Database schemas often become "black boxes" over time. They accumulate technical debt, hidden bottlenecks, and security vulnerabilities that even seasoned developers might miss during a hectic sprint.
I decided to change that.
I’m excited to introduce AI Database Analyzer & Architect, an enterprise-grade tool built with .NET MAUI and Clean Architecture that acts as an autonomous Senior Database Consultant in your pocket.
Why This Isn't Just "Another AI Wrapper"
Unlike simple chat interfaces, this tool is designed with Architectural Intelligence. It doesn't just read your schema; it audits it across 40+ specialized pillars including:
Performance Tuning: Detecting non-SARGable predicates, missing indexes, and trigger-induced deadlocks.
Security & Privacy: Using NER logic to find unmasked PII/PHI data and identifying SQL injection risks in stored procedures.
Scalability: Identifying horizontal scalability bottlenecks and ORM anti-patterns (like the dreaded N+1).
Data Modeling: Detecting normalization violations (3NF) and circular dependencies.
The Tech Stack
Frontend: .NET MAUI (Multi-platform) with a strict Senior-level MVVM pattern.
Hybrid AI: Toggle between Google Gemini for high-scale cloud intelligence and Ollama for local, privacy-first analysis.
Visualization: Interactive, zoomable ER diagrams powered by Mermaid.js.
Reporting: Executive-level PDF exports using QuestPDF.
Deep-Dive Audits
The analyzer goes deep. It checks for Identity Column Capacity (warning you before your INT runs out), Collation Mismatches that kill index performance, and even AI Model Collapse (detecting if your data is being polluted by synthetic LLM noise).
Open Source & Future
This project is built to bridge the gap between raw database metadata and actionable expert insights. Whether you are using SQL Server, PostgreSQL, or MySQL, this tool helps you ensure your data layer is production-ready.
Check out the repo here: https://github.com/furkiak/AIDatabaseAnalyzer
I'd love to hear your thoughts! What's the biggest "hidden" database issue you've ever encountered in production? Let's discuss in the comments!
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