Use Case · AIOps & SRE Automation
AIOps that actually stops the pages.
Anomaly detection, self-healing runbooks, GitOps drift control, and an AI SRE that triages incidents at machine speed. Yobibyte's automation surface plugs into your existing observability stack and learns from every postmortem.
-90%
Median MTTR on top incidents
-60%
Manual toil hours per quarter
85%
Alerts auto-triaged
24×7
AI SRE on rotation
Why teams struggle
The problems that block the work.
We hear the same pattern of failure modes across every engagement. These are the ones Yobitel exists to remove. Not generic platitudes, but the specific frictions that stall delivery.
Alert fatigue
Three thousand alerts a day, 90% noise. On-call engineers stop reading them. Real incidents are missed because the channel is permanently red.
MTTR stays flat
Detect, page, escalate, find the runbook, read the runbook, copy the kubectl, fix. Hours per incident. Postmortems pile up. The same root cause recurs.
Toil eats SRE capacity
Certificate rotations, capacity bumps, RBAC tweaks, drift remediation, log archive cleanup. Highly paid engineers doing what scripts should.
Scattered runbooks
Half on Confluence, half on a fired engineer's laptop, the critical one only Devi knows. No structured execution, no audit, no reusable steps.
What Yobitel delivers
The capabilities we ship, end to end.
Each capability is a first-class product surface, not a slide. They compose into the platform behind every Yobitel customer in production.
Anomaly detection on signals
Forecast-and-deviate on metrics, log embedding clustering for new error classes, and trace-based latency anomaly detection. Tuned per service.
Self-healing runbooks
Runbooks declared as code, gated by policy, executed by an agent with kubectl/terraform/ansible/k8s-api tools and full audit logging.
AI SRE triage
On every page: correlated traces, candidate root cause, suggested runbook, and a single-click execution path. The pager becomes a worklist.
GitOps drift detection
Continuous reconciliation across clusters, clouds, and edge fleets. Drift surfaced as a PR diff, not a 2 AM incident.
Alert correlation & dedup
Cluster alerts by topology, blast radius, and embeddings. One incident, one ticket, one channel — even when 400 alerts fire.
Change risk scoring
Before every deploy, the agent scores risk against recent incidents, blast radius, and SLO headroom. High-risk changes get extra eyes.
Conversational ops
Slack and Teams plug-ins let on-call engineers query metrics, run sanctioned actions, and capture decisions back to the runbook automatically.
Postmortem assist
Auto-drafted timelines, contributing-factor analysis, and action-item generation linked to runbook updates and policy changes.
How adoption unfolds
From pilot to production, step by step.
The typical adoption path. We compress it where you have momentum and we slow it down where compliance or change-control demand it.
Ingest signals
Connect Prometheus, Loki, Tempo, Datadog, Splunk, CloudWatch, or any OTel-compatible source. We baseline within hours.
Tame the alert stream
Correlation + dedup rules cut signal-to-noise. Highest-burn services targeted first. Median noise reduction lands in week one.
Codify runbooks
Convert top-10 incident classes into versioned, policy-gated runbooks executed by the AI SRE — with humans-in-the-loop at first.
Hand off to AIOps
Promote runbooks to auto-execute under guardrails. The agent owns the first response; humans own the exceptions.
Close the loop
Every postmortem feeds runbook updates, policy tweaks, and risk-score retraining. The system gets quieter and faster every quarter.
The Yobitel stack behind this
Products & services that do this work.
No abstractions, no hand-waving. Each item below is a real Yobitel product or service with its own documentation, pricing, and SLA.
Yobibyte Observability
OTel-native traces, metrics, logs, and the unified query layer the AI SRE reasons over.
Yobibyte Automation
Runbook engine, agent runtime, policy gates, and the tool catalogue the AI SRE executes against.
GPU Orchestration
GitOps drift detection and reconciliation across GPU clusters, including spot reclaim and node lifecycle.
InferenceBench
Continuous evals on the AI SRE itself — every model upgrade gated on accuracy and false-positive rates.
Managed Ops
Optional co-pilot: Yobitel SREs on rotation alongside the AI SRE during ramp-up.
Outcomes we measure
The numbers customers report back to us.
Aggregated medians across recent deployments. Specific outcomes depend on workload and starting baseline. We'll model yours during the first conversation.
90%
Reduction in median MTTR on top incident classes
60%
Less manual SRE toil per quarter
85%
Of pages auto-triaged before a human reads them
3×
Faster postmortem-to-action-item closure
Customer story
APAC fintech, 600-service platform
Cut Sev-2 MTTR from 47 minutes to under 5 in two quarters. On-call pages down 71% with zero increase in missed-incident rate.
The first night the AI SRE auto-rolled-back a bad config push at 2:14 AM, nobody got paged. That was the moment we knew.
Where this lands
90%
Reduction in median MTTR on top incident classes
60%
Less manual SRE toil per quarter
85%
Of pages auto-triaged before a human reads them
Other use cases
Explore the rest of the solution suite.
Enterprise AI Operations
Deploy AI at Scale
Multi-tenant model serving, GPU fleet orchestration, governed rollouts, and end-to-end cost attribution — on one platform. Move from notebooks to a hardened control plane with model registry, canary deploys, and per-tenant FinOps built in.
ExploreInfrastructure Modernisation
Modernize Data Centres
Refit aging facilities into AI factories without ripping out what works. Yobitel engineers retrofit cooling, fabric, and orchestration around your existing footprint — then layer GitOps and platform tooling so the new estate runs itself.
ExploreApplied AI Engineering
Build AI Applications
Yobitel ships a complete app-building stack: typed SDKs, RAG primitives, agent orchestration, embeddable UI, and one-click deploy onto Yobibyte. Your product team focuses on the experience — we handle inference, observability, and the unglamorous middle.
ExploreReady to put this into production?
Talk to a Yobitel engineer. We'll map your environment, sketch the architecture, and propose a 60–90 day plan to first measurable outcome.