve production challenges in real-time.
The system’s primary objective is to address issues related to module interoperability when assembling aircraft, where parts from different suppliers are typically used to create a final product. Inconsistencies in the design of these parts pose a costly and time-consuming problem for the industry. To combat this, Articul8’s platform identifies potential design issues and develops solutions using its three specialized AI agents.
The supplier agent ensures that parts meet requirements, while the modeling agent interacts directly with 3D modeling environments to check the geometry of parts. The process agent, which also flags any anomalies, recommends potential solutions. This collaborative approach enables the system to autonomously diagnose complex engineering assembly errors, as demonstrated by Articul8 in a company demo.
“We’re not just showing a point solution – we’re showing the power of connecting traditional engineering and operational silos,” said Arun Subramaniyan, CEO of Articul8. The platform connects design, engineering, and supply chain to show how GenAI can actively unlock value with precision in one of the world’s most complex industries.
Subramaniyan emphasized that domain-specificity is where AI needs to go, rather than relying on general-purpose models. Articul8 is building intelligent domain-specific agents that don’t just generate outputs but make decisions, take action, and create impact at every level of engineering.
Articul8 spun out of Intel early last year and serves customers in aerospace, energy, telecommunications, and semiconductors. The company’s technology emerged from work at Intel, where the team built and deployed multimodal AI models for clients, including Boston Consulting Group, before ChatGPT had even launched.
The company’s approach challenges the assumption that general-purpose models with retrieval-augmented generation (RAG) will suffice for all use cases in manufacturing and industrial contexts. Articul8’s domain-specific models achieve 92% accuracy on industrial workflows, outperforming general-purpose AI models on complex sequential reasoning tasks.
Articul8 started as an internal development team inside Intel and was spun out as an independent business in 2024. The technology emerged from work at Intel, where the team built and deployed multimodal AI models for clients. The company was built on a core philosophy that runs counter to much of the current market approach to enterprise AI.
Manufacturing and industrial supply chains present unique AI challenges that general-purpose models struggle to handle effectively. These environments involve complex multi-step processes where the sequence, branching logic, and interdependencies between steps are mission-critical.
“In the world of supply chain, the core underlying principle is everything is a bunch of steps,” Subramaniyan explained. Everything is a bunch of related steps, and the steps sometimes have connections and they sometimes have recursions.
For example, say a user is trying to assemble a jet engine, there are often multiple manuals. Each of the manuals has at least a few hundred, if not a few thousand, steps that need to be followed in sequence. These documents aren’t just static information – they’re effectively time series data representing sequential processes that must be precisely followed.
Subramaniyan argued that general AI models, even when augmented with retrieval techniques, often fail to grasp these temporal relationships. This type of complex reasoning – tracing backwards through a procedure to identify where an error occurred – represents a fundamental challenge that general models haven’t been built to handle.
At the heart of Articul8’s technology is ModelMesh, which goes beyond typical model orchestration frameworks to create what the company describes as “an agent of agents” for industrial applications. ModelMesh combines Bayesian systems with specialized language models to dynamically determine whether outputs are correct, what actions to take next, and how to maintain consistency across complex industrial processes.
Unlike existing frameworks like LangChain or LlamaIndex that provide predefined workflows, ModelMesh enables Articul8 to describe industrial-grade agentic AI – systems that can not only reason about industrial processes but actively drive them.
While many enterprise AI implementations rely on retrieval-augmented generation (RAG) to connect general models to corporate data, Articul8 takes a different approach to building domain expertise. The company starts with Llama 3.2 as a foundation and transforms it through a sophisticated multi-stage process. This multi-layered approach allows their models to develop a much richer understanding of industrial processes than simply retrieving relevant chunks of data.
The SupplyChain models undergo multiple stages of refinement designed specifically for industrial contexts. For well-defined tasks, they use supervised fine-tuning. For more complex scenarios requiring expert knowledge, they implement feedback loops where domain experts evaluate responses and provide corrections.
Articul8 already claims a number of customers and partners, including iBase-t, Itochu Techno-Solutions Corporation, Accenture, and Intel. The company is deploying its platform to build an intelligent, natural language-based system that helps engineers and technicians diagnose and resolve equipment downtime events in Intel’s fabs.
Intel is using Articul8’s platform to create a Manufacturing Incident Assistant – an intelligent, natural language-based system that helps teams perform root cause analysis (RCA), recommends corrective actions, and automates parts of work order generation. The platform ingests both historical and real-time manufacturing data, including structured logs, unstructured wiki articles, and internal knowledge repositories.
As AI moves from experimentation to production in industrial environments, Articul8’s specialized approach may provide faster ROI for specific high-value use cases while general models continue to serve broader, less specialized needs. The performance gap between specialized and general models suggests that technical decision-makers should consider domain-specific approaches for mission-critical applications where precision is paramount.
Articul8’s generative AI system represents a significant breakthrough in the aerospace industry, demonstrating the power of connecting traditional engineering and operational silos to unlock value with precision. By taking a domain-specific approach to building intelligent agents that can actively reason, collaborate, and solve engineering problems across the entire aerospace life cycle, Articul8 is poised to revolutionize the way industries tackle complex challenges.
The platform’s technology has been developed by combining multiple AI models and applying them in industrial settings to achieve high accuracy on sequential reasoning tasks. The company aims to provide a more effective solution for manufacturers looking to implement intelligent systems that can actively reason and drive processes across various sectors.
Articul8 is making significant strides in the development of AI solutions tailored specifically to complex industries such as aerospace, energy, and manufacturing. By leveraging domain-specific expertise and combining it with cutting-edge technologies like ModelMesh, Articul8 is creating a new standard for intelligent systems that can drive real-world impact across these sectors.
With its comprehensive approach to building intelligent agents that tackle the complexities of industrial processes, Articul8 is well-positioned to address some of the biggest challenges faced by manufacturers in today’s rapidly evolving AI landscape. By focusing on precision and effectiveness in specific high-value use cases, the company can accelerate adoption of AI technology across various industries and unlock significant value for its customers.