Tech Stack · Back-end
Python for data, AI and clear, maintainable services.
Django, FastAPI, async services, ML pipelines and a language your team can read on day one.
Why we use it
Python is the lingua franca of modern data and AI work, and it has aged well as a back-end language too. The syntax is approachable, the standard library is rich, and the ecosystems for data, machine learning and scientific computing are unmatched. When the back-end has to talk to models, pipelines or notebooks, Python is usually the path of least friction.
What we build with it
Production APIs with FastAPI, full-stack applications with Django, data and ML pipelines with the broader scientific stack, and integrations that sit between business systems and AI services. Postgres, Celery and Redis cover the typical supporting cast; Pydantic and SQLAlchemy keep the data contracts honest.
How we work with it
Typed Python with strict linting, clear separation between domain, application and infrastructure layers, and tests that exercise the boundaries that actually break. We package, deploy and observe Python services with the same discipline we apply to any production system, so "it works on the data scientist's laptop" stops being the deployment plan.
Building with Python?
Tell us what you're shipping and we'll bring a senior team that knows the stack.