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

venturebeat.com
5 min read
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Articul8's specialized models tackle complex industrial sequences where timing and order matter, challenging the one-size-fits-all approach to enterprise AI.
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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."

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