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Semiconductor

AI on your own model.

Years of R&D sit in technical documents no one can search across languages. Put retrieval, assistants and content generation on Dify, orchestrated over your own internal model, so the strategic AI work stays in-house.

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Deployed in Production at.

A leading global semiconductor company

A leading global semiconductor company with around 58,000 employees runs Dify Enterprise on Red Hat OpenShift AI, with its own internal large language model underneath.

In this architecture, Dify orchestrates over the proprietary model rather than any external provider, keeping the strategic AI layer firmly in-house.

Use cases span information retrieval, virtual assistants, and content generation across multiple languages and regions. Coding-related work is handled separately through dedicated developer tools, keeping each tool focused on what it does best.

Read the full storyHow a Global Semiconductor Leader Runs AI Over Its Own LLM with Dify Enterprise
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  • 58,000

    Employees on one Dify platform

  • 3

    Use-case families live on Dify (retrieval / assistants / content)

  • 54

    R&D centres' technical docs, searchable on Dify

  • 10+

    languages auto-translated and summarised across regions

Source: Dify deployment at a leading global semiconductor company

Why
this industry, why now.

The constraints

Semiconductor R&D and customer-engineering teams sit on vast internal repositories — technical specifications, application notes, product roadmaps, customer support histories — accumulated across decades and often across languages.

That knowledge is hard to search, harder to surface across language boundaries, and increasingly critical as product portfolios and customer bases grow.

Some companies have also made strategic investments in their own internal large language models, which they want to get real business value from — not just run in isolation.

The role of AI

Here AI tooling is layered, not one box. Content, knowledge and assistant work belongs on a managed orchestration platform. Coding and developer tooling belongs on specialised tooling. The question is which platform carries the orchestration layer, and whether it can run on top of an internal model rather than requiring an external one.

Patterns we see.

Orchestration over your own internal LLM
Companies that maintain their own internal LLM use Dify as the orchestration layer on top of it. Retrieval, routing, prompt management and multi-step workflow logic all sit in Dify. The internal model provides the inference. The strategic investment in that model pays off across a broader set of use cases.
Retrieval across R&D repositories
RAG combined with agent patterns gives engineers and customer-facing teams fast access across technical document repositories, regardless of which language the document was written in. Engineers find specifications. Customer engineers answer technical questions. Product teams surface relevant prior work. All from a single interface connected to the same underlying corpus.
Clear boundaries between content and code
Content, knowledge and assistant use cases run on Dify. Developer tooling runs on separate coding-specific platforms. The split is a design choice: it keeps each tool doing what it is good at, and prevents the Dify environment from becoming a development environment it was not designed to be.

Discuss Semiconductor workflows with Dify.

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