AI-native IT operations

Watch every system. Drop the legacy weight.

Argus tracks incidents, changes, on-call, and postmortems with AI classification, similar-incident search, drafted postmortems, and plain-English search. Audit trail on every entity.

Argus on watch. Reading the trail.1,247 signals last hour · 3 elevated · 5 needs your nod
14:11

Argus

I noticed checkout queue depth climbing past 4k. Opened INC-2104 — classified P1.

Confidence 0.87Needs your nod
14:08

Argus

Two from Q4 ring the same bell — INC-1987 and INC-2042. Same service, same hour, same shape.

Confidence 0.78
13:58

Maria

joined the watch.

13:55

Argus

I closed INC-2099. Memory pressure resolved after gc cycle completed. 19m resolution.

Confidence 0.94Safe-to-auto

Argus · needs your nod

I'd like to throttle Stripe webhook retries to 30/min. That should clear the backpressure without dropping callbacks.

Confidence 0.91Needs nodI'll do it in 1:28 unless you stop me
What I'm doing

Four things I'm doing while you're asleep.

I'm not a chatbot bolted on the side. I work inside the operations workflow — classifying, drafting, searching, surfacing the past incidents that ring the same bell. Every read I give you carries confidence and sources.

I read every alert and call the severity.

The moment an alert lands, I read the title, the description, and the systems involved. I label it P1–P4 and tag the surface affected — before anyone gets paged.

"Checkout 503s climbing"
→ P1 · payments-api · customer-facing

I remember every incident your team has resolved.

Vector embeddings on every postmortem and known error. When a new alert lands, the runbook from three months ago is one keystroke away — not buried six clicks deep.

3 similar: INC-1284, INC-998, INC-742
→ known-error KE-12 · workaround attached

I write the postmortem so you can review it.

Once a thread closes, I pull the timeline from comments, alerts, and the trail into a draft. Yours to revise — never written from scratch.

Timeline · Root cause · Contributing factors · Action items
→ draft ready in 8s · review and publish

Ask me anything in plain English.

"All P1s in payments last quarter." "What ran on the gateway during the 3am page?" I read it. No query DSL, no filter pyramid.

"show me on-call escalations that paged twice last week"
→ 4 results · grouped by team · with timeline
Why teams switch

What I replace.

The legacy ITSM stack is an artefact of pre-AI workflows. I'm built for the way IT teams actually work in 2026 — keyboard, trail, and an agent on the watch from day one.

Legacy ITSM

The old way

  • Six-figure annual licensing
  • Two-month implementation; another two for changes
  • Workflow editor that needs specialist setup
  • Search that returns nothing useful
  • Postmortems written from scratch every time
  • Audit trail toggle that was off when you needed it
Argus

My way

  • Modern pricing, no implementation fee
  • I am on watch by the afternoon
  • Sensible defaults; configure in YAML when you need to
  • Vector search on incidents; plain-English search across every entity
  • I draft the postmortem from the timeline, every time
  • I log every CREATE / UPDATE / DELETE to the trail, automatically
Common questions

Things people ask before signing up.

Is this another wrapper around a chatbot?

No. Argus works inside the operations workflow: it classifies severity, surfaces similar past incidents, drafts postmortems, and presents every suggestion for operator approval before anything commits. Humans stay in the decision loop.

What does Argus replace?

Heavy legacy ITSM suites, lightweight ticketing tools you have outgrown, and the home-grown incident workflows every team starts with. Argus brings ITIL-aligned process discipline without the weight.

Can we self-host?

Self-hosted deployment is available on Enterprise today; managed cloud is the default. Argus is built for teams that need control over data residency, identity, and operational history.

How accurate is the AI classification?

AI output is a suggestion, never a side effect. Severity classification, similar-incident matches, and postmortem drafts are presented to the operator for review. We will publish accuracy benchmarks alongside the public release.

Will my data be used to train models?

No. Tenant data is never used to train models. Your operational data stays under your control, and AI-assisted suggestions are handled under zero-retention commitments where applicable.

Do I need to configure a CMDB before I can use Argus?

No. Argus discovers assets, services, and relationships incrementally. Start with one incident; add structure as you go.

Can I bring my own LLM provider?

Yes. Argus uses Pydantic AI under the hood. Swap providers from the admin panel's service-config screen without code changes — OpenAI, Anthropic, self-hosted, or your enterprise gateway.

When do you launch?

No fixed date. The waitlist is for early access and design partners. We will write when there is something to share, not before.

Pricing

Simple, transparent pricing.

Per-seat, monthly, no surprise overages. Start free, upgrade when your team grows.

Enterprise

Custom

Self-hosted deployment, regulated industries, or a tailored contract? Let's talk.

  • Everything in Team
  • Self-hosted / VPC deployment
  • Custom contracts & DPA
  • Dedicated support
Contact sales
Waitlist

Want me on your watch?

Early access for IT teams, SREs, and incident responders who want an agent on the watch — not another tracker. No countdowns, no spam. I'll write when there's something to share.

We don't sell data. Unsubscribe anytime. One email per major update — that's it.

Curious where I reach? Security posture, API & integrations, blog.