What's included
Every engagement covers the complete pipeline: data exploration, model development, rigorous evaluation, and production deployment.
Predict categorical outcomes or continuous values — customer churn, lead scoring, pricing models — trained on your own historical data.
Collaborative filtering and content-based models that surface the right product, content, or action to the right user at the right time.
Anomaly-based and supervised classifiers that flag suspicious transactions in milliseconds — with explainability built in for compliance.
Time-series models (XGBoost, Prophet) that predict inventory needs, staffing requirements, or revenue — reducing waste and stockouts.
MLflow-tracked experiments with automated drift detection so your model stays accurate in production — not just at launch.
Your model shipped as a FastAPI endpoint, Dockerised and deployed to Vercel or your cloud of choice — with docs and a test suite included.
How it works
We define the prediction target, success metric, and business impact. This step prevents wasted model training — the most common ML project failure.
Exploratory data analysis to understand distributions, outliers, and feature importance. You get a written EDA report before modelling begins.
Multiple algorithms evaluated and compared. Full performance report with precision, recall, F1, and business-impact estimates. You choose what ships.
Model wrapped in a FastAPI service, containerised with Docker, deployed, and wired to MLflow for ongoing drift monitoring and retraining triggers.
What you get
Tech stack
Battle-tested ML tooling from experimentation through to containerised production deployment.
Pricing
Scoped per project — not per hour. Every plan includes full code ownership.
Starter
$800 – $1,500
A single production-ready ML model for one well-defined problem.
Growth
$2,500 – $5,000
A complete ML system with monitoring and a clean API.
Enterprise
Custom
End-to-end ML platform with ongoing model maintenance.