SERVICES · AIOPS

AIOps

AI and automation that detect, understand and resolve operational issues faster, at enterprise scale. We turn the flood of logs, metrics, alerts and cost signals from your cloud and data platforms into a few incidents that matter — and fix most of them before anyone is paged.
OVERVIEW

Fewer alerts.
Faster resolution.

Operations teams drown in signals: thousands of alerts a day, most of them noise, and the real incident buried somewhere in between. XEqualTo applies machine learning and agentic automation to your telemetry — correlating events across cloud, data platform and applications, detecting anomalies before they become outages, and pointing at the likely cause rather than the symptom.
Where the fix is known, an agent runs it with the guardrails you set. Where it isn’t, the on-call engineer gets one incident with context instead of forty alerts without it.
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WHERE YOU ARE
WHERE YOU LAND
Thousands of alerts, most of them noise
Outages found by users
Hours spent finding the cause
Runbooks executed by hand at 3am
Cost, performance and reliability in separate tools
OUR CAPABILITIES

Everything it takes to run
operations with AI, not just alerts.

Anomaly detection & early warning

Machine learning over metrics, logs and cost signals that learns normal for every service, warehouse and pipeline — and flags drift before it becomes an outage or an invoice surprise.

Event correlation & noise reduction

Alerts from cloud, data platform and application monitoring correlated into a small number of incidents with the affected services, owners and blast radius attached.

Root cause analysis

Incidents arrive with the likely cause — a deploy, a schema change, an upstream failure, a cost spike — traced across telemetry and lineage rather than guessed from a dashboard.

Agentic remediation

Known fixes — restart, rerun, scale, roll back, isolate — automated by agents with approval gates and rollback, so the on-call engineer handles the novel cases and the agent handles the rest.

Governed automation

Least-privilege actions, approval policies per action type, full audit trail and reviewable traces, so security and operations can sign off on automation in production.
OUR APPROACH

Connect, learn,
automate, expand.

AIOps only works on top of telemetry you trust and runbooks you understand. We start by connecting the signals, let the models learn what normal looks like, automate the fixes with the clearest payoff, and expand from evidence.
01
Weeks 1–3

Connect

Integrate logs, metrics, traces, alerts and cost data from your cloud, data platform and application monitoring into one operational view. Inventory existing runbooks and the incidents that consume the most engineer time.
Telemetry integrationAlert inventoryRunbook auditIncident baseline
02
Weeks 3–6

Learn

Train anomaly detection on your baselines, correlate alerts into incidents, and validate root-cause suggestions against historical outages with your engineers. Tune until the signal-to-noise ratio is one your team trusts.
Anomaly modelsCorrelation rulesRCA validationNoise reduction
03
Known fixes first

Automate

Automate the remediations with the clearest payoff — reruns, restarts, scaling, rollbacks — as agents with approval gates and full logging. Shadow mode first, then production, one action type at a time.
Remediation agentsApproval gatesShadow modeAudit trail
04
On evidence

Expand

Track MTTD, MTTR, alert volume and engineer hours per incident. Expand automation to new action types and systems as the numbers prove it, and hand the platform to your team or run it as a managed service.
MTTR & MTTDNew action typesManaged serviceTeam enablement
TECH STACK WE USE

Operating the platforms
your business runs on.

AWS, Azure, Google Cloud, Snowflake and Databricks — our engineers run these platforms daily, so detection and automation are built by people who know how they fail.
AWS
Databricks
Microsoft Azure
Snowflake
Google Cloud
Power BI
Tableau
dbt
QUESTIONS

Things people ask
before automating operations.

Not sure whether AIOps fits your monitoring stack? Talk to us directly — we would rather answer it properly than guess at it.
Contact us
Monitoring collects signals and fires alerts. AIOps sits on top: it correlates those alerts into incidents, detects anomalies your thresholds miss, suggests the root cause, and automates the fix. We integrate with the monitoring you already have.
Cloud infrastructure on AWS, Azure and Google Cloud; data platforms including Snowflake, Databricks and Airflow; and application telemetry from your existing observability tools.
Every automated action has a permission scope, an approval policy and a full audit trail. We run in shadow mode first, automate one action type at a time, and keep destructive actions behind a human approval by default.
Same platform, different job. Agentic AI builds agents for business workflows; AIOps applies agents and machine learning to running your cloud and data operations. Many clients start with one and add the other.
Yes. Cost is an operational signal like any other. Spend anomalies from our Quper platform feed the same incident view, so a runaway warehouse is caught the same way a failing pipeline is.
With a free operations assessment: we connect your telemetry, measure alert noise and incident load, and give you a plan for what to automate first within two to three weeks.

Ready for a free
operations assessment?

We’ll come back with your alert noise measured, top incidents mapped and a plan for what to automate first. No commitment beyond the conversation.

Contact us
Precision in Every Data Point
Newtown, Kolkata
One office, every time zone
connect@xequalto.com
+91 9147391433
OUR SERVICES
Data Engineering Data Architecture Agentic AI FinOps DataOps AIOps
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OUR PRODUCT
QUPER
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