Advanced Analytics & Machine Learning

Unlock the predictive power of your data. We help you identify high-value AI use cases and build the Machine Learning platforms to execute them. From rapid prototyping to operationalizing models, we turn algorithms into business value.
Data science shouldn't live in a lab. We bring it to production. We integrate ML tooling with your data platform, allowing you to train, deploy, and monitor models at scale. Whether predicting churn or optimizing inventory, we make AI practical.

Use-Case Identification & Prototyping

Identify and prioritize high-impact analytics scenarios, executing rapid Proofs of Concept (PoCs) to validate value before full-scale investment.

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Analytics & ML Platforms

Enable robust Machine Learning environments on your data platform, integrating notebooks and pipelines to support the full data science lifecycle.

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Operationalising Analytics

Move models from the lab to the front line, packaging analytics into APIs or dashboards and monitoring performance to prevent data and model drift.

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Use-Case Identification & Prototyping

We find the ROI. Not every problem needs AI. We workshop with your team to find the high-value problems—like forecasting or fraud detection. We build rapid prototypes to prove the concept works before you commit to a major project.
  • Identification and prioritisation of analytics/ML use cases.
  • Rapid prototyping and PoCs for high-value scenarios.

Analytics & ML Platforms

We build the workbench. We provide your data scientists with the tools they need. We set up secure environments in the cloud where they can access data, run notebooks, and train models without compromising production security or governance.
  • Enablement of ML tooling on top of data platforms.
  • Integration with notebooks, ML services and pipelines.

Analytics & ML Platforms

We build the workbench. We provide your data scientists with the tools they need. We set up secure environments in the cloud where they can access data, run notebooks, and train models without compromising production security or governance.
  • Enablement of ML tooling on top of data platforms.
  • Integration with notebooks, ML services and pipelines.

Operationalising Analytics

We deploy the intelligence. A model is useless in a notebook. We package it into an API or embed it into a dashboard. We monitor it for "drift" to ensure it stays accurate over time, handing it over to operations for long-term support.
  • Packaging models and analytics into dashboards or APIs.
  • Monitoring of model performance and data drift.
  • Handover to business and IT operations.
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