Python.
APIs, data pipelines and applied AI work, where Python’s libraries do much of the heavy lifting.
Talk to us about Python
Key features
What Python does well.
Data and ML libraries
pandas, NumPy, scikit-learn and PyTorch, plus most AI providers’ official SDKs, are written for Python first.
Type hints
Optional annotations, checked by tools such as mypy or Pyright, catch mistakes in larger codebases before they run.
Mature web frameworks
Django gives a full stack with an admin panel; FastAPI builds typed APIs with request validation and OpenAPI docs.
Free-threaded build
Python 3.14 officially supports a build without the global interpreter lock, though many packages still need to catch up.
What we build with it
The shortest path from data to a working service.
Python has the widest set of libraries for data processing, machine learning and document handling. We use it for applied AI services, evaluation work and pipelines, and for APIs when those sit close to that work.
Document extraction
Services that read PDFs, forms and scans and return structured fields for review.
AI evaluation
Test sets and scripts that measure how a model performs on real examples before it ships.
Data pipelines
Scheduled jobs that clean, combine and move data between systems.
When it’s the right choice
Python is the natural choice when the work involves models, data analysis or document processing, or when your team already runs Python services.
When we’d pick something else
For a general product API with no data science work, Go or Node.js can be simpler to deploy and share fewer dependencies. We match the language to the work and the team who will maintain it.
Our Python services
How we can help.
APIs and AI services
FastAPI or Django services that wrap models, extraction and search behind a clear API for the rest of your stack.
Notebook to production
Turning analysis scripts and notebooks into tested, versioned services that run on a schedule or on request.
Python version upgrades
Moving code off unsupported Python releases and replacing packages that no longer install cleanly.
Code and dependency review
Reviewing structure, typing, test coverage and pinned dependencies in an existing Python codebase.
How we work
Starting a Python project.
Review what exists
Code, data, hosting and the people who maintain it, before any Python decision is made.
Agree the approach
A written scope: what changes, what stays, the risks and how we will know it worked.
Build in reviewable slices
Working increments your team can test, rather than one large delivery at the end.
Hand over or stay on
Documentation and access for your team, or a defined support scope if you want us to keep going.
Where AI fits
AI in back-end work.
Model calls from your services
Node.js, Go and Python all have official SDKs from the major model providers, so AI steps sit inside your existing services.
AI workflow automation ↗Evaluation pipelines
Python’s data tooling makes it the usual choice for scoring a model against real examples before and after release.
AI evaluation & testing ↗
Where it stops: Retries, timeouts and fallbacks matter more for model calls than for most APIs, and every result that changes a record is validated first.
What’s changing
Shifts to plan for.
Dates that affect Python projects, checked against the official sources.
October 2026
Python 3.10 reaches end of life
Security fixes for Python 3.10 end on 31 October 2026, so services and scripts still on it need a newer version.
Where it fits
Related expertise.
- APIs & integrations Connect your product to the systems your team relies on, with clear contracts and handled failures.
- Document data extraction Pull fields from invoices, forms and reports into your systems, with validation and a review queue.
- Knowledge assistants Answer questions from your approved documents, with sources shown and access rules respected.
- AI evaluation & testing Measure whether an AI feature is good enough, using your own examples, before and after it goes live.
Common questions
Python questions.
How do you manage Python dependencies?
We pin versions in a lock file, build repeatable environments and keep packages updated on a schedule, so a deployment does not change behaviour unexpectedly.
Can a Python service work alongside our existing stack?
Yes. It can sit behind an API or a queue and be called by services written in other languages. We agree the contract and deployment with your team.
Further reading
- Firebase, Supabase or a custom back end: how to choose How to choose between Firebase, Supabase and a custom back end by data shape, access rules, cost, exit paths and skills.
- Answers staff can trust: an assistant built on approved documents How to build a document assistant that cites its sources, respects permissions and says when it does not know.
- Offline-first mobile apps for people who work without signal How to plan a mobile app that keeps working without signal, and what to settle before the first line of sync code.