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Internet & Media

AI for digital platforms.

Pilots that impress in a demo and then stall. The shift that matters is from a few prototypes to a capability the whole company uses one non-engineers run themselves, without IT inheriting the upkeep.

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Deployed in Production at.

A leading digital content & price-comparison platform operator (Japan)

This operator runs multiple comparison and review platforms. Its AI & Data Platform team deployed Dify Enterprise on Google Kubernetes Engine, connected it to Azure AD / Okta SSO, and rolled it out company-wide.

The clearest proof is in what front-line teams have built. On one platform, an engineer credited Dify with a significant drop in content-production costs through automated content creation.

On another platform, the marketing team stood up an automated product-information system in about three hours. It now runs independently — no ongoing engineering involvement required.

Read the full storyHow a Leading Japanese Digital Platform Operator Deployed Dify Enterprise Company-Wide
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  • 30%

    Of staff registered within ~1 month (Dify)

  • 70

    Apps built on Dify

  • 5

    Active workspaces

  • 80%

    Of users on Dify every week

Source: Dify deployment at a leading digital content & price-comparison platform operator, 2025

Why
This Industry, Why Now.

The constraints

Internet and media businesses produce content, data and service at a scale that makes manual processes hard to sustain. Editorial teams need to create content faster than hiring allows.

Data teams need structured information from unstructured sources across the web, continuously and at high volume.

And internal productivity demands keep growing as teams handle more platforms, more markets and more SKUs than before — while the expectation is that each workflow change does not require a new development project.

The role of AI

For this industry, the shift that matters is not adding another AI tool. It is putting the capability directly in the hands of the people who run the work — editorial teams, data analysts, operations — so they can build, run and improve their own workflows without routing everything through a development backlog.

Patterns We See.

Content creation pipelines
Teams use Dify workflows to take raw inputs — photos, transaction data, a business brief — and produce structured content ready for publication. The workflow handles the templating, formatting and localisation steps. Editorial teams review and publish rather than draft from scratch.
Data extraction and standardization
Many platforms need a constant feed of product, content or market information from across the web. Dify is deployed as the orchestration layer for scraping, parsing, normalising and loading that data into downstream systems, replacing fragile bespoke scripts with maintained, observable workflows.
Internal productivity assistants
Inquiry-response chatbots, page quality checks, meeting minutes automation, document and policy search — these are built on Dify by the teams that use them, without needing engineering resources for each one. Once built, they run on the same platform that handles everything else.

Discuss Internet & Media Workflows with Dify.

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