Multiverse comes to n8n with a new node to build AI automations

More and more teams want to bring AI into their day-to-day processes to automate increasingly complex tasks. However, they often face a clear barrier: moving from a one-off proof of concept to a workflow that is useful, repeatable, and maintainable.

To tackle this challenge, automation platforms such as n8n are available, allowing teams to connect applications, services, and AI models within a single workflow. This makes it easier to automate processes, integrate with different APIs and systems (email, cloud storage, etc.), and continuously maintain, debug, and improve the workflows over time.

Because n8n is an extensible platform, it also allows custom nodes to be built, expanding its capabilities even further. In this context, at Plain Concepts Research we have worked together with Multiverse to develop automations powered by their CompactifAI technology.

CompactifAI is Multiverse Computing‘s approach to efficient AI. It provides compressed AI models that are lighter and more cost-effective, making it easier to deploy them in private cloud environments and to work with complex models while requiring less memory and storage.

In addition, CompactifAI offers an API-accessible model system that fits naturally into automated n8n workflows: n8n orchestrates the process, and CompactifAI provides the intelligence.

To enable the use of CompactifAI models, at Plain Concepts Research we have built a community node for n8n that makes it easy to integrate them into workflows.

The idea is simple: anyone already using n8n should be able to add CompactifAI AI capabilities to their automations with minimal effort, without having to rebuild existing workflows.

With that goal in mind, we created our community node, integrating CompactifAI as another component within the n8n ecosystem. It is open source and available on npm.

To showcase the capabilities of this new node, we prepared two demo use cases.

In the first demo, we showcase a real-world workflow for the pre-assessment of insurance premium financing. The goal is to shorten the initial analysis time while still giving the customer a clear answer and keeping risk under control.

The workflow we built in n8n follows this sequence:

• A web form captures the lead’s details, including profile, income, expenses, insurance type, and payment history.
• The flow then normalises and validates the data to avoid decisions based on incomplete or inconsistent information.
• An agent powered by CompactifAI models carries out three specialised analyses: data quality, business recommendation (insurance type and broker approach), and initial risk assessment.
• In parallel, the process applies non-negotiable business rules to ensure minimum policy criteria are met.
• Based on all of this, the system generates an initial decision (APPROVE, REVIEW, or REJECT), along with a clear justification for both the business and the customer.

This approach combines AI-driven contextual understanding with explicit rules, ensuring both data consistency and traceability.

The second example highlights a very common challenge for technical teams: having valuable documentation spread across manuals, guides, and API references. The information is there, but finding the right answer at the right time is not always easy. On top of that, it is often written in technical language that can be difficult for someone approaching the technology for the first time.

To solve this, we built an n8n-based RAG flow focused on Evergine. The idea is simple: when a question comes in, the workflow retrieves the most relevant context from the knowledge base and uses it to generate a useful, specific answer for the person asking.

This approach delivers three clear benefits:

• Less time spent searching across multiple sources.
• More accurate answers aligned with the project’s real documentation.
• A better experience for teams that need to move quickly without sacrificing technical rigour.

This launch is only the beginning. We plan to keep evolving the node with new capabilities, a better user experience, and more examples built around real-world scenarios.

We also want to build it together with the community by listening to feedback, identifying needs, and prioritising improvements.

We invite you to try it out, experiment with it, and share your feedback with us so we can keep making it better.