Stabilize production so your team can ship safely.
Improve observability, deployment safety, resilience, and performance around the systems your business already depends on.
- 20–30 minute review
- No preparation needed
- Reliability baseline mapped
- deployment frequency
- 50x
- faster release cycles
- 96%
- uptime
- 99.95%
Biomark · Healthcare delivery reliability overhaul
Organizations we’ve worked with
- Airbnb
- TheAMLab
- Hood
- NAMA
- Klaro
- GoBoon
- Studio Arlington
The ways production keeps burning you
Every one of these is a symptom, not the disease. Reliability is something you engineer in — once.
All six are one problem: the system was never given an operational foundation — so every deploy is a gamble. Build the foundation once, and the rest of the list starts disappearing with it.
- 01Deploys need a maintenance windowReleases happen at midnight with everyone on standby, because shipping during the day is unthinkable.
- 02Users find outages firstThe first sign production is down is a customer email — not an alert from your own systems.
- 03Alert noise and blind spotsHundreds of alerts nobody reads any more, right next to failures nothing monitors at all.
- 04One fragile serverA single box everyone is afraid of. If it dies, so does the business — and everyone knows it.
- 05Performance degrades under loadFine at 9 AM, crawling by noon — and nobody can say why, so the fix is a restart and a prayer.
- 06On-call burnoutThe same people firefight the same class of incident, at night, forever — and they are getting tired.
Every release safe by default
A commit flows through CI, a canary deploy, health checks, and full promotion with zero downtime. Observability streams the whole time, and each release is confirmed healthy before it carries real traffic. No hero deploys, no blind debugging.
CI/CD, infrastructure, and monitoring rebuilt to support safe releases across a multi-app healthcare platform — 96% faster release cycles and 99.95% uptime.
We do not chase 100% uptime theater. Every extra nine has a price, and past a point it buys resilience against failures you will never have. We buy down the risks that actually bite — the fragile deploy, the blind outage, the server that cannot die.
- Reliability targets set by business impact, not vanity nines
- Spend goes to the failure modes behind your real incidents
- We will tell you when the current setup is already enough
The same day, with a foundation under it
Pick the one that sounds familiar — today on the left, on an engineered platform on the right. A representative pattern; your map will be exact.
- 1A release window is negotiated for Saturday nightdays of delay
- 2Someone runs the steps by hand, from memoryhero deploy
- 3The team watches logs and hopesno health checks
- 4Something breaks — rollback is a frantic restorehours down
- 5Everyone quietly agrees to deploy less oftenships slower
Deploys monthly · a weekend each · fingers crossed
- Merge to main triggers the pipelineauto
- A canary takes 5% of traffic firstcanary
- Health checks gate the promotionverified
- Full rollout completes with zero downtimeauto
- Anything looks off — rollback is one clickreversible
Deploys daily · minutes each · verified healthy
Works with the stack you already run
No rip-and-replace — we stabilize the platform you have.+ your existing infrastructure
- AWS
- Azure
- GCP
- Kubernetes
- Docker
- Terraform
- GitHub Actions
- Grafana
- Prometheus
- Sentry
- Cloudflare
- Postgres
The capabilities that deliver it
Cloud & Reliability Stabilization is built from three engineering capabilities working together.
Reliability Engineering
Observability, safe delivery, resilience, and the CI/CD backbone that keeps production stable under real load.
Integration
The connective layer so services, data stores, and third-party systems fail gracefully instead of cascading.
Automation
Automated pipelines, rollbacks, and runbooks so recovery and release stop depending on a person being awake.
How much is firefighting costing you?
Slide to your reality. This is only arithmetic — the review tells you which of those incidents a reliability system would actually prevent.
48hrs/month
≈ 576 hours a year spent firefighting, cleaning up, and rebuilding trust instead of shipping — before counting the revenue lost while you were down.
We will tell you which of those incidents a reliability system would prevent — and which are noise you should simply stop paging on.
From fragile production to boring deploys
Four phases, and each one hands you a concrete artifact — not a status update.
- 01 · Observe
Instrument the system
We wire metrics, logs, and traces into production first, so every later change is measured instead of guessed.
You get
Dashboards and alerts on the signals that matter
Done when
The top three failure modes are visible and named
- 02 · Stabilize
Make deploys safe
We build the pipeline — CI, canary deploys, health checks, and instant rollback — so releasing stops being an event.
You get
An automated pipeline with one-click rollback
Done when
A week of deploys ships with no maintenance window
- 03 · Harden
Remove single points of failure
We add redundancy, autoscaling, and load-tested capacity so the fragile pieces stop being existential.
You get
Failover and scaling verified under real load
Done when
The fragile server can fail without users noticing
- 04 · Operate
Run it calmly
We tune alerts, write runbooks, and set up an on-call rotation that pages people for real incidents only.
You get
Runbooks and an on-call setup people can live with
Done when
A month runs with pages only for incidents that matter
Where Cloud & Reliability Stabilization fits
We would rather tell you it is not the right starting point than stabilize a system that is not the constraint.
Good fit when
Start here- Production incidents, outages, or rollbacks are actively hurting the business
- Deploys are risky, manual, and something everyone dreads
- You have no real observability — failures are debugged blind
- Performance or scaling problems surface under real load
Not a fit when
Not yet- You are pre-production with no live users or real traffic yet
- The need is a brand-new build, not stabilizing an existing system
- We would have no access to the running system to instrument it
- The constraint is product scope, not reliability of what ships
Not sure which side you are on? Most teams aren’t — until the workflow is mapped.
Reliability & stabilization questions
It starts by mapping where production breaks, but the outcome is an engineered reliability system — observability, safe delivery, CI/CD, and scaling — not a report. We build the safeguards into your platform so the improvements hold after we hand it back.
Yes. We stabilize the platform you already run, whether that is AWS, GCP, Azure, or a mix. We are not here to force a rewrite — we engineer reliability into what exists and only propose deeper change where the current design is the actual constraint.
Safer and faster are the same problem solved well. Automated pipelines, canary deploys, health checks, and instant rollback let you ship far more often with less risk — moving from occasional, nerve-wracking releases to routine, safe deploys.
When something breaks, you see why in minutes instead of guessing. Metrics, logs, and traces surface the failing component, alerts fire before customers notice, and every release is confirmed healthy rather than assumed to be.
We start with the failure that hurts most — the recurring outage or the deploy everyone fears. We map it, find the real constraint, and show you the first piece of the reliability system to build before a larger rollout.
Start with the failure that hurts most
We will map where production breaks, find the real constraint, and show you the first piece of the reliability system to build.
20–30 minutes · No preparation needed · Not ready for a call? Send a note instead