Detect
Pressure becomes signal
Live waits, Query Store movement, and workload pressure are separated from background noise.
Now in Early Access
Your SQL Server regressed.
Do you know why?
Monitoring tells you pressure exists. SigmaDbIQ proves what changed, why it matters, and which remediation path can be approved with traceable SQL Server evidence.
Powered by SigmaLens™, it turns symptoms into governed DBA action: evidence attached, rollback planned, human approval kept in control.
SigmaLens™Cause isolatedPlan + waits
Fix packagedRunbook + rollback
Approval gateHuman controlled
Noiseraw monitoring symptoms
Signaltraceable DBA action
6 sigmaStatistical methodology
< 5 minTime to first insight
ZeroBlack-box AI decisions
The gap between detecting and fixing
Your monitoring dashboard lights up red. Now what? You open five DMV queries, cross-reference Query Store, check wait stats, pull execution plans, and hope you don't miss something. That workflow is where hours disappear — and where mistakes happen.
01 — THE PROBLEM
Dashboards often stop at "something changed"
Traditional tools show you charts and alerts. They don't tell you which query regressed, whether the execution plan changed, or if the root cause is a missing index, a statistics update, or a parameter sniffing issue.
02 — THE RISK
AI recommendations without evidence are dangerous
Competitors are bolting AI onto monitoring and auto-generating fix scripts. In production SQL Server, an unverified index change or query rewrite can take a system down. You need proof before action, not suggestions from a black box.
03 — THE GAP
The diagnostic workflow lives in your head
Senior DBAs carry decades of triage knowledge. When they leave, that diagnostic process leaves with them. There's no system that encodes the investigation steps, captures the evidence, and produces a reviewable remediation plan.
Health Pulse, evidence context, and Sigma DBA in one operator view.
Sigma DBA Agent
A context-aware AI DBA agent built for governed production workflows
SigmaDbIQ assembles live evidence, reasons across SQL Server context, prepares a reversible remediation path, and keeps execution behind human approval.
Product Depth
A short path from pressure to approved action
SigmaDbIQ connects SQL Server symptoms to a traceable evidence path: cause, proof, reviewed recommendation, approval gate, and rollback plan stay visible before production changes move forward.
Prove
Evidence stays attached
SigmaLens™ keeps the cause, plan context, baseline delta, and confidence path together.
Govern
Action waits for approval
Recommendations are packaged with verification notes and rollback readiness before execution.
Evidence-First Architecture
Context before recommendations
Every signal in SigmaDbIQ carries its evidence chain. Before the Sigma DBA agent suggests action, it assembles target-specific context from each diagnostic layer so your team can verify the reasoning.
OK
Target registry and scope
Instance, database, workload role, and time window stay attached to the finding.
OK
Query Store runtime, waits, and plans
Baseline deltas, plan changes, runtime history, and wait context are collected together.
OK
Regression z-scores and variance bands
Six Sigma-style scoring separates normal movement from meaningful regression.
OK
Live DMV diagnostics
Current blocking, memory grants, tempdb pressure, and active sessions add operational context.
OK
Reviewed remediation path
Recommendations stay pending until a human reviews the evidence and approves action.
SQL-PROD-AG01
LIVELive evidence pathSignal to approved action
Live evidence
Wait spike + plan changed
Query Store runtime, waits, current sessions, and tempdb pressure stay attached to the target.
SigmaLens™ proof
+38% regression
Six Sigma-style variance bands separate normal workload movement from a material regression.
z-score 2.4Query 5
Sigma DBA Agent
Recommendation prepared
Root cause, impact, rollback path, and verification notes are packaged for human approval.
Approval pendingNo AI black box production changes
RollbackReady
VerifierAttached
ChangeNot run
Audit trailEvidence retained for review
Evidence packAttached before any recommendation
1
Query Store baselineRuntime, reads, CPU, waits, plan history
Compared
2
Regression proofVariance bands, z-score, baseline delta
Scored
3
Plan and wait contextPlan delta, wait profile, pressure source
Correlated
4
Live DMV contextSessions, blocking, grants, tempdb pressure
Current
5
Action governanceApproval state, runbook, rollback path
Pending
Platform
Six capabilities. One diagnostic workflow.
Every module feeds evidence into a single decision pipeline — from first symptom to approved remediation.
Health Pulse
Composite health scoring across waits, pressure, tempdb, storage I/O, regressions, and workload risk. Your first-look triage view that separates noise from signal in seconds.
Live Operations
Real-time surface for current sessions, blocking chains, wait pressure, and workload behavior. See what's happening now, not what happened five minutes ago.
SigmaLens™ Regression Analysis
Six Sigma variance bands, z-scores, and Query Store baselines pinpoint exactly which queries regressed and by how much — with statistical confidence, not gut feeling.
Tuning Workbench
Query tuning, index analysis, and execution plan review — guided by evidence from SigmaLens™. AI assists only where the data already points, never from guesswork.
Sigma DBA Agent
A senior DBA reasoning engine that answers against your target, cites evidence, declares what data is missing, and separates diagnostic guidance from general knowledge.
Consultant Reports
Browser-native HTML dashboards with executive summaries, DBA findings, and prioritized remediation plans. Publish internally or hand directly to clients.
Methodology
DMAIC for SQL Server remediation
Monitoring and alerts are table stakes. SigmaDbIQ adds Six Sigma operating discipline to database performance: define the pain, measure variance, analyze evidence, improve through reviewed action, and control with traceable follow-up. It is manufacturing defect discipline applied to query regressions.
D
Define
Scope the target. Register your SQL Server instance and establish baseline performance boundaries.
M
Measure
Capture Query Store runtime, waits, plans, and baselines. Calculate z-scores and regression deltas.
A
Analyze
Identify root cause through SigmaLens™ variance analysis, plan comparison, and wait-type correlation.
I
Improve
Generate evidence-backed remediation: index DDL, query rewrites, configuration changes — all reviewed before execution.
C
Control
Track the fix. Verify regression resolved. Maintain the evidence trail for audit and knowledge retention.
How SigmaDbIQ compares
SigmaDbIQ includes the SQL Server monitoring and observability teams expect, then carries each signal into evidence-backed diagnosis, reviewed remediation, DMAIC workflow, and portable consultant reporting.
| Capability | SigmaDbIQ | SolarWinds DPA | Redgate Monitor | Idera SQLdm |
|---|---|---|---|---|
| Statistical regression detection (z-score) | ✓ | ML anomaly | Baselines | Alerts |
| Evidence-backed remediation plans | ✓ | — | — | — |
| Human-approval workflow | ✓ | — | — | — |
| Query Store deep integration | ✓ | ✓ | Partial | ✓ |
| Execution plan change detection | ✓ | ✓ | Partial | ✓ |
| Portable HTML consultant reports | ✓ | — | — | — |
| DBA agent with evidence citations | ✓ | AI Assist | Monitor AI | AI 14.0 |
| DMAIC structured workflow | ✓ | — | — | — |
Stop guessing.
Start proving.
When SQL Server performance becomes business risk, SigmaDbIQ gives your team the evidence path: what changed, why it matters, and what action is safe to approve.
Early access onboarding | guided setup | SQL Server evidence review | Schedule in-person demo
Current pilot participants
GE HealthCare
Blue Cross Blue Shield Foundation
Army & Air Force Exchange Service
City of Dallas