Backend &systems
APIs and data layers that stay predictable under load.
We design schemas and APIs we would be happy to own for years. Explicit contracts, sensible defaults, observability from day one, and infrastructure that is reproducible rather than tribal knowledge.
What the engagement involves
Back-end work fails in the places nobody demos: the migration that locks a table for eleven minutes, the retry that fires three times under load, the endpoint that returns slightly different shapes depending on which service answered. We design the schema and the contract before the endpoint, because those are the decisions that get expensive once other systems depend on them.
We ship infrastructure as code, structured logs and metrics from the first sprint, and a documented rollback path for every data change. Data work is treated as the main event rather than a cutover task at the end: mapping, cleansing and rehearsal happen in staging that mirrors production, so go-live is a switch rather than a gamble.
Who it suits
This suits teams whose data has outgrown the application that owns it, organisations integrating several systems that were never designed to agree, and anyone extracting a service from a monolith without freezing feature delivery. It is the wrong engagement if you need one small CRUD service this month ’ that is a fixed-scope build, not a systems engagement, and we will price it as one.
How we run it
Every engagement follows the same sequence. The steps below are in delivery order — each one is independently useful, so an engagement can stop after any of them with something finished.
01
Contracts first
The API is designed on paper before a single endpoint exists.
Every resource, error shape and auth boundary is agreed up front. Front-end and integration work can start against the contract while the implementation catches up.
02
Data models that survive contact
Schemas designed for the tenth year, not the first demo.
We model for the exceptions already sitting in your data, not the happy path in the spec. Migrations are rehearsed against production-shaped data with a rollback for each step.
03
Observable from day one
Structured logs, traces and metrics ship with the first deploy.
The 3am page goes to a dashboard that answers the question, not to a log file that raises three more. Alerting is tuned against real traffic in the first month.
04
Boring infrastructure
Reproducible environments instead of tribal knowledge.
Infrastructure as code, reviewed like application code. If only one person knows how production works, that is the first thing we fix.
When backend work is the wrong answer
A spreadsheet would do
If the data fits in a sheet and two people touch it, a database project buys ceremony without benefit. Automate the sheet instead — we will happily not sell you a backend.
You need data science, not software
Exploratory modelling and one-off analysis need notebooks and statisticians, not APIs and uptime SLOs. Different discipline, different team.
The ops team is the product
If your organisation measures success by ticket throughput rather than system behaviour, our observable-everything approach will chafe. Fix the incentives first.
Questions we get about this kind of work.
01Do you implement enterprise systems or just advise on them?
02Will you tell us not to buy something?
03How do you price enterprise work?
04Can you take over a project that has already gone wrong?
Ready to talk about backend & systems?
Most enquiries get a reply within 2 working days. If it is not a fit we will say so rather than waste your time.