We set reliability targets (SLOs) for each service, monitor the signals that show real user impact and give on-call teams runbooks and automated fixes. Uptime stops depending on the one person who knows how the system works.
What's included
6 capabilities, delivered by one squad. Use what you need now and add more as you grow.
Reliability targets for each service, alerts when the error budget is being used up too fast and rules for when to pause releases.
Latency, traffic, errors and saturation for each service, with tracing, dashboards and alerts sent to the team that owns it.
On-call playbooks, incident roles, escalation and postmortems that prevent repeats.
Runbook automation, self-healing actions and safe rollbacks triggered by reliable signals.
Performance baselines, scaling policies and cost limits that don't hurt reliability.
Planned failure drills (game days) and fixes that limit how far a failure can spread.
Our approach
The usual way
No shared reliability goals, so every issue becomes a priority.
How we do it
SLOs, error budgets and burn-rate alerts give teams clear rules for trading release speed against risk.
The usual way
Noisy paging with no context or ownership burns out on-call teams.
How we do it
Latency, traffic, errors and saturation monitored end to end, with clean dashboards and alerts routed to owners.
The usual way
Recovery depends on what a few engineers remember, not on written runbooks.
How we do it
Runbooks people can follow, automated fixes and postmortems that cut down repeat incidents.
Architecture
Each layer has a clear job, so the system is easier to secure, test and extend.
Service targets, error budgets and burn-rate rules that tie release decisions to reliability risk.
Golden signals, tracing and dashboards that make problems obvious without drowning teams in noise.
Roles, escalation, communication and postmortems, so incidents are managed, learned from and reduced.
Runbooks as code and automated fixes that cut repetitive manual work and prevent repeat incidents.
How we deliver
4 phases, each ending with an output you can review.
We define SLOs and owners, review your incident history and set burn-rate thresholds based on business risk.
Output: SRE readiness blueprint
We instrument golden signals, dashboards, tracing and alert routing, and remove alerts nobody acts on.
Output: Monitoring baseline
We set up the on-call structure, runbooks, communication paths, postmortems and escalation to speed up recovery.
Output: Incident operating model
We automate runbooks and fixes, and use SLO reviews to keep improving reliability.
Output: Reliability that keeps improving
Your team
Specialists join your squad for this work, alongside a delivery lead who keeps you updated.
Defines SLOs, error budgets, alert routing and the reliability process across services.
Builds dashboards, alert rules, tracing and signal quality, so issues are visible and actionable.
Runs response workflows, escalation, communication and postmortems that prevent repeat outages.
Builds runbooks as code and self-healing actions tied to signals, so repetitive work keeps falling.
Trust and control
SLO budgets guide release decisions and incident severity.
Fixes are repeatable, with safe rollback patterns.
Blameless root cause analysis and system fixes that reduce repeat incidents.
Clear ownership and less alert noise keep on-call sustainable for your team.
You keep full ownership of the code, configuration and documentation we create, with no vendor lock-in.
We pick what fits your product and team, not the other way round.
Results
We rebuilt a global healthcare provider's telehealth video platform on distributed video servers (SFUs) that scale with demand and keep patient data out of the logs. It supports 50k+ concurrent sessions with 99.99% uptime.
We wrapped a hotel group's ageing PMS in a modern API layer with caching and live sync, so new guest features no longer need risky changes to the core system. It runs at 99.99% availability, with 45ms API responses.
We built AI network security for a global resort chain. It learns how devices normally behave on guest Wi-Fi and isolates suspicious ones automatically. It neutralised 99.9% of threats and detected them 2.5x faster.
We moved a global core banking system from an ageing mainframe to AWS one function at a time, keeping both ledgers in sync throughout. Operating costs fell 60%, releases became 5x faster, and there was zero service downtime.
An SRE engagement covers four stages: setting SLOs and error budgets from your incident history, monitoring latency, traffic, errors and saturation, setting up on-call, escalation and postmortems, and automating common fixes with runbooks as code. It starts with a readiness review that shows which of these needs attention first.
An SLO is a reliability target for a service, such as the share of requests that must succeed each month. The error budget is the amount of failure that target allows. When the budget is being used up too fast, burn-rate alerts fire and teams slow releases to fix reliability first.
We rebuild alerts around symptoms users notice, such as slow responses or errors, instead of every CPU spike. Each alert goes to the team that owns the service and links to a runbook. Burn-rate alerts replace many fixed thresholds, so people are paged only when a reliability target is at real risk.
We define incident roles, severity levels, escalation paths and how updates are shared. After a significant incident, the team runs a blameless postmortem that looks for the root cause in the system, not a person to blame, and agrees fixes. Those fixes are tracked, so the same incident is less likely to happen again.
Common, well-understood fixes can be automated, such as rolling back a bad release, restarting a failed service or adding capacity under load. We write these as runbooks as code, triggered by reliable signals and with safe rollback built in. Engineers then spend less time on repetitive work and more on problems that need judgement.
A global telehealth video platform we rebuilt supports 50k+ concurrent sessions with 99.99% service uptime. The API layer we built around a hotel group's ageing PMS runs at 99.99% system availability with a 45ms API response. The telehealth platform scales across distributed servers as demand grows, and the hotel layer relies on caching and live sync.
It depends on how many services you run, the state of your monitoring today and how mature your on-call process is. A scoped project can kick off in 1–2 weeks with a readiness review of your SLO baseline and alert noise, which sets the order of the work and the first fixes.
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