Python development
Python is where the AI and data work happens: model integration, retrieval pipelines, document processing, evaluation harnesses and scheduled automation.
Where we use it
We write Python that looks like production software rather than a notebook: type hints throughout, dependency management that reproduces exactly, tests around the logic that matters, and structured logging so failures are diagnosable at three in the morning.
For services we use FastAPI, which gives typed request and response models and generated API documentation as a side effect of writing the code properly.
- FastAPI services with generated documentation
- Retrieval and document processing pipelines
- Evaluation harnesses that run in CI
- Scheduled jobs with retries and alerting
- Typed, tested, reproducible environments
- Containerised deployment to your cloud
Why Python for the AI layer
Every model provider ships a Python SDK first and a good one; the retrieval, evaluation and data tooling ecosystem is largely Python; and the people who will maintain a data pipeline after we leave are far more likely to read Python than anything else. Those three facts decide it more than any language preference.
That does not make it right for everything. Where the AI work sits inside a product that is already TypeScript end to end, adding a Python service means another deployment target, another dependency chain and another thing to monitor. In that case the model calls stay in Node and Python appears only where it genuinely earns its place.
What the handover looks like
Typed with annotations throughout and checked in CI, dependencies pinned, tests that run without network access, and a README that states how to run it locally in under five minutes. Formatting and linting are automated so nobody argues about them in review.
Notebooks are for exploration and do not ship. Anything that reaches production is a module with tests, because a notebook cannot be deployed, diffed or trusted six months later.
Frequently asked questions
Notebooks or proper applications?
Notebooks for exploration, applications for anything that runs twice. Production work delivered as a notebook is a maintenance problem handed to someone else.
How do you manage dependencies?
Locked dependency files and containers, so the environment that passed tests is the environment that runs.
Tell us what you are building.
Send a short description of the problem and we will reply within one business day with an honest view of scope, cost and whether we are the right person for it.
Or email directly: contact@hire-ai-dev.com