Insights You Can Act On
Applied Data Science
We apply machine learning and AI to create measurable business impact. Using explainable models, we enable leadership to act confidently on data-driven insights.
Microsoft credentials behind our AI and ML work
Data & AI on Azure 
Microsoft Fabric Featured Partner 
Digital App Innovation (Azure) 
Real-Time Intelligence Featured Partner Key Capabilities
Predictive Analytics & Forecasting
We build predictive analytics, forecasting, and scenario planning models that enable your organisation to anticipate trends, optimise resources, and make confident decisions about the future.
Process Optimisation & Efficiency Modelling
We apply process optimisation and operational efficiency modelling techniques that identify bottlenecks, reduce waste, and unlock measurable productivity gains across your operations.
Bespoke AI Solutions
We develop bespoke AI solutions with Azure OpenAI & Copilot integration that are tailored to your unique business challenges, delivering intelligent automation and augmented decision-making.
Semantic Indexing & Market Intelligence
We leverage semantic indexing, patent analysis, and market trend identification to surface hidden insights from unstructured data, giving your organisation a competitive intelligence edge.
Analytics-Driven Decision Support
We deliver analytics-driven decision support and accelerated idea-to-product conversion, empowering leadership with the insights they need to act quickly and confidently.
From business problem to production model
Average uplift in forecast or decision accuracy
Models delivered with explainability built in
Higher model adoption with embedded insights
Where Applied Data Science drives value
Demand and revenue forecasting
Replace spreadsheet-driven forecasts with probabilistic models that quantify uncertainty and feed directly into planning and finance processes.
Customer churn and lifetime value
Predict churn risk and customer value so marketing, success and retention teams can act on the right accounts at the right time.
Process optimisation
Model operational bottlenecks and simulate interventions so leadership can invest with confidence in the changes that move the needle.
Anomaly and fraud detection
Surface unusual transactions, network events or process deviations in near real time without drowning analysts in false positives.
Document and patent intelligence
Apply semantic search, summarisation and classification to large unstructured corpora to speed up research, underwriting and legal review.
Pricing and promotion analytics
Quantify elasticity and promotional uplift by channel and segment so commercial teams can optimise margin instead of guessing.
A proven delivery approach
- 01 Step
Frame
Co-define the business decision the model will support, success metrics, data availability and the cost of being wrong.
- 02 Step
Explore
Run rapid experimentation in Fabric Data Science or Databricks ML to validate feasibility before committing to a full build.
- 03 Step
Build
Engineer features, train and evaluate candidate models, and package the winning approach with explainability and bias checks.
- 04 Step
Operate
Deploy via MLOps with monitoring for drift, performance and fairness, and wire the model into the business workflow that consumes it.
Frequently asked questions
How is applied data science different from an AI proof-of-concept?
We only build models we expect to put into production. Every engagement starts with the business decision and the operational workflow that will consume the model — so the output is a running, monitored system, not a slide deck.
What platforms do you use for data science work?
Primarily Microsoft Fabric Data Science, Azure Machine Learning and Databricks, with MLflow for experiment tracking. We use Python, PySpark, and common libraries (scikit-learn, XGBoost, LightGBM, PyTorch) alongside Azure OpenAI for generative workloads.
How long does it take to put a model into production?
A focused use case typically takes 8–12 weeks end-to-end. Simple models on clean data can be quicker; heavily regulated use cases with model risk sign-off take longer.
How do you handle model explainability and fairness?
Explainability and bias assessment are built in from day one using SHAP, LIME and Microsoft Responsible AI tooling. We document datasheets, model cards and validation results so compliance and audit teams can review the model with confidence.
Who needs to be involved on our side?
A business sponsor, a domain expert, and a data or IT point of contact. Our team handles data science, engineering and MLOps; yours provides context and the decision rights that make the model useful.
Can you keep our models healthy after go-live?
Yes. We offer ongoing model monitoring and retraining through Synapx-as-a-Service, tracking drift, performance and data quality so your models stay accurate as the business evolves.
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