Python engineered for production.

Python is the lingua franca of backend, data and ML — but most Python in production looks like research code that escaped a Jupyter notebook. Untyped, untested, slow because nobody profiled it, deployed because someone scp'd it onto a server. We write Python the way other languages do: typed end-to-end, tested at the boundaries, profiled where it matters, deployed like a real service.

• 01FastAPI services with async I/O, type-safe and OpenAPI-documented
• 02Django applications with serious operational maturity
• 03Async architecture with asyncio, anyio and Trio where each fits
• 04Background workers with Celery, RQ, Dramatiq, Arq or Temporal
• 05Database access with SQLAlchemy 2, Tortoise or raw psycopg
• 06Pydantic models for boundary validation and serialization
• 07ML model serving with FastAPI, BentoML, Ray Serve, vLLM
• 08Data tooling: ETL, scraping, scripting, CLI tools with Click / Typer
• 09Profiling and optimization: py-spy, scalene, asyncio diagnostics
• 10Packaging and dependency management with uv, poetry, hatch

• A typed Python codebase — mypy / pyright strict
• Test suite that catches regressions, not coverage theatre
• Container image and deployment configuration
• Performance baseline with documented bottlenecks

• → Teams whose Python services are slowing down or breaking under load
• → Data and ML teams who need to ship models as real services
• → Companies migrating from Flask / Bottle to a modern async stack
• → Founders building backend on Python because their team knows it
• → Internal platforms needing scripting, ETL and operational tooling