The difference between a 5-minute outage and a 4-hour outage is monitoring. We build observability stacks — metrics, logs, traces, and alerting — that tell your team exactly what's wrong, where, and why, before a customer has to report it.
Finding out about outages from customers, drowning in noisy alerts, and debugging without traces are symptoms of an observability gap.
Customers report outages before your team notices anything is wrong. Your alerting exists but it fires 20 minutes after the impact started.
200 alerts fire at once and engineers can't identify the root cause. Alert fatigue has trained your on-call team to acknowledge and ignore.
No structured logging — debugging means SSH-ing into servers and grepping files. Nobody can answer 'what happened to request X at 3:42am'.
Graphs exist but nobody looks at them until something breaks. Dashboards built once, never maintained, now showing metrics for services that no longer exist.
No distributed tracing — a slow API call could be any of 12 downstream services. The only way to investigate is to add log statements and redeploy.
On-call rotation burns out engineers because every alert feels critical. P1 pages for things that auto-recover in 30 seconds erode trust in the whole alerting system.
We build the full observability stack — metrics, logs, traces, and alerting — with runbooks for every alert so your on-call team always knows what to do.
From the first Prometheus scrape config to a mature SLO-based alerting system — scoped to your current state.
Prometheus, VictoriaMetrics, Datadog, New Relic agent deployment — scraping applications, infrastructure, and managed cloud services.
Service dashboards, infrastructure overview, SLO burn rate panels — built to be read during an incident, not just reviewed in reviews.
Loki, ELK Stack, CloudWatch Logs Insights, Datadog Logs — centralized, structured, and searchable in seconds with long-term retention.
Jaeger, Tempo, Zipkin, Datadog APM, OpenTelemetry instrumentation — see the full request path across every service it touches.
Alert rules, severity levels, inhibition rules, and dead man's switch — every alert is actionable or it doesn't exist.
PagerDuty, OpsGenie, on-call schedules, escalation policies, and post-mortem templates built into your team's workflow.
Define SLOs, track error budget burn, and alert before budget is exhausted — alert on user-facing impact, not internal symptoms.
Uptime checks, API probes, and end-to-end browser tests that alert before real users hit a broken flow.
We start with your current state — even if that's no monitoring at all — and build toward a production-grade observability platform.
Review existing monitoring, identify gaps in metrics, logs, and traces, and map critical user journeys that need SLO coverage.
Add metrics, structured logs, and trace IDs to services using OpenTelemetry — the application tells you what's happening.
Prometheus, Grafana, Loki, and Alertmanager deployed and configured — scraping all services and infrastructure targets.
One dashboard per service plus a platform-wide overview — built to answer the questions engineers ask during incidents.
Start with comprehensive alerts, silence noise progressively, and achieve a signal-to-noise ratio where every alert is actionable.
Write a runbook for every alert so on-call engineers know exactly what to do at 3am without waking anyone else up.
The observability ecosystem we work with — open-source and commercial, on-premise and SaaS.
Observability matters wherever uptime is a business requirement — which is everywhere software is running.
Most monitoring projects produce dashboards nobody reads and alerts that never fire. We build observability that engineers trust.
Not just numerous. Every alert we ship has a severity, an owner, and a runbook. Alert fatigue is a product decision, not an inevitability.
On-call engineers know what to do at 3am because we wrote the runbook when we wrote the alert rule — not after the first incident.
Not just your infrastructure. Application metrics, structured logs, and trace IDs — added to your code so you see what's happening inside each service.
Mean time to recovery is the metric that matters. We measure it before we start and after we deliver so you can see the improvement in hard numbers.
A high-traffic e-commerce platform was discovering production issues from customer support tickets — often an hour after the first impact. They had dashboards but no alerts, and no structured logging. We built their full observability stack in 6 weeks.
No structured logging, no distributed tracing, 200+ noisy alerts that engineers had learned to ignore. Outages discovered via customer support.
OpenTelemetry instrumentation across 14 services, Grafana + Loki + Jaeger stack, SLO-based alerting with PagerDuty integration and runbooks for every alert.
Incidents detected in 5 minutes average. Root cause identified in under 20 minutes via traces. On-call engineers report the job is now manageable.
● Success Stories
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