Python
Python development for data, AI, and clear back-end services
Python is our usual language beside AI features, document processing, and integration scripts that need to stay readable. We also use it for APIs when the team and the problem fit, with the same standards of testing and deployment as any other stack.
- AI service layer
- Document pipelines
- Integration services
- Admin-heavy tools
What we build
Ingestion and retrieval for RAG, extraction pipelines, and APIs that front a model or a business rule. Frameworks such as FastAPI or Django are chosen for the job: Django when the admin and the data model are the product, FastAPI when the service is an API beside other systems.
When Python is the wrong core
A large, long-lived transactional system for a Java or .NET organisation should usually stay in that estate. Python then sits at the edge for the model or the document step. We do not rewrite a stable core to chase one language.
Python beside data and documents
Use Python when
- The work is retrieval, automation, or integration
- A small service is enough
- The team can already read it
Not the default for
- A large transactional core you need in Java
- A mobile app
- A GUI the public will live in
What changes a Python service
The job
A document pipeline and a checkout are different responsibilities. Python is often the first.
The model
Calling a model from Python is ordinary. Training your own model is a different project.
The runtime
Pin the environment. A laptop that works is not a release.
Before a Python service
- 01
The job
Retrieval, automation, or a small integration.
- 02
The runtime
How it will be pinned and deployed, not only how it runs locally.
- 03
The model
Calling one, if that is the point. Training one is a different brief.
- 04
The neighbour
The Java or .NET core it should not replace.
Marks Python is in the right place
- 01
A job, not a habit
It is here for data, automation, or a service the team already runs in Python.
- 02
A notebook is not production
The path that runs every day is a service or a job, with the same inputs.
- 03
Dependencies are pinned
The release uses a known set of libraries, so next month matches this month.
- 04
Someone can rerun it
A failed job can be started again by the operator, with a log.
What we build
AI service layer
The API, retrieval, and evaluation harness around a model.
Document pipelines
Parse, extract, and review queues for the files you actually receive.
Integration services
Small, tested services that talk to the systems of record.
Admin-heavy tools
Django when staff need to manage records and the workflow is content-shaped.
Related reading
RAG development
Answers grounded in your documents, with permissions intact.
AI automation
Document intake, triage, and routine steps with a review point.
Google Cloud
Application and data workloads when GCP is the chosen platform.
API development
Stable contracts between your product and the systems around it.Questions we hear
Do you use Python for web front ends?
The server may be Python. The interface is still a proper web front end when the product needs one. We do not serve a notebook as a business system.
Can you productionise a data scientist’s prototype?
Often that is the engagement: tests, configuration, a deployment, and a boundary so the prototype’s shortcuts are not the live system.
Which libraries do you trust?
A short list we can patch. Adding a dependency is a decision, especially anything that handles files or authentication.