Python development
We build in Python where it genuinely earns its place — data work, AI, automation, and backends — chosen for the job in front of it rather than reached for by habit.
Python's strength is the gravity of its ecosystem around data, machine learning, and scientific work: when a task involves models, dataframes, scraping, or numerical work, the mature library is almost always Python, and fighting that is a false economy. It is also a genuinely pleasant language to automate with — short scripts that do real work with little ceremony.
We use it where that pull is real and not where it is absent. An AI-heavy service, a data pipeline, an automation that stitches systems together — Python. Our Argus operations engine, at proof-of-concept stage, does its classification and search work in Python. A typed web backend with no such pull often belongs on Node or Fastify instead, and we pick per project rather than per preference.
Where Python earns its place
-
AI and data work
Model calls, retrieval, extraction, and the numerical and dataframe work where Python's ecosystem has no real rival — the reason to choose it in the first place.
-
Automation and pipelines
Scripts and data pipelines that move, clean, and reshape data on a schedule — reliable, typed where it matters, and observable when they run in production.
-
Typed backends on FastAPI
When the service is Python, we build it typed and validated on FastAPI, so the flexibility of the language does not become a source of runtime surprises.
Related from Ekarche
Have data, AI, or automation work?
Tell us what it has to do. We will build it in Python where that is the right call — and be straight with you when another stack fits the job better.