AI Lead Automation Platform – n8n + Supabase + AI Agents

## Project Overview We are looking for an experienced AI Full-Stack Engineer to build a 2-week of an AI-powered lead automation platform. The platform will capture leads from multiple sources, automatically qualify and enrich them using AI, store and manage lead data in Supabase, and trigger automated workflows through n8n. The ideal candidate should be comfortable working across the entire stack, from system architecture and backend development to AI integration, workflow automation, frontend development, and deployment. This is a hands-on development project covering the complete product lifecycle. ## Core Functionality ### 1. Lead Capture The platform should support multiple lead sources, including: * REST API endpoints * Webhooks * CSV imports * A simple web-based lead capture form All lead data should be securely stored in Supabase and PostgreSQL. The system should maintain lead records, company information, lead status, scoring, interactions, AI analysis, and activity history. ### 2. AI Lead Qualification When a new lead enters the system, an AI-powered workflow should automatically analyze the lead. The AI should: * Analyze the lead and company information * Categorize the lead * Assign a lead score * Identify potential requirements or pain points * Determine buying intent * Identify relevant business opportunities * Generate a concise lead summary * Return structured data that can be consumed by downstream workflows The AI output should use structured and validated responses rather than relying on unstructured text. ### 3. n8n Automation n8n will be used as the primary workflow automation layer. Example workflow: New Lead → AI Analysis → Lead Score → Supabase → Notification → Follow-Up Example high-quality lead workflow: Lead Received → AI Qualification → Score above 80 → Create CRM Task → Notify Salesperson → Generate Personalized Email Example low-quality lead workflow: Lead Received → AI Qualification → Score below 40 → Add to Nurture Workflow The n8n workflows should be designed so that business logic and automation rules can be modified without requiring changes to the core application code. Expected workflows include: * New lead processing * AI lead qualification * Lead scoring * High-priority lead notification * Low-priority and nurture workflow * AI follow-up generation * Scheduled follow-ups * Workflow and activity logging ### 4. AI-Powered Follow-Up The platform should generate personalized follow-up emails using information such as: * Lead details * Company information * Previous interactions * Lead requirements * AI qualification results * Relevant knowledge-base content The generated email should be presented to the user for review and editing before it is sent. ### 5. Knowledge Base and Basic RAG Implement a basic knowledge-base system that allows the AI to retrieve relevant information when generating lead analysis and follow-up content. The knowledge base may contain: * Company information * Products and services * FAQs * Sales material * Relevant business documentation The implementation should be practical and appropriate for an MVP rather than unnecessarily complex. ### 6. Dashboard Build a clean and responsive Next.js dashboard. The dashboard should display: * Total leads * New leads * Qualified leads * Lead scores * Lead pipeline and conversion stages * Recent AI activity * Automation status * Workflow execution history Users should be able to open an individual lead and view: * Lead information * Company information * AI qualification * Lead score * Buying intent * AI-generated summary * Previous interactions * Follow-up history * Automation and activity history ## Technology Stack ### Frontend * Next.js * React * TypeScript * Tailwind CSS ### Backend * Python * FastAPI * REST APIs ### Database and Authentication * Supabase * PostgreSQL * Supabase Auth ### Automation * n8n * Webhooks * REST APIs * Scheduled workflows ### AI * OpenAI API * LLM-based AI agents * Structured outputs * AI lead scoring and qualification * Basic RAG and knowledge-base retrieval ### Deployment * Docker * Vercel * Supabase * AWS or another suitable cloud platform The developer may recommend alternative technologies where there is a strong technical reason to do so. ## Expected Deliverables At the end of the project, we expect a fully working MVP containing: * Next.js web application * Supabase database * Supabase authentication * Python and FastAPI backend * REST API endpoints * Lead capture system * CSV lead import * AI lead qualification * AI lead scoring * AI-generated lead summaries * AI-generated personalized follow-ups * Basic RAG and knowledge-base functionality * 5 to 8 production-ready n8n workflows * Workflow execution logging * Lead activity history * Dashboard and lead management interface * API and webhook integrations * Docker configuration * Deployment to a production or staging environment * Basic technical documentation ## Project Timeline Duration: 2 weeks We expect the project to progress through weekly milestones, with a working feature set demonstrated at the end of each week. ### Week 1 — Architecture and Foundation * Finalize technical architecture * Set up repositories and development environment * Configure Supabase and PostgreSQL * Implement authentication * Set up Next.js application * Set up FastAPI backend * Design database schema * Implement initial lead capture APIs * Implement AI lead qualification * Implement structured AI outputs * Implement lead scoring * Build initial n8n workflows * Connect n8n with the backend and Supabase * Implement activity and workflow logging ### Week 2 — Dashboard and AI Follow-Up * Build lead management dashboard * Build individual lead detail pages * Implement AI-generated follow-ups * Implement knowledge-base and RAG functionality * Add review and editing functionality for AI-generated emails * Complete remaining n8n workflows * End-to-end integration * Testing and bug fixing * Security and error handling * Docker configuration * Production or staging deployment * Documentation * Final walkthrough and handover ## Ideal Candidate You should have strong hands-on experience with several or all of the following: * Next.js and React * TypeScript * Python and FastAPI * Supabase * PostgreSQL * n8n * OpenAI and LLM APIs * AI agents * RAG and vector search * REST APIs * Webhooks * Docker * Cloud deployment Most importantly, you should be capable of taking a project from: Architecture → Development → AI Integration → Automation → Testing → Deployment We are not looking for someone who only handles one part of the stack. ## What to Include in Your Proposal Please include: 1. 2 to 3 examples of AI, automation, or full-stack applications you have personally built. 2. Examples involving any of the following are especially relevant: * n8n * Supabase * AI agents * OpenAI and LLM integrations * RAG * API integrations * Workflow automation 3. Briefly explain how you would approach the architecture for this project. 4. Tell us which parts of the proposed technology stack you have the strongest experience with. 5. Provide your estimated availability over the next 2 weeks. 6. Provide your estimated fixed-price or hourly budget for completing the MVP. ## Important Please mention "STACK" at the top of your cover letter. This helps us confirm that you have read the job description. We are looking for a developer who can own the technical implementation end-to-end, communicate clearly, and deliver working software on a weekly basis. Please do not send a generic proposal. We would like to see relevant examples of work you have actually built.