We build AI/ML systems that are accurate, scalable, and maintainable in production — not just impressive prototypes that never reach users.
RadiCorp's AI & ML engineering team brings together data scientists, ML engineers, and MLOps specialists to deliver AI systems that actually work in production. We cover the full lifecycle from business problem framing to deployed, monitored, continuously-improving models.
We work across verticals — fintech, e-commerce, manufacturing, healthcare — applying ML to forecasting, recommendation, anomaly detection, NLP, and computer vision use cases. We also help organizations integrate large language models (LLMs) and build GenAI-powered products responsibly.
We work with the industry's leading frameworks and platforms to deliver robust, production-grade machine learning systems.
A structured, iterative process that ensures your AI investment translates into real, measurable business value.
We start with your business objective — not with algorithms. We identify high-value use cases, assess data readiness, and establish success metrics before writing a single line of code.
Data pipelines, feature stores, and training dataset construction. We ensure data quality, handle class imbalance, and build reproducible feature engineering workflows.
Rigorous experimentation with tracked runs in MLflow. We evaluate multiple algorithms, tune hyperparameters, and validate models against held-out test sets and business benchmarks.
Productionise models via CI/CD for ML, model registries, containerised serving, and staged rollout strategies — REST APIs, batch inference, or edge deployment as needed.
Continuous model performance monitoring, data drift detection, and automated alerts. We implement responsible AI checks — bias audits, explainability reports, and fairness dashboards.
AI & ML works best when paired with a solid data foundation and cloud infrastructure. Explore the services that complement your ML platform.
Your ML models are only as good as your data. We build the high-quality data lakes, pipelines, and feature stores that feed reliable, production-grade AI systems.
Learn MoreFrom exploratory analysis to predictive dashboards, our data science practice transforms raw data into the insights that drive AI use-case prioritisation and business decisions.
Learn MoreDeploy and scale ML workloads on AWS, Azure, or GCP with the right compute, storage, and networking architecture. We handle the cloud so you focus on the models.
Learn MoreWhether you're starting from scratch or scaling an existing experiment, our ML engineers are ready to build something that ships and performs.