FastAPI development
We build Python services on FastAPI — typed, validated by Pydantic, async where it counts — the right backend when the AI and data work is the point rather than an add-on.
FastAPI brings to Python what strong typing brings to any language: a request is described, validated, and documented before a line of business logic runs, and the editor knows the shapes as well as the runtime does. Pydantic models are the contract, the validation, and the OpenAPI docs at once — which is why it has become the default for Python services that need to be correct, not just quick to write.
We reach for it where Python's real gravity is: work that sits close to models, data, and pipelines. Argus — our AI-native operations engine, at proof-of-concept stage — is built this way, with the classification, similar-incident search, and drafted-postmortem work living in a FastAPI service. When a backend is mostly CRUD and integration with no such pull toward Python, a typed Fastify service is often the better fit, and we will say which your project wants.
What we build with FastAPI
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Typed and validated by Pydantic
Requests and responses modelled explicitly, validated automatically, and documented as OpenAPI without a separate step — the schema is the code, not a stale file beside it.
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Async where it earns it
Async request handling where the work is I/O-bound — model calls, external services, streaming — so the service stays responsive under real concurrency.
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Close to the AI and data work
The natural home for services that call models, run retrieval, or sit in front of a data pipeline — Python's strength, with the typing that keeps it maintainable.
Related from Ekarche
Building something AI-heavy in Python?
Tell us what the service has to do. We will build it on FastAPI — typed, validated, and shaped around the model and data work at its core.