Python Development Services
CrestCoder builds custom Python backends, AI/ML pipelines, and automation for businesses across India, Australia, South Africa, and the UK. Built on Python 3.14 with FastAPI, Django, and the tooling the ecosystem actually uses in production.
Python 3.14 brings free-threading (no GIL), t-strings, and subinterpreters — but the bigger story is that Python is the default language for AI/ML and a strong general-purpose backend.
The dominant language for ML — PyTorch, TensorFlow, scikit-learn, and the entire training-to-inference pipeline runs on Python.
FastAPI delivers async, type-safe, auto-documented APIs with performance that competes with Node.js for I/O-bound workloads.
ETL pipelines, data processing, and task automation — Python's library ecosystem (pandas, polars, Celery) makes these practical to build and maintain.
PEP 779 makes free-threaded (no-GIL) Python officially supported — real multi-threading for CPU-bound work without multiprocessing workarounds.
Model training, inference APIs, and production ML pipelines — from prototype to deployed, monitored system.
Async, type-safe REST APIs with auto-generated OpenAPI docs, built for speed and easy frontend integration.
Full-featured web applications with admin panels, ORM, auth, and the structure Django gives you out of the box.
ETL, data transformation, and scheduled processing with pandas, polars, and task queues like Celery.
Business process automation, report generation, and system integration scripts that save hours of manual work.
Python version upgrades, dependency audits, and ongoing feature development under a monthly retainer.

A platform needed an AI/ML backend that could process, classify, and organise large volumes of user photos at scale — where model accuracy and processing speed directly affected the user experience.
We built a high-performance Python backend with AI/ML models for image classification, paired with cloud infrastructure for training and inference at scale. Python's ML ecosystem made this the natural language choice.

A co-parenting app needed a reliable backend to manage shared calendars, messaging, and expense tracking — handling sensitive family data with the reliability and privacy the context demands.
We built the backend serving the cross-platform Flutter app, handling real-time sync for calendars and messaging, secure data storage, and notification dispatch across both platforms.
We have shipped production AI/ML backends — not just experimented with notebooks.
We build on Python 3.14 and plan upgrades deliberately, so your backend doesn't quietly drift years out of date.
Two-week sprints with a working demo at the end of each — you see real software, not slide decks.
Small enough that senior engineers stay hands-on, large enough to staff a dedicated team when you need one.
We decide on Django vs FastAPI vs Flask, map out the data model, and scope ML requirements if AI is involved.
API endpoints documented early so your frontend team can build in parallel.
Two-week sprints with a working demo deployed to a staging environment you can access anytime.
Unit tests with pytest, integration tests against real data, and ML model validation when applicable.
Production deployment, then an optional monthly retainer for version upgrades and continued feature work.
We reply within 24 hours with an honest architecture recommendation — no obligation.