Build an AI Lead Qualification Agent with Spring Boot and Groq
Businesses receive potential customers through websites, contact forms, email, phone calls, and social media. The problem is that many inquiries are not followed up quickly, and sales teams often spend time asking the same basic qualification questions.
In this tutorial, we will explore how to build an AI lead qualification agent with Spring Boot and Groq. The application uses Java, Spring Boot, REST APIs, an LLM accessed through Groq, and MySQL to turn natural-language customer conversations into structured sales leads.
What Is an AI Lead Qualification Agent?
An AI lead qualification agent is an application that uses a large language model (LLM) together with application logic and business data to interact with potential customers and determine whether an inquiry is a useful sales opportunity.
Unlike a traditional contact form, the customer does not have to fill out every field immediately. The AI can understand natural-language messages and ask relevant follow-up questions.
For example:
Customer: I want to build a modern 4-bedroom home near Dallas.
AI: That sounds like a great project. Do you have an approximate budget in mind?
Customer: Around $800,000.
The application can continue the conversation until enough information has been collected to create a qualified lead.
The Business Problem
Consider a custom home builder receiving dozens of inquiries every week. A traditional website might provide only a phone number and a contact form.
This creates several problems:
- Visitors may not receive an immediate response.
- Sales staff repeatedly ask the same qualification questions.
- Important lead information may be missing.
- Potential customers may leave before contacting the business.
- Sales teams have to manually organize incoming inquiries.
An AI lead qualification agent can handle the initial conversation and collect structured information before the sales team becomes involved.
How the AI Lead Qualification Agent Works
The basic workflow is:
↓
Lovable / React Chat Interface
↓
Spring Boot REST API
↓
AI Service
↓
Groq LLM
↓
Structured Lead Data
↓
Validation
↓
MySQL
↓
Sales Team
Groq provides access to fast LLM inference. The actual agent behavior is implemented by the Spring Boot application through prompts, application logic, validation, persistence, and business workflows.
This distinction is important: Groq provides the model inference layer; the application around the model implements the business agent.
Technology Stack
- Java 21
- Spring Boot 3
- Spring Web
- Spring Data JPA
- MySQL
- Groq API
- Large Language Model (LLM)
- REST APIs
- React or Lovable for the frontend
Creating the Spring Boot Project
Create a new Spring Boot project using Spring Initializr or your preferred IDE.
The main dependencies are:
- Spring Web
- Spring Data JPA
- MySQL Driver
- Lombok (optional)
The application will expose a REST API that receives customer messages and passes them to the AI service.
Project Structure
src/main/java/com/example/aileadagent
│
├── controller
│ └── AiController.java
│
├── service
│ ├── AiLeadService.java
│ └── GroqService.java
│
├── dto
│ ├── ChatRequest.java
│ ├── ChatResponse.java
│ └── LeadResponse.java
│
├── entity
│ └── Lead.java
│
├── repository
│ └── LeadRepository.java
│
└── AiLeadAgentApplication.java
Designing the REST API
The frontend needs a simple endpoint for sending customer messages. We can expose an endpoint such as:
POST /api/ai/chatA simple request DTO can look like this:
package com.example.aileadagent.dto;
public record ChatRequest(
String sessionId,
String message
) {
}
The sessionId allows the backend to associate multiple messages with the same visitor conversation.
Creating the Chat Response
The backend should return the AI's response to the frontend.
package com.example.aileadagent.dto;
public record ChatResponse(
String reply,
boolean leadCaptured
) {
}Creating the REST Controller
The controller receives the visitor's message and delegates processing to the service layer.
package com.example.aileadagent.controller;
import com.example.aileadagent.dto.ChatRequest;
import com.example.aileadagent.dto.ChatResponse;
import com.example.aileadagent.service.AiLeadService;
import lombok.RequiredArgsConstructor;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.*;
@RestController
@RequestMapping("/api/ai")
@RequiredArgsConstructor
@CrossOrigin(origins = "*")
public class AiController {
private final AiLeadService aiLeadService;
@PostMapping("/chat")
public ResponseEntity<ChatResponse> chat(
@RequestBody ChatRequest request) {
ChatResponse response =
aiLeadService.processMessage(request);
return ResponseEntity.ok(response);
}
}In a production application, the CORS configuration should be restricted to the actual frontend domain instead of allowing every origin.
Designing the AI System Prompt
The system prompt defines the role and behavior of the AI agent. For our custom home builder example, the agent should behave as an AI Home Advisor.
You are the AI Home Advisor for a custom home builder serving the Dallas, Texas area.
Your job is to help potential customers understand the custom-home building process
and qualify serious prospects.
You can answer general questions about:
- Custom home construction
- General building processes
- Typical project timelines
- Budget considerations
- Design and customization
- Consultation scheduling
Never invent exact prices, availability, or company policies.
When a visitor demonstrates serious buying intent, naturally collect:
- Name
- Email
- Phone
- Project type
- Approximate budget
- Expected timeline
- Additional requirements
Do not request every field at once.
Ask natural follow-up questions based on information already provided.
When sufficient information has been collected, confirm that the
business team can follow up with the customer.
Keep responses concise, professional, and friendly.A strong system prompt is important, but the application should not rely exclusively on the LLM to enforce business rules. Validation and security should also happen in the backend.
Integrating Groq
The Spring Boot application can communicate with the Groq API over HTTPS. A common approach is to use Spring's HTTP client facilities to send requests to the API.
The exact model available to your application can change over time, so the model name should be configured rather than hard-coded throughout the application.
Store the API key as an environment variable instead of putting it directly into source code.
GROQ_API_KEY=your-secret-keyThen configure the application to read the value from the environment.
groq.api.key=${GROQ_API_KEY}Never expose this key in the React or Lovable frontend.
Groq Service Layer
The service layer is responsible for communicating with the external LLM API. A simplified implementation can use Spring's HTTP client.
package com.example.aileadagent.service;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.http.HttpHeaders;
import org.springframework.http.MediaType;
import org.springframework.stereotype.Service;
import org.springframework.web.client.RestClient;
import java.util.Map;
@Service
public class GroqService {
private final RestClient restClient;
private final String apiKey;
public GroqService(
@Value("${groq.api.key}") String apiKey) {
this.apiKey = apiKey;
this.restClient = RestClient.builder()
.baseUrl("https://api.groq.com/openai/v1")
.defaultHeader(HttpHeaders.CONTENT_TYPE,
MediaType.APPLICATION_JSON_VALUE)
.build();
}
public String generateResponse(String systemPrompt,
String userMessage) {
Map<String, Object> request = Map.of(
"model", "YOUR_CONFIGURED_GROQ_MODEL",
"messages", new Object[] {
Map.of(
"role", "system",
"content", systemPrompt
),
Map.of(
"role", "user",
"content", userMessage
)
}
);
Map response = restClient.post()
.uri("/chat/completions")
.header(
HttpHeaders.AUTHORIZATION,
"Bearer " + apiKey
)
.body(request)
.retrieve()
.body(Map.class);
// Production code should map the response
// to typed DTOs instead of using raw Map objects.
return response.toString();
}
}The model identifier should be replaced with a model currently available to your Groq account. Keeping it configurable makes the application easier to maintain when models change.
Why Structured Output Matters
A conversational response is useful for the visitor, but the business also needs structured information.
Instead of receiving a paragraph such as:
"John wants to build a modern four-bedroom house near Dallas with a budget of approximately $800,000 and hopes to start within six months."
the backend should work toward structured data such as:
{
"name": "John Doe",
"email": "johndoe@example.com",
"phone": "+1 214-555-0199",
"projectType": "Modern Custom Home",
"budget": "$800,000",
"timeline": "Within 6 months",
"message": "4-bedroom home near Dallas",
"status": "QUALIFIED"
}Structured data can then be stored in a database, sent to a CRM, or used to trigger other business workflows.
Creating the Lead Entity
The lead entity represents the information collected during the conversation.
package com.example.aileadagent.entity;
import jakarta.persistence.*;
import java.time.LocalDateTime;
@Entity
@Table(name = "leads")
public class Lead {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
private String name;
private String email;
private String phone;
private String projectType;
private String budget;
private String timeline;
@Column(length = 2000)
private String message;
private String status;
private LocalDateTime createdAt;
public Lead() {
}
// Getters and setters
}Creating the Repository
Spring Data JPA makes database access straightforward.
package com.example.aileadagent.repository;
import com.example.aileadagent.entity.Lead;
import org.springframework.data.jpa.repository.JpaRepository;
public interface LeadRepository
extends JpaRepository<Lead, Long> {
}Saving Leads to MySQL
Configure the MySQL database in application.properties.
spring.datasource.url=jdbc:mysql://localhost:3306/ai_leads
spring.datasource.username=root
spring.datasource.password=YOUR_PASSWORD
spring.jpa.hibernate.ddl-auto=update
spring.jpa.show-sql=trueFor a real production deployment, database credentials should also be supplied through environment variables or a secure secrets-management system.
Lead Qualification Logic
The application should decide when a lead is ready to be stored. For example, a qualified custom-home lead may require:
- Customer name
- Valid email or phone number
- Project type
- Approximate budget
- Expected timeline
The LLM can help identify these fields, but the backend should validate the final data before saving it.
This creates a useful separation of responsibilities:
- LLM: Understand natural language.
- Spring Boot: Control business logic.
- Validation: Check data quality.
- MySQL: Persist the lead.
Example Customer Conversation
Consider a visitor interested in building a custom home in Dallas.
Customer: I want to build a modern 4-bedroom home near Dallas.
AI: That sounds like a great project. Do you have an approximate budget in mind?
Customer: Probably around $800,000.
AI: Thanks. When are you hoping to begin the project?
Customer: Within the next six months.
AI: That helps. Could I get your name and the best email or phone number for the builder's team to follow up?
Customer: John Doe, johndoe@example.com.
The application can now recognize that the conversation contains enough information to create a qualified lead.
Connecting the Agent to a Web Frontend
The backend does not need to know whether the frontend was built with React, Lovable, Angular, or another framework.
The frontend simply sends an HTTP request to the Spring Boot API.
const response = await fetch(
"https://your-api-domain.com/api/ai/chat",
{
method: "POST",
headers: {
"Content-Type": "application/json"
},
body: JSON.stringify({
sessionId: "visitor-123",
message: userMessage
})
}
);
const data = await response.json();
console.log(data.reply);This allows a Lovable-generated frontend to act as the public chat interface while Spring Boot remains responsible for the AI integration and business logic.
From Chatbot to Business Agent
A chatbot becomes significantly more useful when it can perform business actions rather than simply generate text.
For example, the same architecture can eventually support:
- Appointment booking
- CRM lead creation
- Email notifications
- Calendar integration
- Lead scoring
- Automated sales routing
- WhatsApp integration
- Customer follow-up
The architecture can therefore evolve from:
Customer
↓
AI Conversation
↓
Leadinto:
Customer
↓
AI Conversation
↓
Lead Qualification
↓
Lead Scoring
↓
CRM
↓
Sales Notification
↓
Appointment Booking
↓
Human Sales Follow-upError Handling and Security
An AI-powered application still needs normal backend engineering practices.
1. Protect API Keys
Never place the Groq API key inside frontend JavaScript. Keep it on the Spring Boot server.
2. Validate User Input
Incoming requests should be validated before being processed. Do not assume that an LLM-generated value is automatically valid.
3. Handle External API Failures
The Groq API can experience network failures, rate limits, or temporary errors. The backend should handle these cases gracefully.
4. Protect Customer Data
Lead information may contain personal data such as names, phone numbers, and email addresses. Production systems should apply appropriate access controls, encryption, logging policies, and data-retention practices.
5. Restrict CORS
During development, permissive CORS settings may be convenient. In production, only trusted frontend domains should be allowed.
How Businesses Can Use the Agent
Although this tutorial uses a custom home builder as the example, the same architecture can be adapted to many industries.
| Industry | Possible AI Agent |
|---|---|
| Real Estate | Property inquiry and lead qualification agent |
| Dental Clinics | Appointment and patient inquiry assistant |
| Home Builders | Construction project qualification agent |
| Law Firms | Initial client intake assistant |
| Insurance | Customer requirement and lead qualification agent |
| Education | Course inquiry and enrollment assistant |
| Automotive | Vehicle inquiry and test-drive booking assistant |
Possible Future Improvements
The first version of the application can be intentionally simple. Once the core workflow is working, additional capabilities can be added.
CRM Integration
Qualified leads can be automatically sent to a CRM system instead of being stored only in MySQL.
Appointment Booking
The agent can check a business calendar and help customers schedule consultations.
Email Notifications
A new qualified lead can trigger an email notification to the sales team.
Knowledge Retrieval
A retrieval-augmented generation (RAG) layer can allow the agent to answer questions using company documents, FAQs, service information, and other approved business content.
Lead Scoring
Leads can be assigned scores based on factors such as budget, timeline, project type, and buying intent.
Production Architecture
A production deployment would typically separate the public frontend from the backend API.
↓
Spring Boot API
↓
AI Service
↓
Groq
↓
Lead Qualification
↓
MySQL
↓
CRM / Email / Calendar
Authentication, rate limiting, monitoring, centralized logging, secure secrets management, database backups, and proper deployment infrastructure should be considered before exposing the system to real customers.
Why Spring Boot Works Well for AI Applications
AI applications do not have to be written entirely in Python. Java and Spring Boot can provide a strong backend foundation for applications that use LLMs.
Spring Boot already provides mature support for:
- REST APIs
- Dependency injection
- Database access
- Security
- Validation
- HTTP clients
- Application configuration
- Production deployment
This makes it possible to combine traditional enterprise backend engineering with modern AI capabilities.
Conclusion
Building an AI lead qualification agent is more than adding an AI chatbot to a website. The real value comes from connecting the language model to application logic and business workflows.
In this architecture, the customer interacts with a web-based AI assistant, Spring Boot manages the application workflow, Groq provides LLM inference, and MySQL stores the resulting lead information.
The same foundation can be extended into appointment booking, CRM integration, email notifications, WhatsApp workflows, lead scoring, and automated sales routing.
For developers, this approach demonstrates how Java and Spring Boot can be used to build practical AI-powered business applications rather than treating AI as a standalone chatbot.
Frequently Asked Questions
Can I build an AI agent with Java and Spring Boot?
Yes. Spring Boot can communicate with LLM APIs through HTTP clients and provide the REST, database, validation, security, and business-logic layers required by an AI application.
Is Groq an AI agent framework?
Groq provides access to LLM inference. The agent workflow itself is implemented by the application using prompts, conversation state, business logic, tools, validation, and external integrations.
Can this AI agent be connected to a website?
Yes. A React, Angular, or Lovable frontend can communicate with the Spring Boot REST API and display the AI responses to website visitors.
Can the AI agent save leads automatically?
Yes. Once the backend determines that the required lead information has been collected and validated, it can persist the lead in MySQL or send it to a CRM.
Can the same system be used for other businesses?
Yes. The conversation instructions, qualification fields, knowledge sources, and integrations can be adapted for industries such as real estate, healthcare, education, automotive, insurance, and professional services.
FOR DEMO PURPOSE:DEMO APP