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DB AI Ops

From P1 alert to ready-to-ship fix — without waking the DBA.

Database AI Ops turns observability signals, tribal knowledge, and schema context into safe, prescriptive actions your team can actually ship.

What it is

Autonomous database operations, on by default.

An AI operations layer that sits across your database fleet, orchestrating triage, diagnosis, and remediation. It ingests alerts from Datadog, CloudWatch, and ServiceNow, correlates them with telemetry from RDS Performance Insights and Postgres internals, and proposes fixes validated against your historical playbooks in Confluence, Notion, and SharePoint.

Why it matters

The hard part isn't the model — it's the workflow.

DBA teams spend 60–70% of their time on work the data already knows how to solve — the same missing index, the same autovacuum stall, the same connection-pool exhaustion. Database AI Ops compresses that loop from hours to minutes and lets one DBA oversee a fleet that used to require five.

What's included

Unified alert triage across Datadog, PagerDuty, and ServiceNow
Autonomous root-cause agents for RDS, Aurora, and self-managed Postgres
Prescriptive remediations with DDL previews and rollback plans
Change safety: shadow-run on replicas before merge
Self-updating runbooks fed by every incident postmortem
Fleet-wide policy guardrails and human approval gates

Real-world scenarios

How enterprises deploy this service to solve specific, high-stakes problems.

E-commerce

Global retailer held Black Friday p95 under 400ms with no DBA page-outs

During their peak Black Friday window, a $4B e-commerce platform handled 12× normal load. Database AI Ops caught a runaway query plan regression at 02:11 local time, proposed a concurrent index, shadow-validated it against replica traffic, and shipped a pre-approved PR within 7 minutes — all without paging the on-call DBA.

SaaS

B2B SaaS cut MTTR from 3.5 hours to under 9 minutes

A multi-tenant SaaS platform was seeing nightly performance regressions tied to customer onboarding spikes. After deploying Database AI Ops, 89% of incidents were resolved autonomously, and DBA team velocity on roadmap work increased 3×.

Financial services

Regional bank eliminated weekend on-call pages by 94%

Legacy workload bursts from batch reconciliation were triggering chronic weekend P2 pages. The AI Ops agent now auto-scales read replicas, pre-warms caches, and suppresses false-positive alerts with full audit trail for the compliance team.

How it works

1

Assess

Inventory your database estate, alert sources, and current runbooks.

2

Integrate

Connect telemetry, knowledge bases, and code repos via read-only integrations.

3

Pilot

Run in advisory mode against one workload — agent proposes, humans approve.

4

Scale

Graduate high-confidence flows to autonomous execution with policy guardrails.

Typical outcomes

92%
reduction in time from alert to root cause
4.8×
faster remediation via prescriptive PRs
70%
fewer after-hours pages for the on-call DBA
Works with
PostgreSQLMySQLAuroraRDSDatadogCloudWatchServiceNowPagerDutyConfluenceGitHub

Why VS Tech

Prescriptive, not descriptive

We hand your team a reviewable fix, not a dashboard screenshot.

Safe by construction

Every proposal is simulated on a replica before it reaches a human.

Compounds over time

Each incident updates the playbook — tribal knowledge becomes institutional memory.

Ready to see DB AI Ops in your environment?

Book a 30-minute working session with our team. We'll walk through your stack, your pain points, and what a pilot looks like.