Engineering Stacks
Python Development
Python where it genuinely fits: data work, automation, integrations and the services that sit behind AI features. Typed, tested and packaged so it runs somewhere other than the machine it was written on.
Data and automation
Most of our Python work is moving, reshaping and acting on data — pipelines that pull from several systems, reconcile them and produce something a human or another service can use. That work is unglamorous and it is where a lot of operational cost actually hides.
We write it with type hints, tests and idempotency, so a failed run can be repeated safely rather than needing someone to reason about what half-happened.
Services and APIs
FastAPI for new services, Django when the project wants an admin and an ORM out of the box. Either way: dependency pinning, containerised builds, structured logging and a health endpoint.
What the engagement includes
APIs and services
FastAPI or Django REST, typed and documented.
Data pipelines
Extraction, transformation and scheduled runs with alerting.
Automation
The recurring manual task, done properly and monitored.
AI integration
Model APIs, retrieval, evaluation and cost control.
Packaging
Reproducible environments and containerised deployment.
Testing
pytest, fixtures and CI on every change.
Technologies we use
Frequently asked questions
Do you do machine learning?
We integrate models rather than train them from scratch. For most business problems the right answer is a hosted model behind a well-designed retrieval and evaluation layer, not a bespoke training pipeline. If you genuinely need custom model training we will say so and help you scope it.
Django or FastAPI?
Django when you want batteries included — admin, ORM, auth, migrations — which suits content-heavy and internal systems. FastAPI when it is a service that mostly speaks JSON and you want async and automatic OpenAPI docs.
Can you automate a process we currently do by hand?
Usually, and the first step is measuring what it actually costs — how often, how long, and what it breaks when it goes wrong. Sometimes the honest answer is that automation will cost more than the task. We would rather tell you that early.
How do you handle long-running jobs?
Queued with Celery or a managed equivalent, with retries, dead-letter handling and alerting. Long work does not belong in a web request.