Technologies / Python
Python for data pipelines, ML integration, and backend services. Modern, typed, tested Python — not the mess of scripts most Python estates become.
Python is our default for data-heavy and ML-adjacent workloads: pipelines, extraction, model integration, and the automation scripts that turn into production systems. We write Python with type hints, tests, and operational discipline — because 'it's just a script' is how most data outages start.
We use Python where it earns its place. These are the situations we see most often.
A one-off Python script is now the only reason the month-end report exists. Nobody has re-read it in two years.
A model exists in a notebook. Nothing consumes it, because there's no service around it.
requirements.txt with pinned-but-conflicting versions, no environment reproducibility, and Python 2 still hiding somewhere.
We start with your business problem — the process, the users, the constraints — before naming any technology, including this one.
A clear architecture decision record: where this technology fits, where it doesn't, and what it integrates with.
Small, demonstrable increments on real data. Real users, real feedback, real outcomes — not a proof of concept nobody uses.
Operational metrics, documentation, and a runbook your internal team can operate. We're not a lock-in vendor.
Data pipelines with lineage, testing, and observability — auditable when the number is questioned.
ML integrated into operational workflow via well-defined services, not notebooks.
Reproducible environments (uv, poetry, or pip-tools) and CI that runs the same checks as your laptop.
Python that new engineers can read and extend without archaeology.
Ingest, transform, and load with dbt, Airflow, Prefect, or plain Python — chosen for the scale and cadence.
Typed, documented, tested APIs — the modern Python choice for web services.
Wrapping models — your own, or via OpenAI/Anthropic/local — into services with retries, guardrails, and observability.
The 'boring' automations that free up operational time — done properly so they don't become the next production risk.
Working alongside data scientists to move models from notebook to service, with the engineering discipline that transition needs.
Modernising Python 2, un-typed, or dependency-rotten estates into something a team can maintain.
A regulated operator needed a nightly data pipeline pulling from six sources, applying rules, and producing an audit-ready dataset for reporting. Built in Python with dbt and Airflow, with full lineage — the pipeline replaced a spreadsheet that had been quietly failing quarterly.
A B2B operator was hand-extracting structured data from PDFs. We built a Python service using an LLM with structured output and a human-review queue for low-confidence cases, cutting hand-processing dramatically.
Vignettes are anonymised composites drawn from engagements and product work. No client names, logos, or performance figures are implied.
FastAPI for new services (typed, async, great DX). Django where the batteries-included model earns its place, especially with existing Django estates. Flask rarely for greenfield.
uv is our default now; poetry and pip-tools where projects already use them. Reproducible, locked, and CI-verified.
Yes — pipeline, warehouse, and observability. Often adjacent to a dashboards or reporting engagement.
We integrate and productionise models more often than we train them from scratch. Where training is required, we partner with data science specialists.
Yes, we've migrated them. Boring, valuable, and best done in structured phases.
Whether it's a data pipeline, an ML integration, or a script that grew up too fast — we'll give you an honest view on what Python should do here.
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