AI-assisted database performance SaaS

Overview

Teams running MySQL, MariaDB and PostgreSQL connect their databases to the platform with a small agent or a direct connection. The platform collects performance evidence, analyses it and presents ranked recommendations with an explanation, a proposed fix, its risk and a rollback. A user can apply, defer or dismiss a recommendation, ask follow-up questions in a chat, or have a fix opened as a pull request against the application repository. We built the whole platform, and it is still in active development.

The challenge

Tuning a database needs real evidence from a live system, and the advice has to be reliable enough to act on. The platform had to collect that evidence safely, keep the cost of AI predictable, and still be simple enough for someone who is not a database specialist.

What we built

1. A cross-platform agent and background service that collects performance evidence from customer databases
2. Support for MySQL, MariaDB and PostgreSQL, tested against several versions of each
3. Analysis of query, schema and server-configuration problems, including index recommendations
4. AI-assisted analysis and chat
5. Quality controls on AI output before it is shown, and a harness for evaluating it
6. Recommendations that carry a proposed fix, risk, confidence and rollback, with apply, later and dismiss actions
7. A GitHub integration that links findings to application code and drafts pull requests
8. An optional automatic mode with safeguards, maintenance windows and an audit trail
9. Usage-based billing with credits, plans and a free trial
10. A live web interface, notifications and email digests, an admin console, a public documentation site and onboarding
11. An MCP server so customers can use their own AI assistant with their findings (described in a separate case study)
12. Automated testing against real databases and layered continuous-integration checks
13. Container deployment with automated rollback and monitoring dashboards