Ai Analytics Engine With Spring Ai: Questions To Charts
Published 2/2026
Created by Infiproton Tech
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 41 Lectures ( 2h 7m ) | Size: 1.6 GB
✓ 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.
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.
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
