Python development, for data and AI that ships.
Python is the language behind most of what we build — the pipelines that move your data, the models that learn from it, and a good share of the backends that serve it. Readable, well-tested, and boring in the best way: it does the job in production, not just in a notebook.
Where Python earns its place in the stack.
Data pipelines & engineering
Pandas, Polars and PySpark move and reshape data at whatever scale the job needs — from a daily CSV import to a warehouse-scale pipeline.
AI and machine learning
PyTorch, scikit-learn and the wider ML ecosystem live in Python first. It's our default for anything that trains, predicts or reasons over data.
Backend APIs & services
FastAPI gives us a fast, typed, well-documented API layer — the same language on both sides of a data-heavy backend keeps the whole stack simpler.
Automation & tooling
Internal scripts, scheduled jobs, scraping and glue code — Python's standard library and package ecosystem make short work of the unglamorous stuff that keeps a system running.
Readable, versatile, and built for production.
Python isn't our only language, but it's the one that shows up most across our data and AI work — because the ecosystem around it is, by a wide margin, the deepest for that kind of problem.
We don't reach for it out of habit. Between Pandas and Polars for data, PyTorch and scikit-learn for machine learning, and FastAPI for the services that sit in front of it all, there's rarely a reason to look elsewhere for this class of work — and a genuinely good one whenever there is.
One language, fewer handoffs
The same engineers who build the data pipeline can build the model and the API in front of it — nothing gets lost translating between teams or languages.
Mature, well-tested libraries
Pandas, FastAPI, PyTorch — the tools we reach for aren't experiments. They're battle-tested, well-documented, and have communities that catch problems before we do.
Fast to prototype, fine to ship
Python gets an idea working in hours, and with the right structure — typing, tests, proper packaging — that same code is fine to run in production, not just a notebook.
The default for AI-assisted build
Our AI-assisted tooling is strongest in Python. It's often the fastest path from a rough idea to something real, which matters when speed is the point.
Not sure Python's the right call? Ask the engineer who'd build it.
Book a 30-minute working session with a senior engineer — a real conversation about your stack, not a sales call.
What a Python build usually includes.
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Read moreThe stack we ship on.
Pragmatic, mostly boring, and chosen because it works in production — not because it's on the front page of Hacker News.
Have an outcome in mind?
We'll help you
ship it.
- You're building a data product and need a team that can deliver.
- You want to get AI-ready — pragmatically, not theoretically.
- Your reporting is a mess and you need a real platform underneath it.