What we do · 03
Prototype to production
A demo takes an afternoon. A system people depend on takes engineering. We take AI pilots that impressed in a meeting and make them hold up under real users, real volumes and real failures.
Most AI pilots do not fail on the model. They fail on what was skipped to get the demo working: no evaluation, heavy work inside web requests, no plan for failure, permissions enforced only in the interface, and nobody tracking cost.
We find the gaps, fix the ones that matter first, and leave a system your team can run and change.
What we build
01
Evaluation you can rerun
A reviewed baseline, quality checks per field or task, and a way to compare models and prompts before changing them.
02
Background processing
Queues, workers, durable job state and idempotent retries, so heavy work never blocks a user and retries never double-apply.
03
Security and access
Server-side authorisation, protected secrets and least privilege, for the application and for the model's tools.
04
Cost and observability
Cost and latency per job, structured logs and traces from a user action to the background work it started.
05
Safe releases
Automated build and test, migrations that keep old data working, and a tested way back from a bad change.
What you get
- A system that fails visibly and recovers, instead of failing silently
- Known quality, known cost per run
- A codebase your team can operate and extend
Related work
Questions
Can you review a system before building anything?
Yes. An architecture and reliability review is a sensible first piece of work: the bottlenecks, the risky failure modes and the fixes worth doing first.
Do we need microservices?
Usually not. We often start with the existing application plus a separate worker, and split services only when scaling or failure isolation gives a reason.
Also
Have an AI system to deliver?
Tell us what your client needs, or what your team is stuck on. We reply within two working days.
Talk to us