Diagnosis is not the finish line.
A missing index recommendation, regression alert, or blocking diagnosis still has to become a safe production decision.
Strategic report
Beyond
Monitoring
Every serious database platform can detect a problem. That is not enough anymore. SigmaDbIQ is built by senior DBAs to govern what happens next: evidence-backed decisions, human-approved remediation, rollback-ready execution, and a complete record of what changed.
Full platform
Monitoring, diagnosis, DBA guidance, reports, and remediation workflow.
Statistical spine
SigmaLens uses variance and z-score methods to express regression.
Category bet
Governed AI Remediation Execution between DBA judgment and production.
Established platforms
Strong detection
Deep diagnostics
Dashboards
Alerts
Estate visibility
v
SigmaDbIQ
Governed AI Remediation Execution
^
General AI / LLMs
Summaries
Suggestions
Script drafts
Explanations
No production control
Evidence-to-Execution Pipeline
01
Detect
Continuous signal from SQL Server
02
Evidence
Correlated, proven, time-aligned
03
Decide
Policy-aware options and risk scoring
04
Approve
Human-in-the-loop with audit trail
05
Execute
Controlled remediation through runbooks
06
Verify
Post-change validation
07
Audit
Record of evidence, approval, and outcome
The real problem
Detection is solved. Production execution is still exposed.
The big vendors are good. They monitor, alert, diagnose, trend, and surface real SQL Server problems. But after a correct diagnosis, the riskiest step is still the human bridge between recommendation and production change.
AI advice without control creates new risk.
A plausible script is not a governed change. Someone still has to prove it, approve it, plan rollback, and verify the result.
The production-change record is usually scattered.
Evidence lives in monitoring screens, chats, tickets, scripts, and memory. SigmaDbIQ brings that chain into the product.
The AI execution problem
Ungoverned AI remediation is not a feature. It is a liability.
AI can explain waits, summarize a plan, and draft a fix. That is useful. But production systems need more than useful. They need target context, evidence, policy, approval, rollback, execution discipline, and verification.
No evidence trail
Advice without the exact supporting evidence becomes another opinion.
No approval workflow
Production change needs accountable review, not a copied script.
No rollback planning
Remediation should carry a reversal path before it touches production.
No target awareness
Your workload, baselines, Query Store history, and pressure pattern matter.
No retained record
Teams need to know what was recommended, approved, executed, and verified.
Evidence-grounded Sigma DBA
Answers cite the target evidence and state confidence limits.
Human approval gates
The person approving sees the evidence, expected impact, and rollback path.
Rollback-ready runbooks
Remediation is packaged as an operational workflow, not a loose suggestion.
Target-aware context
SigmaDbIQ reasons against the estate, workload, baseline, and evidence it sees.
Verification and record
The system retains the path from signal to outcome for teams and customers.
Six Sigma operating discipline
DMAIC thinking applied to SQL Server performance and remediation workflows.
The SigmaDbIQ difference
AI that shows its work. Remediation that waits for approval.
SigmaDbIQ is not a monitoring add-on. It is a full SQL Server performance intelligence platform built around Governed AI Remediation Execution.
Built by senior DBAs, it combines deep diagnostics, SigmaLens statistical regression intelligence, consultant-grade reporting, and an execution workflow that can carry a production change from evidence to verified outcome.
The difference is simple: SigmaDbIQ does not stop at "something is wrong" or "here is
a possible fix." It governs the path from DBA judgment to production execution.
Market position
From visibility to governed execution.
Monitoring, diagnostics, and alerting are table stakes. SigmaDbIQ is built to compete there, then carry the work into governed production execution.
| Capability | Mature monitoring platforms | Consultants / MSPs | General AI | SigmaDbIQ |
|---|---|---|---|---|
| SQL Server monitoring and diagnostics | Strong | Project based | Input dependent | Full platform goal |
| SigmaLens statistical regression intelligence | Varies | Manual analysis | No estate baseline | Core differentiator |
| Consultant-grade interactive HTML reports | Reporting exists | Manual deliverable | Narrative only | Portable live-feel artifact |
| Governed AI Remediation Execution | Adjacent / partial | Human-led | Suggestion only | Category center |
| Approval, rollback, verification, audit | Often external | Manual | Not a system of record | Built into workflow |
The gap customers feel is not visibility. It is the controlled path from a trusted diagnosis to a production change they can defend.
Consultant market
Reports should feel alive even when the connection is gone.
A consultant does not just need a monitoring screen. They need a deliverable. SigmaDbIQ Consultant Reports are HTML-based, interactive, and built to carry evidence, dashboards, findings, and remediation outcomes to a customer or executive audience.
Anonymized SigmaDbIQ evidence report
Open a customer-style HTML deliverable with executive and DBA views, evidence tabs,
charts, and remediation context.
Open report
Architecture
Purpose-built for governed decisions at scale.
The platform needs strong monitoring, but the center is the execution layer: evidence, decision, action, controls, and retained outcomes all tied together.
Connect to SQL Server estates
Instances, availability groups, cloud, VMs, containers, and locked-down networks.
SigmaLens evidence engine
Variance, z-score, baselines, Query Store, and confidence scoring.
Decision and execution engine
Risk-mapped options, runbooks, approvals, execution, and verification.
Governance, RBAC, and audit
Policies, separation of duties, retained records, and customer-ready evidence.
Experience
Dashboards that matter
Executive - Operations - Engineering - Compliance
Intelligence
Evidence Engine
Correlate - Validate - Time-align
Decision Engine
Policy - Risk - Prioritize
Execution Engine
Runbook - Remediate - Verify
AI Assist
Sigma DBA
Explain - Recommend - Prepare governed execution
Controls
Governance and audit
Policies - Approval - RBAC - Records
Data
Sigma data fabric
Telemetry - Metadata - Query Store - Logs - Change history
Turn diagnosis into governed execution.
Mature monitoring tells teams what is wrong. General AI can suggest what to try. SigmaDbIQ is built to govern the production path in between.