We set reliability targets that match what your users feel, give your teams the signals to meet them, including for AI agents and model calls, and make incidents calmer and shorter.
99.94% availability, 30 days72% error budget left
What it means in practice
- Service level objectives (SLOs) and error budgets tied to user experience.
- Observability across metrics, logs and traces, on the stack you already run: ELK, Splunk, Dynatrace, Datadog, Prometheus and Grafana, or CloudWatch.
- Observability for AI: traces of model calls, agent steps and tool use, with latency, cost and quality signals.
- Incident response: on-call, runbooks and blameless reviews.
- Capacity planning and resilience testing.
What you get
- SLOs and dashboards for your key services
- Alerting that pages for what matters
- Runbooks and an incident process
- A reliability review with priorities
Typical stack
- OpenTelemetry
- ELK Stack
- Splunk
- Dynatrace
- Datadog
- Prometheus
- Grafana
- PagerDuty
- Amazon CloudWatch
We work with the tools your teams already use.
How it runs
- AssessWe review your architecture, data, goals and constraints, and agree what success looks like.
- DesignTarget architecture, a delivery plan and the measures we will be judged on.
- BuildShort iterations, working software early, built alongside your team in your accounts.
- RunWe operate it to agreed service levels, improve it, or hand it over with runbooks.
Tell us where your AI is stuck.
One conversation with an engineer. No sales script.