AIOps consulting services
NextPage helps DevOps, SRE, and IT operations teams use AI safely across observability, anomaly detection, incident workflows, runbook automation, cloud cost signals, and human-reviewed remediation.
Built for
CTOs, DevOps leads, SRE managers, platform engineering leaders, and IT operations teams who have monitoring tools and incident rituals in place but need better signal quality, repeatable triage, safer automation, and clearer ownership.
A ranked AIOps roadmap that separates alert hygiene, telemetry readiness, incident workflow gaps, and safe automation candidates.
Incident support workflows that summarize context, correlate signals, recommend runbooks, draft updates, and keep remediation under human control.
Operations dashboards, audit trails, and improvement loops that make alert quality, triage time, automation impact, and cloud reliability easier to review.
Why this matters
The best outsourcing and software projects work because expectations, ownership, and delivery rituals are clear from the first week.
Monitoring tools are noisy, but alerts do not consistently map to user impact, service ownership, urgency, or a clear first action.
Incident triage still depends on senior engineers connecting logs, dashboards, deploys, tickets, and known runbooks under pressure.
Cloud cost, reliability, performance, and capacity signals live in separate tools, which makes patterns harder to spot before incidents escalate.
Teams want AI assistance, but they cannot allow unmanaged remediation scripts, vague recommendations, or black-box actions against production systems.
Post-incident learning is inconsistent because timelines, contributing signals, runbook gaps, and follow-up actions are not captured in a repeatable workflow.
Leadership needs a practical AIOps roadmap that improves operations without replacing the existing observability stack or bypassing human review.
What we build
We shape the scope around the result you need, the systems you already have, and the first release that can create value.
Review alerts, dashboards, logs, metrics, traces, ownership, SLOs, escalation paths, and incident history before deciding where AI automation belongs.
Plan signals that help teams spot unusual service behavior, cost movement, release impact, dependency failures, and recurring incident patterns earlier.
Build controlled workflows that gather context, classify incidents, suggest known runbooks, draft stakeholder updates, and route exceptions to the right owner.
Connect AIOps workflows to the systems your team already uses for logs, metrics, traces, tickets, deployments, cloud data, chat, and internal dashboards.
Use operations data to surface capacity, performance, reliability, environment, and cost patterns that deserve investigation before they become incidents.
Keep AIOps safe with permissions, action boundaries, review queues, rollback paths, quality checks, monitoring, and a support cadence after launch.
Technology stack
We choose AI tools around the workflow, data sensitivity, latency, model quality, integration depth, and operating cost. The result is an AI system your team can evaluate, monitor, and improve.
Model choices for copilots, agents, retrieval workflows, classification, and content automation.
OpenAI APIs
LLM products and assistants
Anthropic Claude
Reasoning-heavy workflows
Google Gemini
Multimodal AI features
Open models
Private and specialized use cases
Retrieval layers that let AI answer from your policies, product data, documents, and support history.
Vector search
Semantic retrieval
PostgreSQL
Structured business data
Document pipelines
Ingestion and chunking
Evaluation sets
Answer quality checks
Controlled automation that connects AI decisions to tools, APIs, approvals, and operational workflows.
LangChain
Agent and chain patterns
Tool calling
System actions and APIs
Workflow queues
Reliable task execution
Human review
Sensitive workflow control
The application layer that makes AI useful inside software people already use.
Next.js
AI-enabled web apps
Node.js
APIs and integrations
Python
AI services and data work
Docker
Portable deployments
Controls for cost, quality, permissions, auditability, and safe fallback behavior.
Prompt logging
Debugging and audit trails
Cost controls
Token and usage visibility
Guardrails
Policy and output checks
Playwright
User-flow regression tests
Additional capability for prediction, scoring, recommendations, analytics, and model-backed decisions.
Machine learning
Prediction and scoring
Analytics
Adoption and outcome tracking
Data pipelines
Reliable inputs
Model APIs
Reusable AI services
Delivery model
We keep discovery practical, ship in visible increments, and make ownership clear so you can scale with confidence.
We review alerts, incidents, observability tools, ticket flows, release history, cloud signals, runbooks, owners, and escalation paths.
We separate foundation fixes from AI-ready workflows by value, risk, data access, review needs, and expected impact on triage or operations effort.
We implement the retrieval, summaries, correlation logic, dashboards, integrations, approval states, tests, and audit trails needed for production use.
We launch with monitoring, runbooks, feedback loops, incident review, alert tuning, model or prompt updates, and a backlog for additional automation.
Engagement options
Choose the model that fits your current stage. We can start small, add specialists, or run a full product pod.
Best when you need to decide whether alert reduction, incident summaries, runbook suggestions, or cloud operations automation is ready for investment.
Best for one high-friction workflow such as alert triage, incident summarization, ownership routing, status updates, or runbook recommendations.
Best when AIOps becomes an ongoing program across observability, DevOps, cloud operations, service desk, dashboards, and continuous improvement.
Proof
NextPage is not starting from theory. The team has built and operated products, platforms, and internal systems with real users.
Maxabout: automotive platform with large-scale search traffic
NextBite: ordering workflows for food entrepreneurs
ChatRoll and OutRoll: communication and outreach products
FAQ
Clear answers help you understand how the engagement works before we get on a call.
AIOps consulting services help operations teams use AI and automation across observability, anomaly detection, alert reduction, incident triage, runbook recommendations, cloud operations signals, and production support workflows.
A team is usually ready when it has useful telemetry, alert ownership, incident records, runbooks, and clear review paths. If those foundations are weak, the first step is often alert hygiene and observability cleanup before adding AI automation.
It can help when alerts are mapped to service ownership, user impact, urgency, and known remediation paths. AIOps should reduce repetitive triage and grouping work, not hide noisy or poorly designed alerts.
We start with human-reviewed recommendations, summaries, runbook suggestions, and low-risk workflow automation. Production remediation needs scoped permissions, approvals, rollback paths, audit logs, and clear ownership before any action is automated.
AIOps workflows can integrate with observability tools, cloud platforms, ticketing systems, deployment pipelines, chat tools, databases, internal dashboards, and runbook repositories when APIs or safe data access are available.
DevOps consulting improves delivery, infrastructure, CI/CD, observability, and cloud operations foundations. AIOps builds on those foundations by adding AI-assisted signal correlation, incident context, runbook recommendations, and controlled operations automation.
Start with an AIOps opportunity assessment. NextPage reviews alerts, incidents, telemetry, cloud signals, runbooks, integrations, ownership, and risk controls, then recommends the narrowest workflow worth piloting.
Next step
Share your goal, current stack, deadline, and team gaps. We typically respond within 24 hours.
Use the project form first
The form captures your goal, budget, timeline, and service context so we can route the lead, prepare properly, and keep follow-up inside the pipeline.
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