Articul8 Launches A8-SupplyChain: Domain-Specific AI Models for Manufacturing and Supply Chain Operations

Beyond RAG: How Articul8's supply chain models achieve 92% accuracy where general AI fails

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In the race to implement AI across business operations, many enterprises are discovering that general-purpose models often struggle with specialized industrial tasks that require deep domain knowledge and sequential reasoning. While fine-tuning and Retrieval Augmented Generation (RAG) can help, that's often not enough for complex use cases like supply chain. It's a challenge that startup Articul8 is looking to solve. Today, the company debuted a series of domain-specific AI models for manufacturing supply chains called A8-SupplyChain. The new models are accompanied by Articul8's ModelMesh, which is an agentic AI powered dynamic orchestration layer that makes real-time decisions about which AI models to use for specific tasks. Articul8 claims that its 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, including Boston Consulting Group, before ChatGPT had even launched. The company was built on a core philosophy that runs counter to much of the current market approach to enterprise AI. "We are built on the core belief that no single model is going to get you to enterprise outcomes, you really need a combination of models," Arun Subramaniyan, CEO and founder of Articul8 told VentureBeat in an exclusive interview. "You need domain-specific models to actually go after complex use cases in regulated industries such as aerospace, defense, manufacturing, semiconductors or supply chain." The supply chain AI challenge: When sequence and context determine success or failure 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 simply haven't been built to handle. ModelMesh: A dynamic intelligence layer, not just another orchestrator 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 is actually an intelligence layer that connects and continues to decide and rate things as they go past like one step at a time," Subramaniyan explained. "It's something that we had to build completely from scratch, because none of the tools out there actually come anywhere close to doing what we have to do, which is making hundreds, sometimes even thousands, of decisions at runtime." Unlike existing frameworks like LangChain or LlamaIndex that provide predefined workflows, 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. This architecture enables what Articul8 describes as industrial-grade agentic AI -- systems that can not only reason about industrial processes but actively drive them. Beyond RAG: A ground-up approach to industrial intelligence 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. "We actually take the underlying data and break them down into their constituent elements," Subramaniyan explained. "We break down a PDF into text, images and tables. If it's audio or video, we break that down into its underlying constituent elements, and then we describe those elements using a combination of different models." The company starts with Llama 3.2 as a foundation, chosen primarily for its permissive licensing, but then 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. How enterprises are using Articul8 While it's still early for the new models, the company already claims a number of customers and partners including iBase-t, Itochu Techno-Solutions Corporation, Accenture and Intel. Like many organizations, Intel started its gen AI journey by evaluating general-purpose models to explore how they could support design and manufacturing operations. "While these models are impressive in open-ended tasks, we quickly discovered their limitations when applied to our highly specialized semiconductor environment," Srinivas Lingam, corporate vice president and general manager of the network, edge and AI Group at Intel, told VentureBeat. "They struggled with interpreting semiconductor-specific terminology, understanding context from equipment logs, or reasoning through complex, multi-variable downtime scenarios." Intel is deploying Articul8's platform to build what Lingam called - Manufacturing Incident Assistant - an intelligent, natural language-based system that helps engineers and technicians diagnose and resolve equipment downtime events in Intel's fabs. He explained that the platform and domain-specific models ingest both historical and real-time manufacturing data, including structured logs, unstructured wiki articles and internal knowledge repositories. It helps Intel's teams perform root cause analysis (RCA), recommends corrective actions and even automates parts of work order generation. What this means for enterprise AI strategy Articul8's approach challenges the assumption that general-purpose models with RAG will suffice for all use cases for enterprises implementing AI in manufacturing and industrial contexts. The performance gap between specialized and general models suggests technical decision-makers should consider domain-specific approaches for mission-critical applications where precision is paramount. As AI moves from experimentation to production in industrial environments, this specialized approach may provide faster ROI for specific high-value use cases while general models continue to serve broader, less specialized needs.

Articul8 Launches A8-SupplyChain: The First Domain-Specific GenAI Family of Models Built for Manufacturing Supply Chain Operations

Enter your email to get Benzinga's ultimate morning update: The PreMarket Activity Newsletter SANTA CLARA, Calif., April 04, 2025 (GLOBE NEWSWIRE) -- Articul8, the leader in domain-specific Generative AI (GenAI) for enterprises and regulated industries, today announced the launch of A8-SupplyChain, the industry's first family of specialized GenAI models specifically engineered to optimize supply chain, manufacturing, and industrial process operations with autonomous reasoning and real-time decision-making capabilities. Unlike general-purpose large language models (LLMs), which rely on manual prompts and struggle with complex industrial systems, A8-SupplyChain autonomously translates complex technical documentation into structured, actionable sequences - enabling real-time reasoning, decision making, and adaptation within industrial environments that demand deep contextual understanding. A8-SupplyChain seamlessly integrates into Articul8's proprietary ModelMesh™: a dynamic intelligence layer that regulates decision and action nodes across multiple specialized models, providing context-aware recommendations even from fragmented and legacy documentation. "We built A8-SupplyChain specifically to tackle the problems that general-purpose GenAI can't: delivering accurate, transparent, and fully traceable reasoning through complex technical documentation and real-world workflows," said Arun Subramaniyan, founder and CEO of Articul8. "This is not just another model - it's a fully autonomous system built specifically for mission-critical environments." Built for Where the Work Happens A8-SupplyChain is designed to support complex enterprise production environments and platforms - including customers and partners, such as iBase-t, Itochu Techno-Solutions Corporation, Intel, and Accenture. The models perceive and reason over fragmented, unstructured data - across PDFs, engineering diagrams, maintenance logs, quality systems, and structured tables - without the need to move or centralize data, a core differentiator for enterprise-grade security, performance, and compliance. The models are trained on high-fidelity technical documentation, augmented with proprietary training methodologies including continued pretraining and multi-modal reinforcement. A8-SupplyChain autonomously identifies dependencies, inconsistencies, and potential improvements within manufacturing and supply chain processes, delivering AI-driven recommendations without extensive manual customization. Performance That Outpaces the Field In performance benchmarking and repeatability tests - spanning digital work instruction generation, defect pattern recognition, and root cause analysis in assembly and manufacturing, A8-SupplyChain consistently outperformed leading open and closed-source GenAI models, including LLaMa 3.2 and GPT4o. The models achieved 92% accuracy when assigning correct labels and ordering elements in complex assembly and manufacturing workflows. Reasoning performance on complex manufacturing process sequences improved 3x compared to traditional methods, showcasing a step-change in how GenAI supports real-world production logic. A8-SupplyChain models also demonstrated expert-level reasoning capabilities, scoring 89.1% on MATH-500 (a benchmark for advanced numerical reasoning) and 80% on AIME-2024 (a test for real-world problem-solving skills). Optimized for production environments, the models generate approximately 140 tokens per second in real-time synchronous execution mode and up to 300 tokens per second in batched execution mode on L40-class graphics processing units (GPUs) - enabling seamless deployment in production and manufacturing environments. Setting a New Standard for GenAI in Aerospace & Defense Manufacturing General-purpose GenAI models are not designed for the structured, regulated, and interdependent nature of manufacturing and supply chain operations. Articul8's domain-specific approach delivers autonomous insight generation with minimal tuning, structured reasoning across complex documentation and workflows, and compliance-ready outputs with traceability and auditability. This launch builds on Articul8's fast growing portfolio of a domain-specific family of models across various industries including energy and semiconductor - and cements its role as the go-to GenAI partner for enterprises operating in high-stakes, data-intensive environments. "With A8-SupplyChain, we're giving aerospace and defense leaders something new: a fully orchestrated system that doesn't just generate answers - it understands, adapts, and drives outcomes," said Subramaniyan. "This is the next leap forward in enterprise AI - intelligent systems that operate at scale, with context, precision, and trust built in." To learn more, please visit articul8.ai. About Articul8 Articul8 AI is a technology company whose products transform enterprise data and expertise into powerful engines of growth, value and impact. Our full-stack GenAI platform is revolutionizing how enterprises harness their data and expertise to build expert-level Generative AI applications for their mission-critical challenges. Our products deliver enterprise-scale impact with ROI in hours to weeks. General-purpose GenAI models, while necessary, are not sufficient to deliver enterprise-specific decisioning and actioning. Our platform addresses this gap by making it straightforward for companies to build sophisticated, enterprise-scale and expert-level GenAI applications that encode their domain expertise. Our proprietary technology does the heavy lifting through autonomous decisions and actions, automated data intelligence, improved precision and relevance with industry knowledge encoded into Articul8's library of domain and task-specific models. We are purpose-built for regulated industries and meet the highest standards of compliance, data security, privacy and performance, including traceability and auditability at every step. We are trusted by leading global enterprises such as Franklin Templeton, Intel, Itochu Techno-Solutions Corporation, AWS, Intel and Accenture transform their mission-critical work. Media Contact Kacie Thomas (559) 287-0325 Kacie.Thomas@articul8.ai Market News and Data brought to you by Benzinga APIs

Articul8 Launches Domain-Specific GenAI Models Built for Supply Chain Operations | PYMNTS.com

Articul8 has launched a family of GenAI models built to optimize supply chain, manufacturing and industrial process operations. The new A8-SupplyChain models are domain-specific and have the deep contextual understanding needed to autonomously translate complex technical documentation into structured, actionable sequences, the company said in a Friday (April 4) press release. "We built A8-SupplyChain specifically to tackle the problems that general-purpose GenAI can't: delivering accurate, transparent and fully traceable reasoning through complex technical documentation and real-world workflows," Articul8 Founder and CEO Arun Subramaniyan said in the release. "This is not just another model -- it's a fully autonomous system built specifically for mission-critical environments." The A8-SupplyChain models support complex enterprise production environments and platforms, including customers and partners, according to the release. They can use unstructured data, including PDFs, engineering diagrams, maintenance logs, quality systems and structured tables, the release said. Because they are trained on high-fidelity technical documentation, the models deliver AI-driven recommendations without extensive manual customization, per the release. "With A8-SupplyChain, we're giving aerospace and defense leaders something new: a fully orchestrated system that doesn't just generate answers -- it understands, adapts and drives outcomes," Subramaniyan said in the release. "This is the next leap forward in enterprise AI -- intelligent systems that operate at scale, with context, precision and trust built in." Enterprises are turning to AI to automate not just repetitive tasks but also more complex processes like compliance monitoring, fraud detection and supply chain optimization, PYMNTS reported in January. Articul8 was established in January 2024 by Intel and DigitalBridge Group, which joined forces to create it as an independent company that provides enterprise customers with a secure and vertically optimized GenAI software platform. The company's platform enables business to harness the power of AI while keeping their data secure; offers a turnkey GenAI software platform that delivers speed, security and cost-efficiency to large enterprise customers; and supports a range of hybrid infrastructure alternatives, allowing customers to choose cloud, on-premises or hybrid deployment options. Today, in addition to the new A8-SupplyChain, Articul8 offers domain-specific models for various industries, including energy and semiconductor, according to the Friday press release.