DARC: Cutting Through Dataspace Complexity with an AI Assistant

Joining a dataspace is supposed to unlock data, not bury participants in decisions. Yet for many organisations the first encounter with a federated environment looks less like an open door and more like a wall of questions. Which modules do I actually need? Are my two dataspaces even compatible for the workflow I have in mind? What does the regulation require of me before I share a single record? And once I have the answers, how do I configure everything correctly without a specialist on hand? This is the challenge the DS2 Discovery, Assessment, Recommend, Configure module (DARC) was built to solve.

The Challenge

DS2 deliberately offers a rich, modular toolkit. That richness is a strength, but it also raises the entry barrier. A typical participant — a farmer's cooperative, a city department, a small analytics provider — knows their own goal in plain terms: "I want to share field sensor data with an agronomy service, but keep ownership." They do not necessarily know that this maps to a particular combination of connectors, policy modules, and retrieval and orchestration components, each with its own prerequisites and configuration files.

Three difficulties compound here. First, discovery: understanding what a dataspace and its modules can and cannot do before committing. Second, assessment: judging whether a complex, cross-dataspace data lifecycle is even feasible, and what it demands in terms of compatibility and regulatory compliance. Third, the technical gap between a recommendation and a working setup, where good advice still leaves the user facing manual configuration. Without help, participants risk choosing the wrong modules, misconfiguring them, or abandoning the process altogether. For an inter-sectoral project whose whole purpose is to lower the friction of data sharing, that friction at the front door is precisely the problem worth removing.

The Solution: An AI-Driven Conversational Guide

DARC addresses this through a single, approachable interface: a conversational assistant powered by a Large Language Model. Instead of reading documentation and cross-referencing module specifications, a participant simply describes what they want to achieve in natural language, in plain English or other European languages, and DARC walks them from intent to a running pipeline.

The module's name maps directly to the four-step journey it guides users through:

• Discover — DARC helps participants inquire into the data-oriented capabilities and limitations of DS2 dataspaces and modules, both within a single dataspace and across interconnected ones.

• Assess — It provides AI-driven assessment of the prerequisites and feasibility for executing complex Digital Life Cycles (DLCs) between participating dataspaces, flagging compatibility and compliance considerations along the way.

• Recommend — Based on that assessment, it recommends the best-fitting DS2 modules, complete with their software prerequisites and configurations, to compose the "ideal use" scenario for the user's needs.

• Configure — Crucially, DARC does not stop at advice. Through APIs, it can automatically configure a subset of the recommended modules, so participants can build and start using their DS2 pipeline with minimal manual effort.

  
  

The result is an end-to-end guided experience that takes a user from "here is what I am trying to do" to a configured, ready-to-use data workflow, without requiring deep technical expertise at every step.

More Than a Generic Chatbot

The obvious question is what stops this from being a chatbot that gives confident but wrong answers. The difference is that DARC is grounded in DS2's own knowledge: its responses draw on a curated base of DS2 module documentation, configuration details and relevant regulations, so recommendations reflect what the toolkit can actually do rather than generic guesswork. It also works across terminology and languages, so users are not tied to a single vocabulary. And because the assistant can act on its own recommendations through the modules' configuration APIs, the path from advice to a working setup stays inside the same conversation.

In short, DARC pairs the accessibility of a chatbot with the reliability of a system that knows DS2 from the inside.

Why It Matters

DARC turns the breadth of the DS2 toolkit from an obstacle into an advantage. By lowering the technical entry barrier, it lets a wider range of participants, including those without dedicated integration teams, take part in dataspaces and set up sovereign, compliant data sharing.

Consider the Precision Agriculture use case in Northern Greece, where the DigiAgro and AgroScience dataspaces exchange IoT sensor data, satellite indices, weather forecasts and AI models while farmers retain full ownership of their data. Coordinating the modules this requires — secure cross-dataspace exchange, policy enforcement, data rights management — is exactly the kind of multi-module, multi-dataspace setup where a participant benefits from being guided rather than left to assemble it alone. DARC is designed to be that guide: assessing feasibility, recommending the right combination, and helping configure it.

By making discovery, assessment, recommendation and configuration as simple as a conversation, DARC helps DS2 deliver on its core promise: trusted data exchange across sectors that is secure, sovereign and genuinely accessible to the people who need it.

DARC is developed by ATC. Explore the module documentation and the wider DS2 toolkit via the DS2 Portal & Modules.