ZeroSev
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Your oncall copilot

By the time you're paged, the root cause is ready.

ZeroSev's always on call. It investigates alerts the moment they fire, builds a root-cause hypothesis, and drafts the fix — so your first human minute is reviewing, not reading dashboards.

See it in action

minutes to value · stack-neutral · human-in-the-loop

zerosev / investigation / inc_2kq9
critical
Incident

API latency spike — checkout flow degraded

detected 14:06:22 UTC
investigating · 38s
TOP HYPOTHESISconfidence 0.82

Downstream users-svc degraded following deploy v2.14.0. Checkout retries are amplifying the backlog.

Evidence
  • metricapi p99 latency
    12.4s+840%
  • logusers-svc errors
    2,103/minspike 14:02
  • deployusers-svc v2.14.0
    14 min agosuspect
Suggested action
Roll back users-svc to v2.13.7
What ZeroSev does

Four jobs, one investigation.

Detect, diagnose, act, learn — ZeroSev closes the loop on every incident, not just the detect-and-page half your current stack covers.

Detect

Alerts flow in from your existing paging and observability stack. ZeroSev picks them up the instant they fire — no lag, no dropped signals.

Diagnose

Reads your logs, metrics, traces, and deploys. Forms a hypothesis, verifies it against evidence, and cites its sources.

Act

Proposes a corrective action for your review. You apply it, or ZeroSev does — up to the autonomy threshold you set.

Learn

Every incident becomes institutional memory. The knowledge graph grows sharper — so the next time it happens, ZeroSev remembers.

How it works

From page to proposed fix — before your team joins the call.

The moment an alert arrives, ZeroSev is already investigating — so by the time the first human joins the call, the evidence is gathered and the hypothesis is cited.

01→

An alert fires

PagerDuty, Datadog, Grafana, or any webhook. ZeroSev is already listening — no engineer needs to wake up first.

02→

Builds context

Pulls logs, metrics, traces, and recent deploys. Cross-references your service catalog to start the investigation with full context.

03→

Proposes a fix

A root-cause hypothesis with cited evidence and a draft corrective action. Ready for your review in seconds.

04

You stay in the loop

Approve, edit, or chat. ZeroSev can auto-apply within the autonomy level you set — escalates everything else.

Inside step 02 · Builds context

An SRE that actually knows your systems.

ZeroSev's service catalog maps your services, dependencies, datastores, and recent deploys. When an alert fires, that catalog feeds the investigation with context — so ZeroSev isn't starting cold.

  • Seamless onboarding. Connect your stack, grant read access, and the graph populates on its own.

  • Minutes, not months. No baseline-learning period. Ready to respond to its first incident on day one.

  • Grows with every incident. Root causes and resolutions feed back into the graph as institutional memory.

knowledge graph · live7 services · 12 edges
apiusers-svcorders-svcpostgresredisv2.14.0
suspectdatastoredeploy eventupdated 2s ago
Inside the agent

Every incident, a full investigation report.

Not an alert summary. A complete, cited investigation you can interrogate — and a proposed fix you actually trust.

zerosev / investigation / inc_2kq9
critical
Incident

API latency spike — checkout flow degraded

detected 14:06:22 UTC
investigating · 38s
TOP HYPOTHESISconfidence 0.82

Downstream users-svc degraded following deploy v2.14.0. Checkout retries are amplifying the backlog.

Evidence
  • metricapi p99 latency
    12.4s+840%
  • logusers-svc errors
    2,103/minspike 14:02
  • deployusers-svc v2.14.0
    14 min agosuspect
Suggested action
Roll back users-svc to v2.13.7
follow-up · inc_2kq9
you
Why are you suspecting users-svc? Could it be postgres instead?
zerosev
p99 on users-svc tripled after the 14:02 deploy. Postgres connection count is flat and query p95 hasn't moved. Pointing you to the users-svc change log:
users-svc commit · 7c4a1f2 · "retry loop on 503"
you
Ok, roll back and open a ticket on the retry logic.

  • Cited evidence.

    See the exact log lines, metric windows, and trace spans ZeroSev used to form its hypothesis. No black boxes.

  • Ask follow-ups.

    Ask "why?" Ask "what if?" Ask "check the last deploy." ZeroSev answers in the context of the live investigation.

  • Human sign-off.

    Nothing runs without your sign-off — unless it's below the autonomy threshold you set.

The ZeroSev difference

What the vendor-bolted agents won't give you.

Portability

Any stack. Any cloud. Any model.

Bring your own model. Use your existing observability. Run on any cloud. ZeroSev is vendor-neutral by design — we work with what you have, not what we want to sell you.

Meet your stack where it is. Bring your own model. Own your data.

Observability
DatadogGrafanaNew RelicSplunkHoneycombPrometheus
Paging
PagerDutyOpsgeniexMatters
Comms
SlackTeams
Cloud
AWSGCPAzureKubernetes
Models
ClaudeGPTGeminiLlamaself-hosted
Built differently

Every other agent makes you pick a side.

Vendor-bolted AI lives inside one platform. Workflow tools orchestrate but don't investigate. ZeroSev does both — without the lock-in.

Where it lives
Vendor AI
Inside one vendor's platform
Workflow tools
On top of your incident process
ZeroSev
Stack-neutral — connects to your existing tools
Time to value
Vendor AI
Weeks of baseline learning
Workflow tools
Immediate, but shallow
ZeroSev
Minutes, with a live knowledge graph
Autonomy model
Vendor AI
On or off
Workflow tools
Human-driven only
ZeroSev
Tunable threshold, human-in-the-loop
Investigation depth
Vendor AI
Alert summaries
Workflow tools
Orchestration + retros
ZeroSev
Cited root cause + conversational follow-up
Your data
Vendor AI
Theirs to see
Workflow tools
Theirs to see
ZeroSev
Yours — SaaS or self-hosted, bring your own model

Your pager shouldn't run your weekends.

Let ZeroSev take the first look at every alert. Your team arrives with a cited root cause and a proposed fix — not a wall of dashboards.

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