Backend & API Architecture
Most systems do not fall over because of traffic. They fall over because two things happened at once and the data model had no opinion about which should win.
We design backends around the state that matters: what the invariants are, where they are enforced, and what the system does when a request arrives twice or a downstream call times out halfway through.
What we build
Data modelling
Schemas designed around real access patterns and invariants, with constraints enforced in the database rather than hoped for in application code.
API design
REST or GraphQL interfaces with consistent errors, pagination, versioning and idempotency, documented from the schema so the docs cannot drift.
Concurrency correctness
Transaction boundaries, optimistic locking and idempotency keys applied where races actually occur, rather than blanket locking that trades correctness for throughput.
Background processing
Queues, scheduled work and long-running jobs with retries, dead-letter handling and back-pressure that degrades predictably under load.
Integrations
Third-party APIs, webhooks and payment providers wrapped so their outages and rate limits are contained instead of propagating through your system.
Observability
Structured logging, tracing and metrics chosen so an incident can be reconstructed afterwards from what was already recorded.
Migrations are part of the design
A schema that cannot be changed safely while the system is serving traffic is a schema that will stop being changed. Teams then work around it, and the workarounds become the real data model.
We plan expand-and-contract migrations from the start: additive changes first, backfills that can be paused and resumed, and a rollback path that does not depend on a backup restore.
Scaling the part that is actually slow
Scaling work is frequently spent on the wrong layer because nobody measured first. Adding instances behind a load balancer does nothing for a system bottlenecked on one unindexed query.
We profile before changing anything, then fix in order of effect — query plans and indexes, then caching with a real invalidation story, then horizontal scaling once the per-instance work is genuinely reduced.
Frequently asked questions
Which stack do you use?
Most often Node.js or Python with PostgreSQL, though we work in whatever your team already runs. We would rather extend a stack your engineers can maintain than introduce one only we understand.
Can you work on an existing backend?
Yes, and a large share of our work is exactly that — reviewing an existing system, identifying where correctness or performance is actually breaking down, and reworking those parts without a rewrite.
Do you do microservices?
When the team structure and deployment needs justify them. A well-organised monolith is the right answer more often than it is fashionable to say, and we will tell you when that is the case.
Tell us what you are building.
Every engagement starts with a technical conversation, not a quote. Tell us where the system is today and we will tell you honestly what we think it needs.