Python developers measured in production, not in notebooks.
Hiring Python developers from CODT Technologies gets you engineers whose Python runs live operations: the PyTorch and XGBoost demand-forecasting platform we built for the FeelEat group holds 98% accuracy in production, with Airflow pipelines feeding it and automated retraining keeping it honest. Python for machine learning and data — scoped, shipped and monitored.
Data first, model second, production always.
A paid discovery sprint
We start by asking whether this is actually a machine-learning problem — and by auditing the data you have, not the data you wish you had. The output is a written scope, an architecture and a costed roadmap.
A fixed, transparent quote
Cost and timeline are agreed before development starts, priced by outcome rather than by the hour — so research never quietly becomes an open-ended meter.
A senior Python team builds baseline-first
A simple baseline first, then complexity that has to earn its place against it. Models run in shadow mode against the humans they will assist until the track record — not the math — wins the argument.
Ongoing care keeps the model honest
Menus rotate, sites open, seasons shift. Automated retraining and accuracy monitoring are part of the build, not an afterthought — that layer is why FeelEat’s forecasts hold 98% over time.
The Python disciplines behind production ML.
AI / ML engineering
PyTorch, XGBoost and scikit-learn models with evaluation harnesses, shadow-mode trials and monitoring — machine learning built to survive contact with production.
AI / ML solutions →Data engineering
The layer every model stands on: Airflow-orchestrated pipelines, event capture and warehousing that turn scattered operational data into one auditable source of truth.
Data engineering & analytics →AI agents
Tool-using agents with deterministic guardrails, human-in-the-loop approval gates and enforced cost budgets — automation with an audit log.
AI agents →The product around the model
A model ships inside a product, not a notebook — the platform, APIs and telemetry around your Python core, engineered by the same company.
SaaS app development →Python that paid for itself in a quarter.
FeelEat demand forecasting — Python over a live operation
For the FeelEat group we built per-site, per-dish demand forecasting in Python: PyTorch learns the long-range temporal structure — seasonality, trend, how a promotion ramps and decays — and XGBoost turns the combined features into sharp, interpretable decisions, with Airflow orchestrating the pipelines on PostgreSQL and AWS. The model ran in shadow mode until its track record won the argument, then paid for itself in the first quarter.
Questions teams ask before hiring Python developers
Zet het in een briefing. Een senior engineer — geen verkoper — reageert binnen één werkdag.
Q.01What kind of Python work does CODT actually do?
Production machine learning and the data engineering under it — PyTorch, XGBoost, TensorFlow, scikit-learn and ONNX for models; Airflow, PostgreSQL and AWS for the pipelines that feed them. We are deliberate about the boundary: our named web platforms run on TypeScript and Node.js, so if you need a Django storefront, we will say so rather than stretch. Hire our Python developers for the modelling and data layer that has to be right.
Q.02Why PyTorch and XGBoost together?
Each tool does what it is best at. PyTorch learns pattern over time — multi-year seasonality, slow trend, how a promotion ramps and decays — and feeds those learned representations back as features. XGBoost then makes sharp, interpretable decisions over the combined feature set. That division of labour is how FeelEat’s forecasts hold 98% in production.
Q.03How do you keep a model accurate after launch?
The same way we earned trust before it: evidence. Models run in shadow mode against human decisions until the track record wins; in production, automated retraining and accuracy monitoring catch drift as menus rotate and seasons shift, and new sites cold-start from transfer off similar ones until local data arrives.
Q.04What results has your Python work produced?
The forecasting platform we built for the FeelEat group runs at 98% accuracy, cut stockouts by 58% and excess stock by 41% — and paid for itself in the first quarter. The full case study, including how it was built, is linked on this page.
Q.05Do Python engagements include the data pipelines?
Yes — a model is only as honest as the data reaching it. Engagements cover event capture, Airflow-orchestrated pipelines, warehousing and governance, so the forecasting or ML layer sits on one auditable source of truth instead of a nightly CSV ritual.
Q.06Who owns the code, the model and the data?
You do — 100%: the Python code, the trained weights, the pipelines and the infrastructure configuration, transferred at delivery. We keep a copy for support purposes only with your written permission, and an NDA on request is standard.
Q.07How fast can Python developers start?
A senior engineer reviews your enquiry and replies within one business day. Every engagement opens with a paid discovery sprint — problem framing and a data audit — that produces a written scope and a fixed, transparent quote before any model is built.
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