AIOps
& Infrastructure
Reliable AI in production, with the visibility and cost control to keep it that way
From unpredictable AI workloads to operations you can forecast
We help you to:
See how your AI systems actually behave in production
Bring compute, model, and pipeline spend under control
Set runtime limits, fallbacks, and usage thresholds
Detect and resolve AI-specific failures quickly
Hold performance steady as models and usage change
AIOps Services
AI Operations Readiness Assessment
Get a review of what is running in production today: model usage, pipeline behaviour, spend patterns, and where visibility is missing.
AI Observability & Monitoring
Maintain a real-time view of how your AI systems perform: full visibility into model performance, latency, drift, failures, and behaviour across environments.
AI Cost & Resource Optimisation
Improve your pipelines, runtime patterns, and model usage to cut compute overhead, streamline resource consumption, and stop runaway spend.
AI Infrastructure & Deployment Pipelines
We build the layer underneath it all: container and GPU workloads, multi-agent orchestration, and hardened deployment pipelines.
Performance Tuning & Model Lifecycle Ops
Performance optimised across environments, new models and versions evaluated before they ship, regressions tracked, and stability maintained.
Guardrails & Runtime Policies
Runtime limits, fallbacks, access controls, and model usage thresholds, implemented so your systems stay stable and safe.
Incident Response for AI Systems
Get playbooks, escalation paths, and detection rules for hallucinations, degraded performance, unexpected outputs, and broken workflows.
Automated Quality Checks
We automate evaluations, build regression suites for model behaviour, and wire the results into your existing pipelines.
FAQs: AIOps & Infrastructure
What problems does AIOps actually solve?
AIOps addresses the operational problems that appear once AI reaches production: runaway costs, no visibility into what models are doing, reliability issues, unpredictable outputs, agent misbehaviour, and fragile pipelines. It puts the monitoring, controls, and automation in place so AI systems run efficiently and consistently. The outcome is an operational setup your teams can trust, without firefighting or surprise bills at the end of the month.
How is AIOps different from AI Governance?
Governance defines how AI should be used: policies, guardrails, and acceptable use. AIOps makes sure those systems actually run properly, covering performance, monitoring, automation, and stability. Governance protects your organisation; AIOps protects your operations. Most organisations end up needing both, and they work well in sequence.
How is this different from the DevOps and observability we already have?
Your existing stack was built for deterministic software, where a failure is usually an error, a timeout, or a spike in resource usage. AI systems fail differently: outputs degrade in quality without throwing errors, costs move with token usage rather than request volume, and agent workflows break several steps deep. AIOps extends what you have with AI-specific telemetry, evaluation, and detection rules. In most engagements we build on your existing tooling rather than around it.
When do companies typically need AIOps?
It becomes essential once AI leaves the prototype stage. If you are deploying AI to production, integrating agents, rolling out AI features, or scaling usage across teams, AIOps gives you the control, visibility, and reliability to operate at scale. The common trigger is the first production incident nobody could explain, or the first invoice nobody could account for. Both are easier to design for than to recover from.
Can AIOps reduce our cloud and compute costs?
Yes, cost optimisation is a core outcome rather than a side effect. We look at model usage patterns, container workloads, GPU utilisation, orchestration inefficiencies, and runtime behaviour, then reduce compute spend without compromising performance. Much of the saving comes from matching model choice to the workload and removing waste in how inference is batched and scheduled. You also get the monitoring to keep spend visible afterwards.
Do you implement the monitoring and pipelines, or only design them?
We can implement whatever you need. That means standing up observability, integrating dashboards, configuring alerts, automating evaluations, and hardening pipelines in your environment. Frameworks on their own do not keep a system running. The goal is an operational setup your teams can own and extend once we step back.
Will you replace our existing tooling?
Not by default. Most clients already run some combination of cloud monitoring, logging, and CI tooling, and the faster path is usually to extend it with AI-specific signals rather than migrate. Where a tool genuinely cannot capture what AI workloads need, we will say so and explain the trade-off. Any recommendation to replace something comes with the reasoning behind it.