Gushwork bets on AI search for customer leads — and early results are emerging

Introduction

In today's rapidly evolving digital landscape, AI-powered search tools are revolutionizing how businesses discover and engage with potential customers. Gushwork's recent success demonstrates the power of AI in lead generation, and this tutorial will teach you how to build a similar AI-powered customer discovery tool using Python and OpenAI's API. You'll learn to create a system that can analyze customer data, identify patterns, and surface relevant leads for your business.

Prerequisites

Before diving into this tutorial, you'll need:

• A Python 3.7+ environment
• An OpenAI API key (free to get at platform.openai.com)
• Basic understanding of Python programming
• Installed libraries: openai, pandas, numpy, scikit-learn

Step-by-Step Instructions

1. Set Up Your Development Environment

First, create a new Python project directory and install the required dependencies:

mkdir ai_lead_finder
 cd ai_lead_finder
 pip install openai pandas numpy scikit-learn

This creates a dedicated workspace and installs all necessary libraries for our AI lead discovery system.

2. Initialize Your OpenAI API Connection

Create a Python file called lead_finder.py and start by setting up your API connection:

import openai
import os
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Set your OpenAI API key
openai.api_key = os.getenv('OPENAI_API_KEY')

# Test the connection
try:
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Hello"}]
    )
    print("API connection successful!")
except Exception as e:
    print(f"API connection failed: {e}")

This step verifies that you can connect to OpenAI's API, which is essential for leveraging AI capabilities in your lead discovery system.

3. Prepare Your Customer Data

Next, create a sample customer dataset that your AI system will analyze:

# Sample customer data
sample_customers = {
    'company': ['TechCorp', 'InnovateInc', 'DataSystems', 'CloudSolutions'],
    'industry': ['Software', 'Healthcare', 'Data Analytics', 'Cloud Services'],
    'size': ['100-500', '50-200', '200-1000', '500-2000'],
    'interests': ['AI', 'Machine Learning', 'Data Science', 'Cloud Computing'],
    'budget': ['Medium', 'High', 'Medium', 'High']
}

customer_df = pd.DataFrame(sample_customers)
print(customer_df)

This creates a realistic dataset that mirrors the kind of customer information businesses collect, which will be analyzed by your AI system.

4. Create the AI Search Function

Implement the core AI search functionality that will analyze customer profiles:

def find_relevant_leads(customer_data, search_query):
    """Use AI to find relevant leads based on search query"""
    
    # Combine customer information into a single text field
    customer_data['combined_info'] = (
        customer_data['company'] + ' ' + 
        customer_data['industry'] + ' ' + 
        customer_data['size'] + ' ' + 
        customer_data['interests'] + ' ' + 
        customer_data['budget']
    )
    
    # Use OpenAI to analyze the search query
    prompt = f"""
    Analyze the following customer search query and identify which of these companies would be most relevant:
    Query: {search_query}
    
    Company Information:
    {customer_data[['company', 'industry', 'interests']].to_string(index=False)}
    
    Return only the company names that match the query, separated by commas.
    """
    
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=100
    )
    
    relevant_companies = response.choices[0].message.content.strip()
    return relevant_companies

This function combines customer information and uses OpenAI's language model to match search queries with relevant companies, mimicking Gushwork's approach to AI-powered lead discovery.

5. Implement Lead Scoring System

Add a lead scoring mechanism to prioritize the most promising prospects:

def score_leads(customer_data, relevant_companies):
    """Score leads based on relevance and business fit"""
    
    # Convert string of companies to list
    company_list = [comp.strip() for comp in relevant_companies.split(',') if comp.strip()]
    
    # Score each company based on various factors
    scores = []
    for _, customer in customer_data.iterrows():
        score = 0
        
        # Check if company matches
        if customer['company'] in company_list:
            score += 10
            
        # Check industry relevance
        if customer['industry'] in ['Software', 'Cloud Services', 'Data Analytics']:
            score += 5
            
        # Check budget level
        if customer['budget'] == 'High':
            score += 3
            
        scores.append(score)
        
    customer_data['lead_score'] = scores
    return customer_data.sort_values('lead_score', ascending=False)

This scoring system helps prioritize leads based on multiple criteria, making your AI discovery more strategic and actionable.

6. Run Your Lead Discovery System

Finally, put everything together to run your complete AI lead discovery system:

# Main execution
if __name__ == "__main__":
    
    # Set your API key (replace with your actual key)
    # os.environ['OPENAI_API_KEY'] = 'your-api-key-here'
    
    # Define search query
    search_query = "AI-powered customer analytics for healthcare startups"
    
    # Find relevant leads
    relevant_companies = find_relevant_leads(customer_df, search_query)
    print(f"\nRelevant companies: {relevant_companies}")
    
    # Score the leads
    scored_leads = score_leads(customer_df, relevant_companies)
    
    print("\nTop Lead Scores:")
    print(scored_leads[['company', 'industry', 'lead_score']])

This final step executes your complete system, demonstrating how AI can help identify and prioritize customer leads based on your business needs.

Summary

In this tutorial, you've built a foundational AI-powered customer lead discovery system similar to what Gushwork is implementing. You learned how to connect to OpenAI's API, process customer data, use AI to match search queries with relevant prospects, and score leads based on business fit. This system demonstrates the core concepts behind AI search tools that are revolutionizing customer acquisition strategies.

The key takeaway is that AI search tools like ChatGPT can be leveraged to automate and enhance lead discovery processes, helping businesses identify high-value prospects more efficiently than traditional methods. As you continue developing this system, you can expand it with more sophisticated data analysis, additional AI models, and integration with CRM systems.