Incident and SRE assistance that cuts MTTR without the firefighting.
Correlate alerts and changes, retrieve runbooks, maintain the incident timeline, and draft updates while engineers control every production action.
Nothing is finalized until a human approves it.
Where the time and money actually go.
During incidents, responders search dashboards and chat, repeat diagnostics, miss context across tools, and later reconstruct an incomplete timeline.
On-call engineers, incident commanders, support and communications teams
Trigger to act: MTTR is rising, on-call load is unsustainable, incident communications are inconsistent, or complex services create too much alert/context switching.
The result you can model before you sign.
Illustrative only: 20 priority incidents/year × 30 minutes faster mitigation × $15,000 estimated business impact/hour = $150,000 annual exposure reduction. Impact/hour must come from the client’s incident history.
The outcome, plainly: Correlate alerts and changes, retrieve runbooks, maintain the incident timeline, and draft updates while engineers control every production action.
Inputs in. A cited, review-ready result out. Your expert decides.
A read-only, grounded incident-response agent. Every material fact is grounded in an allowed source and returned with its identifier, with no invented data.
Claude Sonnet 4.6 or GPT-5.6 Terra, selected on the client’s code/security golden set; deterministic scanners, policy rules and tests remain authoritative; use a smaller model for labeling only. Read-only tool adapters; sandboxed execution; static analysis / test framework.
- Alerts
- Telemetry
- Recent deploys/config changes
- Topology
- Runbooks
- Prior incidents
AI-Native, not autonomous. Judgment stays with your people.
The machine does the work; the human’s role narrows to the one thing that matters, judgment. That constraint is what makes it safe to deploy.
Engineers approve every production command, rollback, traffic change, data repair, and customer communication; destructive action is never autonomous.
A scorecard, not a demo. We baseline what breaks in production.
Every deployment ships with an evaluation suite. These are the numbers we baseline before launch and monitor after.
The category is crowded. Most of it isn’t built for your workflow.
Fixed-scope, tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in from day one. A price you own, not a subscription you rent.
It plugs into the stack you already run.
No rip-and-replace. Access is scoped to the minimum data necessary, isolated per tenant, and fully logged.
Transparent by design. The build price buys the workflow and the proof.
A fixed implementation fee plus a monthly bill that scales with volume and governance. No hidden seats.
- ✓ One process / scope
- ✓ Live workflow on your data
- ✓ Baseline evaluation suite
- ✓ Measured vs. current process
- ✓ Full scope & integration
- ✓ Human-review UI & audit trail
- ✓ Write-back to your systems
- ✓ Production evals & monitoring
- ✓ Multi-facility rollout
- ✓ Advanced security & compliance
- ✓ Custom control & escalation
- ✓ Dedicated evaluation program
25,000 to 1,000,000 security events per month; SIEM/export charges can dominate.
Planning assumptions, not vendor quotations. Your observability and other platform licenses are separate and owned by you. Figures confirmed during scoping.
“His vast knowledge of technologies and a natural problem-solving mindset consistently lead us through complex challenges with clarity and confidence.”
Questions serious buyers ask.
Does the AI act on its own?
No. Engineers approve every production command, rollback, traffic change, data repair, and customer communication; destructive action is never autonomous. The system drafts and recommends; a human approves every consequential action. Explicitly excluded: no autonomous production change or command; no claim of root cause without evidence; no secret exposure; no replacement for incident command.
How do you stop it inventing facts?
Every material claim is grounded in an allowed source record and returned with its source identifier. The system separates observed facts, model inference, and missing information, and routes to a human whenever confidence is low, evidence conflicts, or an adverse outcome is possible.
What does it cost to run each month?
A usage bill of roughly $1,550 to $18,200 per month (25,000 to 1,000,000 security events per month; SIEM/export charges can dominate), plus a $3,200 per month managed retainer for evaluation, monitoring and maintenance. Your existing platform licenses are separate and already yours. Exact figures are confirmed during scoping.
Do we need a ChatGPT or Claude subscription?
No consumer ChatGPT or Claude subscription is required for the production workflow. You need an approved API/cloud billing account. Workspace seats are optional for internal prototyping and administrator access.
How is this different from Datadog Bits AI?
Tools like Datadog Bits AI, New Relic AI, and Dynatrace Davis are broad platforms you adapt to. This is a fixed-scope implementation tuned to your systems and rules, grounded in your data, with the human gate and audit trail built in, and a transparent price instead of a seat subscription.
How long until it is live, and how do we prove it works?
This is an enterprise / regulated launch. We baseline MTTA/MTTR first, then measure against that baseline. You see the scorecard before expanding scope: the evaluation suite ships with the system, not as an afterthought.
Bring your real numbers. Leave with a fixed-scope plan.
A 30-minute engineering-led working session, no slideware. You leave with a sized opportunity estimate, a fixed-scope pilot plan, and the integration & human-review path mapped.
VP of Growth at ViitorCloud · senior delivery owner confirmed before paid work