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

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.