AI Automation Specialis: Self-Learning Lead Response Agent

Overview We’re building a self-learning Lead Response Agent that helps our virtual assistants (VAs) respond to inbound sales leads faster and smarter, and gets better every time it’s corrected. The goal: once a mistake is fixed once, the system never makes it again. We already have a clearly defined VA workflow and a detailed data structure. Now we need an experienced automation developer to design and implement the system that ties it all together. Project Goal Create a ChatGPT-based assistant that: References external data (past conversations, company rules, feedback) Generates accurate, client-ready responses for new leads Learns from supervisor corrections and updates itself automatically Stores all interactions in a structured external database (Google Sheets, AirTable, etc) Continuously improves through data feedback loops How the System Fits Into Our Workflow Step 1: Lead Inquiry A lead sends a question. The VA copies the entire conversation history and pastes it into the ChatGPT Lead Response Agent. Step 2: Context Retrieval The agent automatically queries a external database for: Similar past conversations (by industry, context, and tone) Company rules and business guidelines Time-weighted relevance (30/90 days → all history) Step 3: Response Generation ChatGPT: References the most relevant historical examples from external database Applies company rules Generates a context-aware, client-ready response Step 4: Feedback and Logging The VA submits the GPT response for supervisor approval. If approved: response is logged with Quality_Rating: “pass.” If corrected: GPT logs both the original and improved version for learning. GPT then adds a new row to external database with: Full conversation context Lead characteristics and industry Generated response + improved response Quality rating and summary Step 5: Continuous Learning GPT automatically references these logged corrections for future conversations. It always queries historical data before generating a new response. Company rule updates (like pricing or policy changes) are logged and applied dynamically. System Requirements Core Capabilities Two-way integration between ChatGPT and external database (Google Sheets, Airtable, etc.) GET: Retrieve relevant past data, rules, and examples POST: Log new responses, corrections, and rule updates Context prioritization based on recency, similarity, and quality Dynamic rule management for easy updates by non-technical users Structured learning system to improve responses over time Data Structure Primary Sheet columns: Date, Conversation_ID, Turn_Index Conversation_Context, Lead_Characteristics, Industry Response_Approach, Key_Components Original_Question, Immediate_Context Full_Response, Context_Summary Quality_Rating, Improved_Response Company Rules Sheet columns: Date_Updated, Category, Rule_Title, Rule_Description Deliverables Fully functional backend integration (Google Apps Script, Python, or similar) OpenAPI 3.1.0 schema compatible with ChatGPT Custom GPT Configured external database for all conversation and rule data Working feedback logging system for continuous learning Documentation for future maintenance by non-technical users What Success Looks Like ✅ ChatGPT can retrieve and post to external database (bi-directionally) ✅ GPT always queries historical data before generating responses ✅ Feedback and corrections are stored and reused intelligently ✅ Company rules are easily editable and automatically applied ✅ The system functions smoothly for non-technical VAs and supervisors Ideal Candidate Experienced with ChatGPT API + Google Apps Script (or Airtable/Firebase) Strong understanding of data retrieval and update automations Prior experience with AI feedback or “learning” loops Able to build documented, hand-off ready systems Communicates clearly and thinks about maintainability I am also open to fixed price contracts if you would like to propose one. Otherwise, please estimate the number of hours a project like this might take Apply tot his job