Autonomous SRE · v0.1

Every AIOps tool tells you what's broken. Aizen fixes it.

Aizen is an autonomous SRE that detects, diagnoses, and resolves production incidents — usually within 5 minutes, before your team is paged. No more switching between five tools at 2 AM. No more "here's a hypothesis, good luck."

Book a 30-min call → How it works
Built with feedback from SRE leaders at
Meta NVIDIA IBM HP Chase Intuit Palo Alto Yahoo eBay Tangoe
Aizen's roadmap is shaped by the engineers who lived this problem at scale — across big tech, enterprise IT, security, fintech, and cloud.
A night your team knows too well

One expired certificate.
Two hours. Six engineers.

This happens 50–100 times a year at the average enterprise. The same failures, the same fixes, the same war rooms. The runbook exists — but at 2 AM, nobody remembers, and nobody coordinates.

2:47 AM
Database latency spike. Alerts fire across 3 monitoring tools simultaneously.
2:48 AM
6 engineers paged across 3 time zones. War room opens.
3:15 AM
Still jumping between Datadog, Splunk, Kubernetes. Nobody knows what changed.
3:40 AM
Senior SRE wakes up. Manually checks deploy history.
4:12 AM
Found it. One expired certificate.
4:47 AM
Fixed. This had happened before.
Cost: 2 hours · 6 engineers · 3 time zones disrupted · ~$400K–$10M depending on industry
The cost of downtime

Every minute offline has a line-item price.

Hourly downtime cost varies significantly by company size and industry. The numbers below come from public industry research.

Mid-market enterprise
$200K–500K
per hour
500–5,000 employees · SaaS, tech, e-commerce, insurance
Large enterprise
$1M–5M+
per hour
Retail, healthcare, manufacturing, government, media
Banking & fintech
$5M+
per hour
Financial services, payments, trading platforms
Sources: ITIC 2024 Hourly Cost of Downtime Survey (1,000+ firms) · Gartner 2024 · Siemens True Cost of Downtime 2024
The three problems Aizen solves

Stop switching tools. Start resolving incidents.

01 · Unification

Stop juggling five tools.

Datadog. Splunk. PagerDuty. K8s. Slack. Runbooks in Confluence. Aizen replaces the juggling with a unified workflow. Your SREs stop context-switching and start resolving.

02 · Resolution

Diagnosis isn't enough.

Every AIOps tool detects and correlates. None of them push the fix button. Aizen does — autonomously for low-risk actions, with single-click approval for high-risk. Always with rollback.

03 · Visibility

Everyone sees what they need.

Engineers get unified telemetry. Leadership gets incident cost in dollars, not graphs. Customers get an honest, real-time status page. No more waiting for a post-mortem to know what happened.

The core insight
~0%

of production incidents are repeated — the same failure, the same fix. Your engineers have solved these before. The runbook exists. The remaining 20% — novel, high-risk — stay with your engineers, with full AI-generated context to help them move faster.

0%+
Incidents resolved without human intervention
0%+
Of mid-size & large enterprises report $300K+/hr downtime cost
ITIC 2024
0%
Audit trail on every autonomous action
How Aizen works

From signal to resolution — without humans.

01

Observe

Aizen ingests logs, metrics, traces, and deployment events from Datadog, Splunk, Prometheus, CloudWatch. No new instrumentation. No agents. No code changes.

02

Diagnose

Builds causal incident graphs from service dependencies and deployment history. Root cause in under 5 minutes — vs. 30–45 minutes of manual context stitching today.

03

Fix

Pre-approved runbooks execute via K8s API, Terraform, cloud CLIs. Low-risk actions run autonomously. High-risk surface to your engineer with full context. Rollback on every action.

04

Learn

Automated postmortems. Runbook suggestions for novel incidents. Model accuracy improves with every resolution. The system gets smarter the longer it runs.

Aizen vs every other AIOps tool

Detection has been solved. Action is the gap.

Modern AIOps platforms are excellent at telling you what's wrong. None of them actually fix it. That's the line Aizen crosses.

Capability Existing AIOps tools
PagerDuty · Datadog · BigPanda · Moogsoft		 Aizen
Detect incidents ✓ ✓
Correlate signals across tools ✓ ✓
Suggest root cause ✓ ✓
Execute the fix ✗ ✓
Replace tool-switching ✗ ✓
Learn from every resolution partial ✓
Audit trail on every action N/A ✓
Visibility · for everyone who needs it

Engineers see signals. Leaders see dollars.
Customers see honesty.

Most platforms give one dashboard for everyone. Aizen gives each audience the view they actually need — without an engineer manually translating between them.

For SRE & Platform engineers

Unified incident view

  • · Logs, metrics, traces in one pane
  • · Causal graph for every incident
  • · Deploy history correlated
  • · Auto-generated runbooks
  • · Single-click rollback
For Engineering & Business leaders

Cost & impact, in plain English

  • · $ of revenue at risk, live
  • · MTTR trends over time
  • · Top 5 recurring incidents
  • · On-call hours reclaimed
  • · Board-ready monthly report
For your customers

Honest, real-time status

  • · Auto-updated status page
  • · Affected services & regions
  • · Plain-English explanations
  • · Real ETAs, not "investigating"
  • · No more silent outages
Integrations

Aizen sits on top of your existing stack.

Splunk
Datadog
PagerDuty
Kubernetes
AWS / GCP / Azure
ServiceNow
Incidents Aizen resolves
K8s pod crashes & restarts
Database failovers
Certificate expirations
Memory & CPU spikes
Service degradation
Pipeline failures
Deployment & security

Built for environments that can't tolerate risk.

✓Deployed in your VPC
✓Read-only telemetry access
✓Full audit trail
✓No data leaves your environment
✓Rollback on every action
CS
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[placeholder photo]
Why I built Aizen

I'm Chandni Singh. I've spent years leading SRE and platform teams — and watched the same pattern play out at every company I worked with.

We'd buy a new monitoring tool. Then another. Then an AIOps layer on top to "correlate." Every quarter, the toolchain grew. The dashboards multiplied. The alert noise got worse. And when something actually broke at 2 AM, my best engineers still spent the first 30 minutes figuring out where to look, not fixing the problem.

The insight that started Aizen was simple: SRE is the only engineering discipline where the AI tools stop at "here's a hypothesis." Coding assistants write the code. Sales tools draft the email. But incident response AI just hands you a Slack message and walks away. That gap is where the 2 AM pages live. That gap is where Aizen plays.

I've been pressure-testing the product with SRE leaders at Meta, NVIDIA, IBM, HP, Chase, Intuit, Palo Alto Networks, Yahoo, eBay, and Tangoe. Their feedback hardened the design choices that matter most — rollback on every action, read-only ingest, no data egress, single-click human override for high-risk fixes. The result is a system enterprise platform teams can actually deploy, not a demo that breaks at week three.

— Chandni Singh, Founder · Aizenops
Design partner program

Be one of three design partners this quarter.

Help shape what Aizen becomes. Get results first. Early participants get preferential pricing and direct roadmap input. We're onboarding three teams this quarter — teams who want to stop solving the same incidents twice.

What you need

1 platform engineer · 5 hrs/week · Read access to Datadog and PagerDuty · No code changes

What you get

30–50% MTTR reduction · 60%+ incidents automated · Executive dashboard · Full ROI report

Next step

30-minute technical deep-dive with your platform team. No commitment required.

Book a 30-min call →
Or email hello@aizenops.ai