Python developers for machine learning in production.
If you need Python for machine learning and data, our engineers have a production system to show. The PyTorch and XGBoost demand-forecasting platform we built for FeelEat holds 98% accuracy in live operation, fed by Airflow pipelines and kept accurate by automated retraining. The same team scopes, ships and monitors your Python work.
We start with your data.
A paid discovery sprint
First we check whether this really is a machine-learning problem, and we audit the data you actually have today. The sprint closes with a scope document, an architecture and a costed roadmap.
Agreed cost, no running meter
The price and the schedule are set before any development. We price the outcome, so research time cannot quietly turn into an open-ended bill.
A senior Python team builds baseline-first
We build a simple baseline first, and any extra complexity has to beat it. The baseline also gives you a number to judge every later model against.
Retraining after launch
Automated retraining is built in from the start. When FeelEat adds dishes or opens sites, accuracy is tracked and the model retrains as new data comes in, which is how the forecasts stay at 98%.
The Python disciplines behind production ML.
AI / ML engineering
PyTorch, XGBoost and scikit-learn models with evaluation harnesses and monitoring, so they keep working once real data arrives.
AI / ML solutionsData engineering
Every model depends on this layer: Airflow pipelines, event capture and a warehouse that pulls scattered operational data into one place you can audit.
Data engineering & analyticsAI agents
Tool-using agents with deterministic guardrails, human approval steps and hard cost budgets, with every action written to an audit log.
AI agentsThe product around the model
The platform, APIs and telemetry around your Python core, built by the same company, so the model has a product to live in.
SaaS app developmentA Python forecasting platform that refreshes every four hours.
FeelEat demand forecasting — Python over a live operation
For FeelEat we built demand forecasting per site and per dish in Python. PyTorch learns the long-range patterns (seasonality, trend, how a promotion ramps up and fades), and XGBoost turns the combined features into sharp, explainable decisions. Airflow runs the pipelines on PostgreSQL and AWS.
Questions teams ask before hiring Python developers
Put it in a brief. A senior engineer reads it and replies within one business day.
What kind of Python work does CODT Technologies 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 clear about the limits. Our named web platforms run on TypeScript and Node.js, so if you need a Django storefront, we will tell you. Hire our Python developers for the modelling and data layer that has to be right.
Why PyTorch and XGBoost together?
Each tool does what it is best at. PyTorch learns patterns over time (multi-year seasonality, slow trends, how a promotion ramps up and fades) and feeds them back as features. XGBoost then makes sharp, explainable decisions over the full feature set. That split is behind the 98% accuracy in production.
How do you keep a model accurate after launch?
Accuracy is checked against real outcomes, and drift triggers retraining on fresh data. A new site starts from models trained on similar sites until it has data of its own.
What results has your Python work produced?
The forecasting platform we built for FeelEat runs at 98% accuracy and cut stockouts by 58% and excess stock by 41%. The full case study, with how it was built, is linked on this page.
Do Python engagements include the data pipelines?
Yes. Engagements cover event capture, Airflow pipelines, warehousing and governance, so the ML layer reads from one auditable source and nobody has to merge CSV files every night.
Who owns the code, the model and the data?
All of it is yours: the Python code, the trained weights, the pipelines and the infrastructure configuration, handed over at delivery. A support copy stays with us only with your written permission, and we sign an NDA when asked.
How fast can Python developers start?
A senior engineer replies within one business day. Next comes a paid discovery sprint, which ends in a written scope and a fixed quote. The build team starts 4–5 business days after you sign the quote. On Python work the sprint also covers problem framing and a data audit, so both are done before any model is built.
Have a problem worth
solving well?
Tell us what you are building and the deadline it has to meet. You will get a clear read on whether we fit and what the work would take.