# Building secure AI workflows: integrating Dify with Regolo for enterprise Integrating Regolo with Dify allows businesses to build advanced AI workflows without compromising data privacy. By combining Dify’s visual orchestration platform with Regolo’s European infrastructure, teams can deploy GDPR-compliant agents, chatbots, and RAG systems using open-source models. ## The business case for visual AI orchestration Managing AI applications through raw code quickly becomes a bottleneck for scaling teams. Hardcoding prompt chains, managing retrieval logic, and debugging multi-step workflows require dedicated engineering resources. Dify removes this friction. It operates as an open-source visual platform where developers and product teams can design AI workflows, connect databases, and manage observability from a single interface. Instead of writing custom Python scripts for every internal tool, teams use Dify to map out logical steps visually. You define where the data comes from, how the model should process it, and what the final output should look like. ## Why connect Regolo to Dify While Dify solves the orchestration problem, the choice of the underlying language model determines your compliance and security posture. Defaulting to standard US-based API providers often introduces risks regarding data sovereignty and GDPR compliance. Regolo provides the execution engine for these workflows through a [secure, European-based infrastructure](https://regolo.ai/privacy-first-ai-in-europe-zero-retention-sovereignty-and-the-new-risks-we-cannot-ignore/). By setting Regolo as the model provider in Dify, companies can access [high-performance open-source models](/models) like Llama-3.3, GPT-OSS-120B, and Qwen2.5-VL. [Data sent through the Regolo API](https://docs.regolo.ai) is not used for model training, ensuring that proprietary business context, customer data, and internal documentation remain private. ## Use Cases: Internal RAG for technical documentation Companies accumulate vast amounts of fragmented documentation across Confluence, Google Drive, and local servers. Using Dify, you can connect these knowledge bases to a retrieval-augmented generation (RAG) pipeline. Regolo processes the retrieved context and generates precise answers. Because the processing happens on European infrastructure, you can safely index sensitive internal architectures and financial policies. ## Multi-step data extraction Many businesses handle high volumes of unstructured data, such as invoices, contracts, or customer feedback forms. A Dify workflow can be built to receive a document, pass it to a [Regolo vision model](/models) to extract text, and then route the extracted text to a second model to format it as structured JSON for your database. ## How to configure the integration Connecting the two platforms requires a few initial steps to route Dify’s generation requests through the Regolo API. 1. **Obtain API credentials:** log into the Regolo dashboard and generate a new API key. Store this securely, as it authenticates your workflow requests. 2. **Configure the provider in Dify:** inside your Dify workspace, open a new or existing workflow. Navigate to the model settings for your LLM node, select "Model Provider Settings," and search for Regolo. 3. **Set the API key:** input your Regolo API key into the configuration panel. Dify will now fetch the list of available open-source models hosted on Regolo. 4. **Select the model:** choose the model that fits your specific node. You might use a smaller, faster model for basic text classification, and a larger model for complex reasoning tasks within the same workflow. ## Architectural limits and trade-offs Visual workflow builders simplify development, but they have limits. Highly complex applications with custom looping logic or non-standard API integrations can turn a clean visual graph into an unmaintainable web of nodes. In those edge cases, writing custom code might be more efficient. Additionally, combining multiple LLM calls in a single workflow increases latency. If a Dify pipeline triggers three sequential Regolo model inferences before returning an answer, the end-user will experience a delay. Teams must balance the depth of the workflow with the required response time, often relying on smaller, faster models for intermediate steps to keep the overall application responsive. --- ## FAQ **Does Regolo train models on the data processed through Dify?** No. Data sent to the Regolo API via Dify workflows is used strictly for inference and is not stored or used to train foundation models. **Can I use vision models in this integration?** Yes. Regolo supports multimodal models like Qwen2.5-VL, which can be selected inside Dify to process images and documents within your workflow. **Is Dify capable of handling RAG pipelines on its own?** Dify has built-in vector storage and retrieval capabilities. You can upload documents directly to Dify and use Regolo purely as the generation engine to synthesize the retrieved data. --- Start **our free 30-day trial at [regolo.ai](https://regolo.ai/) and deploy LLMs with complete privacy by design.** 👉 [Talk with our Engineers](https://regolo.ai/contacts/) or [Start your 30 days free →](https://regolo.ai/pricing) --- - [Discord](https://discord.gg/ZzZvuR2y) - Share your thoughts - [GitHub Repo](https://github.com/regolo-ai/) - Code of blog articles ready to start - Follow Us on X [@regolo\_ai](https://x.com/regolo_ai) - Open discussion on our [Subreddit Community](https://www.reddit.com/r/regolo_ai/) --- *Built with ❤️ by the Regolo team. Questions? [regolo.ai/contact](https://regolo.ai/contact)* or chat with us on [Discord](https://discord.gg/ZzZvuR2y)