Turn business data into decisions, predictions & intelligent products.
Portalwiz builds data science and machine-learning solutions that help teams understand patterns, forecast outcomes, personalise experiences and automate decisions—integrated into real business workflows.
From a measurable business question to production-ready insight and intelligent workflows.
Models matter when they improve real decisions.
Our data science work connects business questions with data preparation, experimentation, modelling, evaluation and production integration—so analytics can move beyond dashboards into decision support.
From raw data to business intelligence.
The model is only one part of the solution. We connect data engineering, experimentation, machine learning and application engineering so insights can become usable decisions.
Data Preparation
Data discovery, cleaning, transformation, feature preparation and analytical datasets.
Machine Learning
Classification, regression, forecasting, recommendation, clustering and model experimentation.
Analytics & Forecasting
Dashboards, trend analysis, predictive insights, scenario modelling and decision support.
Operationalisation
APIs, workflows, monitoring and integration of models into business applications and processes.
Applied ML for real operating problems.
Use cases are shaped by the quality of available data, the business decision to improve and the level of automation appropriate for the workflow.
Lead scoring & propensity
Prioritise prospects using behavioural, demographic and engagement signals.
Demand forecasting
Model demand patterns to improve planning, allocation and commercial decisions.
Segmentation & personalisation
Group audiences or customers and adapt journeys, content or recommendations.
Anomaly & risk signals
Surface unusual patterns that deserve human review or operational intervention.
Text & document intelligence
Classify, summarise, extract and structure information from unstructured content.
Decision-support systems
Combine analytics, rules and models inside business dashboards and applications.
Business question first.
Model second.
We start with the decision that needs to improve, test whether the data can support it, build a measurable proof of value, and only then industrialise the solution.
Business question, success measure and constraints.
Data quality, signals and feasibility.
Models, baselines and validation.
API, app, dashboard or workflow integration.
Monitoring, drift, learning and iteration.