AIOps, in full
Observability instrumentation — metrics, structured logs and distributed traces emitted by your services, so the question 'why is this slow' has an answer that does not require guessing.
Service level objectives — availability and latency targets defined per service, with error budgets that make 'good enough' a number instead of an argument.
Anomaly detection — baselines learned from your own traffic, so alerting fires on genuine deviation rather than on a threshold somebody picked a year ago.
Alert consolidation — correlated, deduplicated alerts routed to the team that can act — the direct remedy for on-call fatigue and for alerts nobody reads.
Incident analysis — timelines assembled from telemetry rather than memory, feeding blameless post-incident reviews and a tracked list of follow-up actions.
Capacity and cost intelligence — trend analysis that turns 'we might need more capacity' into a dated forecast with a price attached.
What AIOps services cover
Infrastructure monitoring services and intelligent monitoring solutions
Infrastructure monitoring services start with instrumentation, because no amount of analysis rescues data that was never collected: metrics, logs and traces correlated by request so a slow response can be followed across services. Intelligent monitoring solutions add the layer above — grouping related signals so an incident arrives as one event with context rather than forty alerts from forty components describing the same outage.
AI monitoring services, AI-based monitoring and anomaly detection
AI monitoring services are useful precisely where static thresholds are not: traffic with daily and weekly shape, where a fixed limit is either permanently breached or never. AI-based monitoring learns the normal envelope per service and flags departures from it. AI for IT operations is applied narrowly and on purpose — anomaly detection, alert correlation and forecasting — because a model nobody can interrogate is a worse on-call companion than a threshold everybody understands.
Predictive IT operations and AI incident management
Predictive IT operations means catching the trend before the outage: disks and connection pools that will exhaust in days, latency drifting a little each release, error budgets burning faster than the sprint. AI incident management shortens the rest — related alerts grouped into one incident, likely blast radius identified from the dependency graph, and the recent changes to that service surfaced alongside, because the honest first question in any incident is what changed.
IT operations automation and cloud operations automation
IT operations automation removes the repeat work that fills an on-call rotation: restarting a known-bad process, expanding a volume, draining a node, rotating a stuck queue — each as a reviewed runbook that executes rather than a paragraph somebody follows at 3am. IT automation services and cloud operations automation extend that to scheduled capacity changes and cost control, with every automated action logged and reversible.
Enterprise AIOps, AIOps platform services and AI infrastructure management
AIOps consulting is the assessment: what is instrumented, what is merely alerting, and which pages a human should never have received. AIOps solutions are built on Prometheus, Grafana, Loki and the ELK stack rather than a proprietary platform, so AIOps platform services leave you owning the data. Machine learning for IT operations sits behind those three and nowhere else. Enterprise AIOps adds SLOs per service with error budgets that make reliability a shared decision, intelligent IT operations means the on-call rotation gets context rather than volume, and AI infrastructure management and AI-powered IT management describe the same thing from the operations side. IT performance monitoring is what it all reports on.
What we actually use
Prometheus
The metrics backbone: pull-based scraping, a dimensional data model and PromQL for the questions you did not think to ask when you built the dashboard. Recording rules keep expensive queries cheap, and Alertmanager handles routing, grouping and silencing so a single failure produces one page instead of forty.
Grafana
Where the telemetry becomes something a human can act on. We build a small number of dashboards that answer specific questions — is the service healthy, is it within its SLO, what changed — rather than a wall of graphs nobody reads at 3am. Dashboards are provisioned as code, so they survive a rebuild.
ELK
Elasticsearch, Logstash and Kibana for log aggregation and search across services, with structured JSON logging and a correlation ID threaded through every request. Index lifecycle policies keep retention affordable and, where a compliance regime dictates how long logs must be held and how they must be protected, enforceable.
The shape of the work
We start by instrumenting what exists and establishing a baseline, because anomaly detection on an unmeasured system is theatre. Dashboards and SLOs are defined with the people who carry the pager, then alerting is tuned down until every remaining page is one a human should genuinely wake up for. The whole stack is deployed into your infrastructure and stays yours.
Who this is for
Operations teams carrying a pager that has stopped meaning anything. The pattern is consistent: dozens of alerts per incident, thresholds nobody remembers setting, and an outage timeline reconstructed from memory in the post-mortem. It is also the right engagement if you are being asked to commit to an availability target — in an enterprise SLA or a government tender — and have no measured basis for the number you are about to sign. Anomaly detection on an unmeasured system is theatre, so the baseline comes first.
Fewer pages, and the ones that remain mean something. Mean time to detection drops because the system tells you before a customer does, and mean time to resolution drops because the trace is already collected by the time an engineer opens the dashboard. Every change is a change you can point at in a review.
Observability is a build-time decision, which is why it shows up in what we ship. RelayZap exposes status, per-mailbox counts and a full event log as product surface rather than as an afterthought — a system that moves other people's mail has to be able to prove what it did with each message. BazaarBandhu made improved observability across a decoupled commerce stack an explicit objective, because once catalog, orders and payments are independently scalable, no single service can tell you why checkout is slow.
Questions we are asked
What is AIOps, and how is it different from monitoring?
Monitoring collects and displays signals. AIOps services act on them — correlating related alerts into one incident, learning normal behaviour per service instead of applying one static threshold, and automating the responses that are already well understood. Infrastructure monitoring services are the prerequisite, not the same thing.
Do we need to replace our current monitoring stack?
Usually not. AIOps solutions here are built on Prometheus, Grafana, Loki and the ELK stack, and most estates already run part of that. The first phase is normally filling instrumentation gaps and consolidating alerting, not a migration — AIOps platform services should leave you owning your own telemetry.
How does anomaly detection avoid becoming more noise?
By being scoped to where it earns its place. AI-based monitoring is applied to signals with genuine seasonality, where static thresholds cannot work, and every detection is routed through the same correlation layer as everything else. The measure of success is fewer pages, not more detections.
What does IT operations automation actually automate?
The responses your team already performs from a runbook: restarting a known-bad process, expanding a volume, draining a node, clearing a stuck queue, scaling for a predictable peak. Each is version-controlled, reviewed like code, logged when it fires and reversible — cloud operations automation is not a licence to let a model act unsupervised.
Talk to our team
Tell us what you are running now and what has to change. We will come back with a written assessment, not a brochure.