Detect SQL Server pressure before it becomes guesswork
Waits, Query Store movement, workload pressure, tempdb behavior, and blocking context are kept together so the operator starts from evidence instead of noise.
AI DBA Agent for SQL Server
A governed DBA agent for SQL Server operations.
SigmaDbIQ is evolving into a context-aware AI DBA agent for production SQL Server teams: evidence first, recommendations traceable, and remediation controlled through human approval and rollback readiness.
Production Workflow
From pressure to approved remediation review.
The goal is not to make a model sound confident. The goal is to give SQL Server teams a reviewable path from regression signal to controlled action.
Explain the regression with traceable findings
SigmaLens™ ties baselines, variance, impact, and missing data into a reviewable evidence trail that a DBA can challenge before any action is considered.
Move toward remediation without bypassing governance
Recommendations are packaged with approval gates, rollback readiness, and human control so production changes stay auditable and reversible.
Governance Model
Designed for production teams that need evidence before action.
SigmaDbIQ keeps AI assistance inside a DBA operating model. It can surface likely causes, summarize the proof, and prepare the next step, but the workflow stays grounded in SQL Server evidence and approval control.
Evidence attached to each finding
Human approval before production execution
Rollback path prepared before action
Missing context called out explicitly
SQL Server-specific reasoning surface
No black-box autonomous changes
Evaluation Questions
Questions evaluators usually ask.
What is an AI DBA agent for SQL Server?
An AI DBA agent for SQL Server is an operational assistant that analyzes database evidence, explains likely causes, and prepares governed remediation for DBA review. SigmaDbIQ focuses that workflow on SQL Server performance regressions, Query Store context, waits, and production approval controls.
Does SigmaDbIQ execute SQL Server changes automatically?
SigmaDbIQ is designed around governed workflows. Findings, evidence, recommendations, and rollback context are prepared for human review before production execution.
How is SigmaDbIQ different from a chatbot?
SigmaDbIQ is not positioned as a general chatbot. It is built around SQL Server evidence, SigmaLens™ regression analysis, traceability, approval gates, and operational workflows.
What SQL Server evidence can SigmaDbIQ use?
SigmaDbIQ can reason from SQL Server operational evidence such as Query Store movement, waits, workload pressure, blocking context, health scoring, and regression baselines.