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AI Analytics Engine with Spring AI: Questions to Charts

Name: AI Analytics Engine with Spring AI: Questions to Charts
Rating: 5.0 (7 reviews)

Build a production-ready AI analytics system with Spring AI using LLMs, SQL generation, insights, and charts

Created byInfiproton Tech, Harish B N

Last updated 3/2026

English

What you'll learn

Build a complete AI analytics engine using Spring AI that converts natural language questions into SQL, insights, and charts automatically.
Generate SQL safely using LLMs and validate queries with a SQL parser before executing them on a live PostgreSQL database.
Convert raw database results into structured summaries, findings, recommendations, and chart-ready data using Spring AI.
Dynamically read database schema at runtime so your AI system adapts automatically when tables or columns change.
Detect vague or unreliable questions and prevent meaningless insights from reaching users.
Design reliable, deterministic AI backend systems using proper architecture, prompt design, and Spring Boot integration.
Automatically generate charts based on analysis patterns like trend, distribution, and correlation.
Build production-ready AI systems using Java, Spring Boot, PostgreSQL, and Spring AI.

Course content

8 sections • 42 lectures • 2h 8m total length

What We Are Building: AI Analytics Engine Overview1:47
Understand the system we will build, its architecture, and how natural language questions become insights and charts.
Types of Business Questions This System Will Answer1:48
Explore real analytics questions businesses ask and see how AI can answer them using structured data.
Free IntelliJ IDEA (90 Days)0:41
Get IntelliJ IDEA Ultimate for free and follow this course using a professional development environment.
Redeem your 90-day access and get started quickly.
Creating the Spring Boot Project2:22
Create the Spring Boot project, add dependencies, and prepare the base structure for the AI analytics system.
Setup PostgreSQL, Load Dataset, and Explore Tables4:33
Run PostgreSQL using Docker, load schema and dataset, and explore tables and relationships.
Setting Up PostgreSQL with Docker and Loading the Dataset1:02
Creating the Insight API Endpoint Skeleton4:55
Create the REST endpoint that will accept questions and return insight responses.

AI Insight Flow Architecture2:34
Understand the full pipeline from question to insight and why analysis and interpretation are separate AI steps.
Generating SQL with AI Prompts7:57
Create and use the SQL prompt template to generate accurate SQL from user questions.
Executing AI SQL with JdbcTemplate2:37
Run the generated SQL and observe how raw database rows are returned dynamically.
Turning Rows into Insight Summary5:20
Send database rows to AI and generate a business-friendly summary mapped to your DTO.
Adding Findings to Insight Response3:12
Extend the DTO and prompt to include findings without changing SQL or API flow.
End-to-End Insight Demo with Postman2:10
Ask real questions and observe SQL, rows, summary, and findings working together.

Why Insights Need a User Interface1:18
Understand why Postman output is not usable for users and why a visual layer is required.
Connecting the Web Page to the Backend API1:56
Integrate the provided HTML page with your API and render insights without writing frontend code.
Adding Recommendations to Insights2:39
Extend the DTO and prompt to include recommendations that automatically appear in the UI.
Understanding the Role of UI in AI Systems1:48
Read the UI code briefly and derive a key AI design principle for frontend systems.

Identifying the Limitation of Hardcoded Schema2:02
Understand why static schema causes system failure.
Breaking the System with a New Database Domain2:32
Introduce new tables and observe system failure.
Reading Schema Dynamically from Database Metadata5:51
Fetch schema information dynamically using information_schema.
Injecting Dynamic Schema into AI Prompts2:28
Update prompts to use dynamic schema.
Caching Schema for Performance Optimization2:16
Cache schema to avoid repeated database reads.
Architectural Principle: Dynamic Schema Systems1:27
Understand why dynamic schema is critical for production AI systems.

Risks of Executing AI-Generated SQL Directly2:41
Understand why validation is necessary.
Parsing SQL Using JSQLParser6:58
Use a parser to analyze SQL structure.
Validating Table Names Against Schema3:49
Ensure referenced tables exist.
Validating Column Names Against Schema4:23
Prevent invalid column usage.
Blocking Unsafe SQL Statements5:35
Ensure only SELECT queries are allowed.
Integrating SQL Validation into System Flow2:43
Add validation layer into the pipeline.
Testing System Protection with Failure Cases3:31
Verify system rejects invalid queries.
Architectural Principle: Never Trust LLM Output Blindly2:20
Understand importance of validation in AI systems.

Observing Reliability Issues with Vague Questions3:23
Understand reliability challenges in AI systems.
Detecting Analytical vs Non-Analytical Questions2:27
Extend AI to detect unreliable questions.
Updating Prompt and DTO for Reliability Detection3:12
Implement reliability detection.
Preventing Invalid Questions from Execution2:49
Block vague questions safely.
Ensuring Deterministic and Consistent AI Output3:45
Configure deterministic AI behavior.

Requirements

Basic Java programming knowledge
Familiarity with Spring Boot fundamentals
Basic understanding of SQL
No prior experience with AI, LLMs, or Spring AI is required — everything is explained step-by-step
No prior frontend experience is required — a simple UI is provided and integrated

Description

Modern applications are no longer limited to dashboards built manually by developers. Today, users expect to ask questions in plain language and instantly receive meaningful insights, summaries, and visualizations.

In this course, you will build a complete AI-powered analytics engine using Spring Boot and Spring AI that converts business questions into SQL queries, structured insights, and charts automatically.

This is not a chatbot tutorial. This is a real backend system designed using production-grade architecture, reliability principles, and proven engineering practices.

By the end of this course, you will have built a system that accepts natural language questions, generates safe and validated SQL using LLMs, interprets database results into meaningful insights, and renders charts automatically in a web interface.

Everything is built step-by-step using Java, Spring Boot, PostgreSQL, and Spring AI.

Includes free 90-day access to IntelliJ IDEA Ultimate for a professional development experience.

Includes professionally prepared subtitles in Spanish, Portuguese (Brazil), Japanese, and Chinese.

What You Will Build

You will build a complete AI analytics pipeline with the following flow:

Question → AI generates SQL → SQL validation → Database execution → AI interprets results → Insight JSON → Charts rendered automatically

The system will include:

• Natural language question input
• Automatic SQL generation using Spring AI
• SQL validation using a parser to ensure safety
• Dynamic schema reading from the live database
• AI-generated summaries, findings, and recommendations
• Automatic chart generation based on analysis patterns
• Simple web interface that renders insights and charts
• Deterministic configuration for consistent and reliable output
• Protection against vague or unsafe questions

This mirrors how real AI analytics systems are built in production.

Why This Course Is Different

Most AI courses focus on basic prompt examples or simple chatbots.

This course teaches how to design and build a complete AI analytics backend using proper architecture and engineering discipline.

You will learn critical engineering principles such as:

• Generating SQL safely using LLMs
• Validating LLM output before execution
• Reading database schema dynamically at runtime
• Converting raw database rows into structured business insights
• Generating chart-ready data automatically
• Making AI systems reliable and deterministic
• Evolving intelligence using prompts without changing infrastructure

These are essential skills for building real AI systems.

End Result

By the end of this course, you will have built a complete AI analytics engine that:

• Accepts business questions
• Generates and validates SQL safely
• Produces meaningful insights automatically
• Generates charts automatically
• Adapts dynamically to database schema changes
• Ensures reliable and deterministic behavior

This project can serve as a foundation for real analytics products, internal tools, or enterprise AI systems.

Who this course is for:

Java developers who want to build AI-powered backend systems
Spring Boot developers integrating LLMs into applications
Developers interested in AI analytics using real databases
Backend engineers building analytics or data-driven systems
Anyone wanting to learn Spring AI through a real production-style project

AI Analytics Engine with Spring AI: Questions to Charts

What you'll learn

Explore related topics

Course content

Project Setup, Dataset, and Business Analytics Context7 lectures • 17min

From Question to Insight JSON using AI, SQL and Structured Output6 lectures • 24min

Presenting Insights in a Web Interface4 lectures • 8min

Understanding Analysis Patterns Behind Business Questions3 lectures • 8min

Visualizing Insights Using Charts3 lectures • 7min

Making the System Adapt to Database Changes6 lectures • 17min

Validating AI-Generated SQL for Safety8 lectures • 32min

Making the System Reliable and Deterministic5 lectures • 16min

Requirements

Description

Who this course is for: