AI-assisted database engineering, not a black box.
DBMind AI uses AI to accelerate assessment, analysis, planning, optimization, automation, and validation — with database engineers reviewing every recommendation before it touches production.
AI analysis, live on your workload
A live view of workload health, with AI recommendations ranked by impact.
Query #1842 is experiencing increased execution time due to a missing index and elevated logical reads. Recommendation: create a non-clustered index on CustomerID.
Illustrative example. Actual metrics and recommendations depend on your environment.
Where AI fits into database engineering
Assessment
Rapidly inventory environments and flag compatibility and risk before a project begins.
Analysis
Correlate query plans, metrics, and logs to find root causes faster than manual review.
Planning
Model migration and optimization options, and estimate effort, risk, and impact.
Optimization
Recommend indexing, query, and configuration changes ranked by impact.
Automation
Apply low-risk, pre-approved changes automatically, on your schedule.
Validation
Confirm outcomes against baselines after every change is applied.
AI-assisted, engineer-validated
AI accelerates the work — it doesn't replace engineering judgment.
Recommendations, not autonomous changes
AI surfaces options; your team and ours decide what gets applied and when.
Explainable by design
Every recommendation includes the evidence and reasoning behind it.
Engineer-reviewed
Database engineers validate AI findings against your environment before production.
Full audit trail
Every recommendation, decision, and change is logged for compliance and review.
See what AI can find in your database.
Get a structured, AI-assisted assessment of your environment — reviewed by database engineers.