Autonomous infrastructure
investigation for
modern engineering teams.
Azhiru continuously reasons across deployments, logs, runtime systems, cloud infrastructure, and operational signals — detecting failures, investigating root causes, reducing cloud waste, and helping teams resolve incidents faster.
Operations is fragmented across a dozen surfaces.
Modern infrastructure produces more signal than humans can hold in their head. Engineers spend hours stitching tabs together to answer one question.
Dashboards show what.
Not why.
Charts confirm something broke. They never close the gap to cause. The reasoning still happens in human heads, on Slack threads, at 2am.
Logs are an ocean.
Not an answer.
Every system speaks a different log dialect. Correlating across them requires intuition no on-call engineer should need at 2am.
The graph lives
in tribal memory.
Who depends on what, what changed when, why this service is here — the model lives in senior engineers' heads. It does not survive their offboarding.
Dashboards show symptoms.
Azhiru investigates causes.
A reasoning loop that
never goes off-shift.
Azhiru sits next to your stack, not in front of it. The same four-step loop runs whether a human is asking or the system is watching.
Connect
Read-only adapters to your cloud, Kubernetes, deploys, logs, metrics, and runtime APIs. No agents required.
Model
Azhiru builds a live operational graph of every service, dependency, deploy, and signal across your stack.
Reason
Ask in plain English or let it watch. A multi-agent reasoning engine traces issues, anomalies, and waste autonomously.
Act
Proposed remediations, runbooks, and rollbacks — reviewed and executed inside the same conversation.
Watch Azhiru investigate a real incident. 42 seconds, end-to-end.
An engineer asks why users are getting 403 errors. The reasoning engine traces the request path, finds the anomaly, correlates the deploy, and identifies the root cause — autonomously.
Conversation
Reasoning Trace
Ingest signal
parse query intent → 403 errors
Map topology
load runtime graph @ t-15m
Probe edge layer
cloudflare.errors.4xx +312%
Trace ingress
k8s.ingress-nginx upstream=jwt-mw
Anomaly: jwt-middleware
p99 latency 12ms→480ms · err 4.1%
Correlate deploy
auth-svc@4f2a1c deployed t-23m
Diff JWT signer
kid="2024-q4" → kid="2025-q1"
Root cause
JWKS cache stale · key rotation missed
Operational intelligence,
in every register.
One reasoning engine covers detection, investigation, prediction, and cost — across every layer.
Runtime graph intelligence.
A continuously updated topology of every service, dependency, deploy, and signal — reasoned over, not just visualized.
GPU cost anomaly detection.
gpu-pool-3 · h100 · 14:22 UTC
Crash loop semantics.
Infrastructure drift.
Terraform says one thing. Production says another. Azhiru watches the delta.
Deployment memory.
Every change, every owner, every consequence. The graph remembers what your team forgot.
Operational feed.
A first-class CLI.
Because operators live in the shell.
Azhiru ships as a binary. Pipe it into your runbooks. Wire it into CI. Ask it questions over SSH. The same reasoning engine — read-only, scriptable, audit-logged.
A reasoning engine built
on top of your stack.
Azhiru never owns your data. It reads, models, and reasons — entirely inside your security perimeter.
Not a dashboard with a chat box.
A reasoning system, all the way down.
Every layer is built around an agent that thinks. Conversation isn't a feature — it's the primitive.
You query. It returns rows.
You ask. It investigates.
Built for teams
that cannot afford
to be wrong.
Azhiru runs read-only by default. Your data never leaves your perimeter. Every action is audited, reviewable, and revocable.
Read-only by default
Every adapter is scoped to read. Write operations require explicit human approval per action, with audit trail.
In-perimeter deployment
Self-hosted control plane. Bring your own model. Data never crosses your network boundary.
Reasoning is reviewable
Every conclusion includes its full trace — sources cited, signals correlated, hypotheses considered.
Granular RBAC
Scope reasoning, remediations, and connectors per team, environment, and namespace.
When reasoning is cheap,
the question becomes:
what should we ask?
Why is checkout latency up 12% since the Tuesday deploy?
What in production drifted from staging this week?
Which workloads are wasting GPU capacity right now?
Show me every deploy in the last hour that touched payments.
What changed before the EU-west p99 spike at 03:14?
Forecast our autoscaler cost if traffic doubles by Q3.
The operating system
for infrastructure operations.
Azhiru is in private beta with a small number of infrastructure teams. Request access to bring autonomous reasoning to your stack.