Machine learning that reaches production
ML models that solve a real problem and stay useful in production, backed by MLOps and governance.
Our approach
We design, build and deploy ML models that solve a real business problem and stay useful in production, backed by proper MLOps and governance. Deployment, monitoring and governance are designed in from the first sprint.
Problem-First Modelling
Propensity, churn, targeting, forecasting and other prediction and optimisation problems scoped against a defined business outcome the model will move.
MLOps
Training pipelines, model registries and serving infrastructure built as a coherent system, so retraining and promotion become routine operations.
Monitoring
Drift, quality and business-metric monitoring so models keep working after launch, and degradation is caught before it reaches users.
Governance
Model cards, approvals and audit trails that satisfy technical and regulatory reviewers, so production sign-off does not stall.
What's in the way
Most machine learning projects never leave the notebook. The maths is rarely the problem. The failure is in deployment, monitoring and governance, so models drift, break, or never ship.
How we deliver
Senior data scientists and ML engineers, onshore, who have put models into production and kept them there. Every model ships with the monitoring and governance to keep it reliable.
When to bring us in
For organisations with a defined prediction or optimisation problem, propensity, churn, targeting or forecasting, that need the model running in production and monitored.
What you'll see
Returns you can point at
Models in production returning multiples on the program they support, from lead scoring to campaign targeting.
Built to stay useful
MLOps, monitoring and retraining designed in, so performance holds after the model ships.
Defensible by design
Model risk documentation, evaluation frameworks and champion-challenger workflows, so risk and compliance can sign the model off.
We learned a lot about what good looks like from a marketing and analytics execution point of view. The team delivered and embedded best practice knowledge within our teams.
Will Creedon
Founder and CEO, Alloggio
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Common questions
What is machine learning consulting?
Machine learning consulting is help designing, building and deploying ML models that solve a real business problem. Xephyr focuses on models that reach production and stay useful, backed by proper MLOps and governance, because a model that never ships delivers nothing.
What is MLOps and why does it matter?
MLOps is the practice of deploying, monitoring and maintaining machine learning models in production. It matters because most models fail not in the maths but in the operations: without MLOps they drift, break, or never leave the notebook.
Why do so many ML projects never reach production?
Usually because the work stops at a proof of concept with no plan for deployment, monitoring or governance. We build for production from the start, which is why our models reach it and stay there.
See what AI can do for you
Request a personalised demo and see how our solutions work with your data.