CrossBorder AI Guide

An AI agent that turns complex cross-border bureaucracy into clear visual guides.

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Inspiration

Cross-border administrative procedures are often confusing for both citizens and companies. When two countries are involved, people struggle to understand which documents are required, which institutions are responsible, and what the correct order of steps is. This problem exists across the European Union and globally. Even official portals such as “Your Europe”, created under the EU Single Digital Gateway initiative, exist because navigating cross-border procedures remains complex for users. Source: European Commission – Single Digital Gateway (2023) https://single-market-economy.ec.europa.eu/single-market/single-digital-gateway_en The idea behind CrossBorder AI Guide was to explore whether an AI agent could transform an unclear situation into a structured action roadmap. Instead of giving a generic answer, the agent interprets the scenario, asks clarification questions, and generates a practical procedure guide.

What it does

CrossBorder AI Guide is an AI agent designed to help users understand cross-border administrative and market-entry scenarios. The agent takes a simple user description such as: "Romanian citizen marrying in Germany" or "Aluminum cookware from India entering the EU market" The system then performs a structured reasoning process: Interpret the scenario Classify the type of cross-border case Ask up to three clarification questions Identify relevant procedural or regulatory domains Generate a structured action roadmap The output includes: Required documents or business data Responsible institutions Complexity level Step-by-step actions This approach transforms vague questions into a clear procedure roadmap.

How we built it

The project was built as a lightweight AI agent workflow using web technologies and a generative AI model. Architecture: Frontend HTML CSS JavaScript AI Layer Gemini model via API The agent logic is divided into two stages. Stage 1 – Scenario Analysis The model interprets the user input and returns a structured JSON output containing: actor (citizen or company), origin country, target country, goal, scenario type, clarification questions, relevant procedural domains Stage 2 – Roadmap Generation The structured analysis is then passed to a second prompt which generates the final Cross-Border Procedure Roadmap. This separation between analysis and report generation improves clarity and makes the agent behave more like a real decision-support system rather than a simple chatbot. The interface also displays the agent workflow steps so users can see how the system interprets their request.

Challenges we ran into

One challenge was defining the correct scope for the MVP. Cross-border procedures can quickly become extremely complex, especially when legal regulations are involved. Instead of attempting to build a full legal system, we focused on creating a navigation tool that highlights relevant procedures and next steps. Another challenge was designing prompts that guide the AI model through structured reasoning instead of producing generic answers. Separating the process into two AI calls (analysis + roadmap generation) significantly improved the reliability of the outputs.

Accomplishments that we're proud of

The main accomplishment of this project is demonstrating a working AI agent workflow rather than a simple Q&A chatbot. The system: interprets cross-border scenarios, classifies the problem type, asks targeted clarification questions, maps relevant procedural domains, produces a structured action roadmap The project also demonstrates how the same agent can handle both citizen scenarios and business market-entry scenarios, showing the flexibility of the approach.

What we learned

This project showed how important workflow design is when building AI agents. The quality of the system does not depend only on the AI model itself but on how the reasoning process is structured. Breaking the process into multiple steps significantly improves the usefulness of the results. We also learned that making the agent’s reasoning visible in the interface increases user trust and helps people understand how the system reaches its conclusions.

What's next for CrossBorder AI Guide

Future development could expand the system in several directions:, integration with official administrative data sources, expanded scenario coverage (residence permits, diploma recognition, company formation), multilingual support, regulatory knowledge retrieval using document databases In the long term, the goal would be to build an AI navigation layer that helps users interact more easily with complex cross-border administrative systems.

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