Observability before scale
Logs, metrics, and traces that answer "what is it doing right now" have to exist before load makes the question urgent. Scaling a system nobody can see multiplies the fault rather than the capacity.
Services
Cloud-native Engineering
Kubernetes, observability, CI/CD, secure infrastructure — so your AI doesn't fall over the day after launch.
What this covers
The unglamorous layer that decides whether any of it stays up.
AI systems fail in production for the same reasons every other system does. A certificate expires. A dependency is pulled at build time and is not the version anyone tested. A pod is evicted under memory pressure and nothing alerts. The model is fine; the platform underneath it was assembled once, by hand, and never described anywhere.
Cloud-native engineering is the discipline of making that layer boring. Builds are reproducible, so the artefact tested is the artefact deployed. Deployment is a path anyone on the team can follow, not a sequence of steps living in one person’s memory. Permissions are scoped to what a workload needs, so a compromise stays small. And the system is observable before it is scaled, because scaling something you cannot see is how a small fault becomes an outage.
This work is most valuable before it is urgent. Retrofitting observability during an incident is possible, and it is the most expensive time to do it.
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A representative image for Cloud-native Engineering — a real system diagram, an architecture whiteboard, or the team at work. Not a stock photo.
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The shape of it
How the pieces fit together.
How we approach it
Four stages, in order.
- 01
Assess
Establish what actually runs where, what it depends on, and which parts exist only as undocumented manual steps.
- 02
Codify
Move infrastructure and deployment into version control so the running system has a written, reviewable source of truth.
- 03
Instrument
Add the logging, metrics, tracing, and alerting that make failure visible early and diagnosable afterwards.
- 04
Sustain
Rehearse rollback, patching, and certificate renewal until they are routine rather than incidents in waiting.
Principles
What we hold to, and why.
Reproducible builds
Pinned dependencies and a build that produces the same artefact twice. If the thing deployed cannot be traced to a commit, no rollback is trustworthy and no incident review is conclusive.
Least privilege by default
Every workload gets the narrowest set of permissions that lets it do its job, and secrets are held where they can be rotated. The blast radius of a compromise is decided long before the compromise.
The deploy path is a product
It has users — the engineers who ship — and it deserves the same care as anything else they use. A deploy that only one person can perform is an availability risk with a name attached.
Is this you?
Signals that this is the work you need.
- A deployment can only be performed by one person, from one machine.
- The first sign of an outage is a customer reporting it.
- Nobody can say with certainty which commit is running in production.
- Credentials are long-lived, broadly scoped, and have never been rotated.
- The staging environment differs from production in ways nobody has written down.
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A short walkthrough for Cloud-native Engineering — an engineer explaining the approach, or a screen recording of the system being discussed.
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Tell us what you're building.
We'll tell you straight whether this is the right thing to spend on.