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 Now
Apply Now