Backend

Python Development

The right tool when the work is data, automation or machine learning.

Python earns its place in our stack where the work is computational rather than transactional: cleaning and transforming data, running a model, scripting an integration, orchestrating an ETL job. Its ecosystem for those tasks has no serious rival. We treat it as a specialist tool rather than a default. A Python service sitting beside a Laravel application, doing the one thing Python is best at, is a sound architecture. A second full application stack maintained for its own sake is not.

Why we use it

We use Python for document extraction pipelines, forecasting and model-serving, data migration and any integration where the vendor's only decent SDK is Python — which is most of the machine-learning ecosystem. We would not rewrite a working business application in it. The libraries that make Python excellent at data work do not make it a better place to model an invoice, and the operational cost of a second stack is real and permanent.

Built with Python