AIOps Platform Engineering
We build observability and incident-response platforms that learn your stack, predicting failures before pagers fire and triaging noise into a focused signal.
Operations that learn, instead of paging.
Most ops setups are reactive: an alert fires, an engineer responds and the cycle repeats every quarter as systems get more complex. Our approach inverts that. We treat your telemetry as training data, building models that learn the rhythm of your stack, surface the unusual before it cascades and execute the runbooks your team already trusts. The result is an operations layer that gets smarter with every deploy, not louder.
Telemetry-first, not tool-first
We start with the signal you already produce, metrics, logs, traces and only add tooling where it earns its place.
Graduated autonomy
Models go shadow → human-approved → autonomous. You stay in control, with auditable hand-offs at every step.
Engineer-grade SLAs
Public response targets, post-incident reviews shared with your team and observable model behavior, no black boxes.
Why AIOps is on every CIO's 2026 board agenda.
Three forces are turning AIOps from a "nice-to-have" into a survival capability for any team running production at scale.
Microservice fan-out, multi-cloud sprawl and AI workloads have triggered an alert explosion legacy ops can't triage by hand.
Faster detection, measured in seconds, not minutes, directly preserves revenue. ML-driven triage is now the only realistic path.
Burnout is a hiring problem. Teams that automate the noise retain senior engineers; teams that don't bleed them quarterly.
AIOps Platform services we offer.
Each item below is a discrete, measurable workstream we own end-to-end, with senior engineers, real timelinesand the test coverage to back it up.
Anomaly detection at the metric level
Time-series models tuned on your seasonality, distinguishing real incidents from deploy traffic, cron jobs and weekend lulls.
Correlated incident triage
A single alert fires when 200 metrics flap together. We collapse storms into narratives your SREs can act on in seconds.
Predictive capacity planning
Forecast disk, memory and DB pressure 7–30 days out. Plan migrations on schedule, not when production catches fire.
Auto-remediation runbooks
Runbooks expressed as code. Roll back deployments, restart leaking pods, scale read replicas, without waking anyone up.
Root-cause graphs
Live service-dependency graphs that surface the most-likely culprit when incidents propagate across services.
Alert noise reduction
We typically cut alert volume by 60–80% in the first quarter, what stays is actionable.
We're fluent in your stack.
Vendor-agnostic by design. We pick the right tool for the problem in front of us, not the one our partner discounts apply to.
Real engagements. Real numbers.
Cut MTTR by 64% during peak trading hours
A capital-markets client routed every alert through our triage layer; weekend on-call paging dropped from ~40 a night to under 8.
Six reasons enterprises run AIOps Platform with Infivit.
Built for the 2026 reality of AIOps Platform: the actual buyer pain, the actual technical constraints and the actual outcomes that matter, not generic AI talking points.
90% fewer alerts, 100% of the signal.
AI correlation collapses 10,000 noisy events into 50 actionable incidents. Your SREs stop chasing flapping checks and start fixing real problems.
MTTR under 8 minutes, by design.
Auto-remediation runbooks handle the top 70% of recurring incidents before a human is woken up. The other 30% arrive with full root-cause already attached.
Capacity outages forecast 6 hours ahead.
Time-series and anomaly models predict disk, memory, latency and traffic pressure with hours of lead time. Plan, don't panic.
Sits on top of Datadog, Splunk, ServiceNow.
Stack-agnostic by design. We layer intelligence on what you already pay for and turn 12 dashboards into one narrative. No rip-and-replace.
Smart sampling cuts log spend 50%.
Dynamic retention, log-to-metric conversion and adaptive sampling. Same insight, half the Datadog or Splunk bill, every month forever.
Self-healing playbooks, chaos-tested.
Every runbook is exercised in chaos drills before it touches production. Auto-remediation that's audited, reversible and trustworthy enough to run unattended.
The questions you were already going to ask.
Got a aiops platform problem?
Let's ship the fix.
A 30-minute call with one of our senior engineers, no slideware, no scoping doc. You leave with a concrete view of what the first 30 days look like.