FastAPI services
Async APIs with Pydantic validation and generated OpenAPI docs, used to serve models and wrap business logic.
Languages and frameworks
Python development.
Python is where almost all of the AI and data tooling lives, which makes it our default for model services, pipelines and automation. We pair it with typed code and tests so it stays maintainable once it leaves the notebook.
A short call. Your first build, a fixed quote, and the timeline. No obligation.
What we build with Python
Where it fits.
Data pipelines
Scheduled and event-driven pipelines with pandas, Polars or PySpark, orchestrated by Airflow or Prefect.
Background workers
Celery or Arq workers that process documents, call model APIs and retry safely when a provider is slow.
Automation scripts
Integrations that scrape, parse and sync data between systems, packaged with tests and clear logging.
When it is the right choice
Choose Python when
- →The work touches machine learning, data science or LLM libraries.
- →You need glue code between many APIs and data sources.
- →Speed of development matters more than raw runtime speed.
Worth knowing
Honest trade-offs.
- →Raw CPU performance is lower than compiled languages, so hot paths may need vectorised libraries or a different language.
- →Dependency and environment management needs discipline, so we pin versions and build in containers.
Often paired with
FastAPIPostgreSQLRedisDockerPydanticCelery
Related services
Services that use it.
Proof
Work built by the same team.
Closeum
13+ connected modules from calls through commissions
13+connected modules
5role types
32completed transactions
40+leads managed
SIIMS
Orders, production, warehouse, and dispatch on the same records
4operational roles
37backend tests passed
17hosted workflow tests passed
Ledgerline
603 tests passed in a dated financial-control verification
SAR 760Kcash reconciled end to end
603tests passed
14control findings fixed
3finance sources in one ledger
Common questions
Before you start.
Is Python fast enough for production?
For most APIs and pipelines, yes, because the work is waiting on databases and networks. For tight compute loops we use NumPy, compiled extensions or move that piece to Rust or Go.
How do you keep Python code maintainable?
Type hints checked in CI, linting, tests around the business logic and pinned dependencies. Notebooks are for exploration, not for what ships.
FastAPI or Django?
FastAPI for lean, async service APIs, and Django when you want an admin, authentication and an ORM out of the box. We choose by the shape of the product.
What do you build with Python?
AI services, Data pipelines, APIs, Automation scripts, and related work. We choose Python when it is the right fit for the product, the team and the budget.
Can you work with our existing Python code?
Yes. We can review what you have, fix what is fragile, and extend it, or build alongside your team.
Keep exploring
More than Python.
Where we apply it
The Audit
Get my free AuditKnow what to build first.
Book a short call and show us one workflow. You leave with what we would build first, a fixed quote, and what should wait.