Get Started with your requirements and primary focus, that will help us to make your solution
We build AIOps and AI SRE agents that detect, correlate and diagnose incidents across your stack, then run approved remediations within limits you set. Every action is checked before it runs and recorded after.
Trusted on five-year contracts by a leading Indian bank and a Dubai government entity.
AIOps (artificial intelligence for IT operations) uses machine learning and AI to analyze logs, metrics, traces and events across IT systems. It reduces alert noise, detects anomalies, finds root causes and automates responses, so operations teams resolve incidents faster and prevent repeat outages with less manual triage.
An AI SRE is an AI agent that does site reliability engineering work alongside human engineers. It investigates alerts, reads telemetry and recent changes, proposes or runs remediation runbooks, and updates tickets. Good AI SREs act only within approved boundaries and record every decision for review.
Most AIOps tools stop at insight: a cleaner dashboard and fewer alerts. The step that saves the most time is acting on that insight, and that is where agents need business context, permissions and an audit trail. That is the part we build.
Group alerts from every monitoring tool into one incident, with duplicates and noise removed.
Learn normal behavior per service and catch drift in latency, errors, saturation and cost before users notice.
Trace an incident through dependencies, deploys and config changes to a likely cause, with the evidence shown.
Turn your runbooks into actions agents can run: restart, scale, roll back, fail over, each within set limits.
Agents that take the first page, investigate, brief the human on call and handle routine fixes around the clock.
Spot idle, oversized and runaway resources, including GPU and model spend, and right-size within policy.
AIOps agents act on live systems. We build every one on decision infrastructure, so it knows the business impact of a service, is held to the limits you set, and leaves a record you can audit.
Service ownership, dependencies, SLOs, change windows and the business cost of downtime, kept current.
Each action is checked against policy before it runs: which services, which runbooks, what blast radius, who approves.
Runs the fix, updates the ticket and writes a decision trace: signals, reasoning, policy and outcome.
Every remediation path starts beside your on-call team. Decision traces prove accuracy before autonomy rises.
We start with the incidents that cost you most and earn autonomy runbook by runbook. Phase timing is scoped to your tooling and service map.
Review incident history, alert volume, tooling and runbooks. Pick the first targets.
Wire up telemetry, ITSM and the service map. Build the context graph.
Cut noise and add root-cause analysis. Value lands before any automation.
AI SRE proposes fixes beside your on-call team. Traces prove accuracy.
Raise autonomy where earned. Add runbooks, tune and report.
Datadog, Prometheus, Grafana, OpenTelemetry, Elastic and cloud-native monitoring.
We read your existing signals. No rip and replace.
PagerDuty, ServiceNow, Jira Service Management, Opsgenie, Slack and Teams.
Agents open, update and close tickets where your team works.
Kubernetes, Terraform, Argo CD, AWS, Azure, GCP, private cloud and on-prem.
Runs on ElixirClaw, ElixirData Context OS and NexaStack.
| Dimension | Traditional AIOps tools | XenonStack agentic AIOps |
|---|---|---|
| Output | Correlated alerts and dashboards | Diagnosed incidents and completed fixes |
| Root cause | Probable cause, left to engineers to confirm | Cause with evidence, plus a proposed runbook |
| Remediation | Scripts triggered by fixed rules | Agents choose runbooks within decision boundaries |
| Business context | Infrastructure topology only | Topology plus SLOs, owners, change windows and impact |
| Audit | Event logs | A decision trace for every action |
| Rollout | All-or-nothing automation | Shadow, supervised, bounded, then full, per runbook |
| Where it runs | Vendor SaaS | Any cloud, private cloud, on-prem or air-gapped |
Incident and alert analysis, runbook inventory and a prioritized automation plan. Scope and timeline are agreed per engagement.
Correlation, root-cause analysis and an AI SRE in shadow mode on your most critical services.
We run and extend your AIOps agents under SLA, with deployed engineers alongside your SRE team.
Building agents beyond IT operations? See our AI agent development services or go back to the XenonStack homepage.
AIOps is the practice of applying AI to IT operations data. An AI SRE is an agent that uses that analysis to do reliability work: investigating, deciding and running fixes.
No. We connect to the observability, incident and ITSM tools you already use and add correlation, diagnosis and action on top.
Only within limits. Agents start in shadow mode, every action is checked against policy and blast-radius rules before it runs, and higher-risk changes always need human approval.
Noise reduction and root-cause analysis can show value within weeks. Auto-remediation grows runbook by runbook as decision traces prove accuracy. Typical phase timing is confirmed after the readiness review.
Frequent, well-understood incidents with a clear runbook and a small blast radius, such as restarts, scaling and rollbacks on stateless services.
Yes. Agents and models can run in private cloud, on-prem or air-gapped environments, with data kept inside your boundary.
Against your own baseline: alert volume, time to detect, time to resolve, incidents handled without paging, and how often the agent's diagnosis was right.
Bring your last quarter of incidents. We'll show where AIOps cuts noise, which fixes an AI SRE could run, and the boundaries it would need.
AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability
Proactive detection of performance and availability issues
Root-cause analysis across microservices and environments
Automated remediation playbooks to reduce MTTR
AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly
Real-time detection of suspicious motion or intrusion
Natural language video search and instant playback
Smart summaries for audits, investigations, and compliance
Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics
Connects to warehouses, lakes, and streaming sources
Question-answering in natural language
Continuous monitoring for anomalies and KPI deviations
Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment
Automated diagnostics for recurring errors
Playbook execution: restart services, scale pods, clear queues
Feedback loop for improving remediation strategies
AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready
Continuous control checks across infrastructure and SaaS
Automated evidence collection for audits
Risk scoring and prioritized remediation recommendations
Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter
Real-time visibility into spend and commitments
Anomaly detection on invoices and vendor performance
Intelligent workflows for approvals and sourcing decisions
