ML Model Development
Custom model development for classification, regression, clustering, ranking, time-series forecasting, and anomaly detection.
Machine Learning Development
Machine Learning That Moves from Notebook to Production
We build ML models that solve real business problems, then we deploy and maintain them in production — so you get lasting value, not just a proof of concept.
50+
Clients
100+
Projects
5+
Years
98%
Satisfaction
What We Do
A focused approach to real results
Most ML projects fail not in the modelling phase but in deployment. The model works in a notebook, then dies in production due to data drift, infrastructure gaps, or lack of monitoring. Foundrex closes this gap. We build ML systems with production in mind from the first design decision — proper data pipelines, model versioning, drift detection, and retraining triggers so your ML investment compounds over time rather than decaying.
Services Included
What's inside this service
ML Model Engineering
Take an existing research model and make it production-ready: optimised inference, containerisation, API wrapping, and monitoring.
MLOps Implementation
Build the infrastructure layer: CI/CD for models, feature stores, model registries, experiment tracking, and automated retraining pipelines.
ML Consulting
Independent assessment of your ML strategy, model quality, infrastructure, and team capability with concrete recommendations.
Data Pipeline Engineering
Build the data infrastructure that feeds your models: ingestion, transformation, validation, and feature engineering pipelines.
Model Fine-Tuning & Optimisation
Take an existing model and improve its accuracy, reduce its inference cost, or adapt it to a new domain.
Why Foundrex
Built different, for a reason
Production from day one
Every architecture decision we make — from feature engineering to model serving — is made with production reliability in mind, not just notebook accuracy.
Drift detection built in
We implement statistical monitoring that catches data and concept drift before it silently degrades your model's performance in production.
Reproducible experiments
We use experiment tracking tools so every model version, hyperparameter set, and evaluation result is logged and reproducible.
Handover that actually works
We document model architecture, training procedures, and operational runbooks so your team can own the system after we hand it over.
How We Work
Our process, from brief to launch
01
Data audit
We assess data quality, volume, labelling, and pipeline reliability before committing to a modelling approach.
02
Baseline model
We build a simple baseline model quickly to establish a performance benchmark and validate the problem is solvable.
03
Iterative development
We run experiment cycles — feature engineering, architecture changes, hyperparameter tuning — tracking every run.
04
Production deployment
We containerise the model, build the serving API, and deploy with monitoring and alerting.
05
Monitoring & retraining
We set up drift detection, performance dashboards, and retraining pipelines to keep the model accurate over time.
Common Questions
What people ask us
MLOps is the set of practices and tools that keep ML models working reliably in production — monitoring for data drift, automated retraining, version control for models, and deployment pipelines. If you have one model in production, you need the basics. If you have five or more, you need proper MLOps infrastructure.
We implement statistical drift detection that monitors the distribution of incoming data against training data. When drift exceeds a threshold, alerts trigger and retraining pipelines can run automatically or with human approval.
Yes. We assess the existing model's architecture, training data quality, feature engineering, and evaluation methodology, then propose targeted improvements with estimated impact.
Move Your ML from Experiment to Production
Book a free technical review. We'll assess your current ML setup and tell you exactly what needs to change.
Book a Free Consultation →