APIs, data pipelines, and backend services built with FastAPI and Django. We ship Python that handles concurrency, deploys cleanly, and stays reliable under real traffic.
The language rewards beginners. The ecosystem punishes teams that skip the hard parts. We have fixed the same production problems across startups and enterprises.
Python is excellent for data, APIs, and automation. But the distance between a working script and a production service is measured in concurrency models, observability, and deployment discipline — areas where most Python projects drift.
Not just because we write Python. Because we know what happens when the data grows and the traffic spikes.
FastAPI for high-throughput async APIs with auto-generated OpenAPI docs. Django for full-featured backends with admin panels and ORM. We pick the framework based on the workload, not on which one we learned first.
"The PM was on our side for most of the time, but they delivered everything on time, and everyone on their team was responsive."
— Mateusz Piwnicki, CEO, Software Agency
Verified on ClutchML pipelines with scikit-learn and PyTorch, ETL jobs with pandas and Polars, LLM integrations with LangChain — all in Python, without stitching together a polyglot stack that nobody wants to debug.
"Now we can clearly show our users what products we have. The payment system is simple and easy."
— Dave Chiu, Managing Director, Teafolks
Verified on ClutchStructured logging, health checks, Prometheus metrics, and CI/CD pipelines that run tests and linters before code reaches a branch. We deploy to AWS, GCP, Azure, or Docker on your own metal.
"We are happy with the output and are currently using the screens and the prototype to seek investment."
— Aaron Braich, Co-Founder, Toggled
Verified on ClutchA repeatable path from framework choice to a production service that your team can own.
We start by defining the service boundaries, async model, and data contracts. Then we build the API layer with Pydantic schemas, database models, and background task queues. We integrate with your existing systems and deploy with full observability. Depending on the product, we bring in web-app engineering for the API layer and full-stack delivery, AI automation for ML pipelines and LLM integration, and dedicated teams when you need long-term backend capacity.
"We love the new dashboard; it looks amazing."
— Head of News Company
Verified on ClutchReal products where Python powers the backend — from data pipelines to AI inference.
An agricultural operation runs 756 vehicles, AI route planning, and weather prediction across remote fields. Python handles the backend — IoT sensor ingestion, fleet tracking, route optimization algorithms, and compliance reporting for operations across three continents.
Read the full storyNimibot runs a conversational commerce platform where the AI is trained on the product catalog. Python powers the retrieval-augmented generation pipeline, the training infrastructure, and the commerce integration — processing natural language queries into product searches, cart actions, and checkout flows.
Read the full storyHealthcare & Wellness, legal tech, logistics, fintech — we know which Python patterns survive traffic spikes and which ones collapse under real load.
We use async where throughput matters and sync where readability matters more. No blanket asyncio conversion and no blocking the event loop.
AWS, GCP, Azure, Docker on your own servers. Your infrastructure decision shapes ours — never the other way around.
FastAPI for APIs, microservices, and async workloads. Django for full-featured backends with admin panels, ORM, and built-in auth. We pick based on the product, not preference.
Yes. We take models from notebooks to fast, monitored APIs with versioned endpoints and A/B testing. See our AI automation service.
Docker with multi-stage builds, CI/CD pipelines with lint and test gates, and infrastructure-as-code for repeatable deployments. We set up monitoring and alerting before the first deploy.
Yes, after a short audit. We tell you honestly whether the monolith can be refactored incrementally or whether a structured decomposition will save more time.
Tell us what you are building and what broke last time. We will design the architecture, the deployment, and the monitoring so it stays up.