1. Project Title:
The Humanitarian AI Chatbot
2. Implementing Organization (Placeholder):
baena.ai — Jesus Baena (Solo Developer)
3. Project Background & Problem Statement:
Humanitarian practitioners often require real-time data analysis and decision-making support in challenging field environments. While AI offers a solution, many tools are gated or lack the specific context of humanitarian datasets. There is a need for an accessible, “humanitarian-first” assistant that provides immediate value to guest users while allowing professional staff to maintain a persistent, secure history of their technical queries and operational data.
4. Project Goal:
To deploy an open-access, web-based AI application optimized for humanitarian field staff, offering immediate guest access and an optional authenticated path for persistent conversation management.
5. Project Objectives:
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Public Accessibility: Develop a public, session-based chat experience for anonymous users with zero friction to entry.
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Persistent Session Management: Implement a delegated authentication system via
baena.aito allow registered users to save and retrieve their full conversation history. -
Low-Overhead Architecture: Build a custom backend integration that connects a Nuxt server to n8n webhooks, ensuring all AI interactions are logged and persisted in a Supabase database.
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Educational Context: Optimize model responses to append humanitarian educational context, specifically supporting remote field staff in their decision-making.
6. Target Beneficiaries:
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Humanitarian Practitioners: Field staff requiring real-time data analysis and context-aware assistance.
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Humanitarian Organizations: Entities looking to enhance operational efficiency through standardized AI tools.
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Anonymous Visitors: Users seeking quick, reliable AI interaction based on humanitarian datasets.
7. Expected Outcomes & Deliverables:
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Live Chat Platform: A fully functional, production-ready web application hosted at
chat.baena.ai. -
Nuxt-n8n Bridge: A robust custom API layer that routes frontend requests to n8n workflows without traditional server bloat.
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Data Persistence Layer: A Supabase integration managing user profiles, assistant selection, and thread history.
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Technical Documentation: A summary of the architecture and features, moving from the initial Bubble prototype to the current custom stack.
8. Sustainability:
The project ensures long-term viability by migrating from a no-code prototype (Bubble) to a sovereign, custom architecture using Nuxt UI Pro, n8n, and Supabase. This “low-overhead” stack minimizes maintenance costs while maximizing control over data and AI behavior. By decoupling the frontend from the core automation workflows, the system can easily scale or adapt to new AI models as the humanitarian sector’s needs evolve.