Languages & Frameworks
Python (pandas, scikit-learn, PyTorch, TensorFlow)
R
SQL
Services > Data Science
Data science that changes decisions, not just dashboards.
We build production models, forecasts and analytics that earn their place inside the workflows where money actually moves.
— Is this right for you?
Recognise any of these?
If your organisation is facing any of these challenges, data science is likely the right next step.
01
Dashboards without decisions
Dashboards everywhere, decisions still based on gut.
02
Notebooks that never ship
Models built in notebooks that never reach production.
03
Forecasts that miss
Forecasts that miss because they ignore real-world drivers.
04
Signals seen too late
Churn or fraud signals visible in hindsight but not in time to act.
05
Analytics no one trusts
Sales and operations teams who do not trust the analytics they are given.
— how we work
How we deliver results
We frame the business decision first, then build the model to change it — not the other way round.
01
Assessment / Discovery
Business question framing — what decision should this work change? — followed by a data audit and feasibility assessment before any modeling starts.
02
Strategy / Planning
Analytical approach, feature engineering plan, model selection, evaluation framework with business-aligned metrics and a path to production.
03
Implementation / Delivery
Data pipelines, model development and validation, productionization, integration into business workflows and dashboards, with clear documentation of assumptions and limits.
04
Optimization / Support
Model performance monitoring, drift detection, scheduled retraining, ongoing evaluation against business KPIs, and decommissioning models that no longer earn their keep.
— What we deliver
Common use cases
Real delivery patterns across industries and complexity levels — not theoretical capability.
Demand forecasting for retail and supply chain.
Customer churn prediction and intervention.
Customer and product segmentation for marketing.
Fraud and anomaly detection on transactional data.
Pricing optimization.
Predictive maintenance for manufacturing assets.
— Free resource
Take our guide with you.
Download our guide for analytics leaders deciding which model deserves production budget.
— FREE WHITEPAPER
From notebook to production: a data science delivery checklist.
Covers feasibility scoping, evaluation frameworks tied to business KPIs, and what monitoring a production model actually requires.
- How to frame a business question before you model anything
- A go/no-go feasibility checklist
- Evaluation metrics tied to business KPIs, not just accuracy
- What drift monitoring looks like in year one
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The right tools for the job, not a fixed stack.
We select technology to fit your requirements and existing estate — every layer engineered for speed, built for trust and designed to adapt.
Orchestration
Airflow
Prefect
dbt
Visualization
Power BI
Tableau
Looker
MLOps
MLflow
Kubeflow
Native cloud ML (AWS/Azure/GCP)
Data Platforms
Databricks
Snowflake
BigQuery
Postgres
Apache Spark
— SEctor experience
Industries we serve
Real delivery experience across regulated and high-growth sectors — not first-time builders.
Retail & E-commerce
Banking & Financial Services
Insurance
Manufacturing
Healthcare
Logistics
3–5 mo
Typical idea-to-production timeline
2
CUBE companies with dedicated data science practices
6
Core industries with proven delivery history
— Client stories
What our clients say
Discover how our clients have transformed their ideas into successful projects through collaboration, creativity, and solutions tailored to their needs.
"Forecast accuracy went from a running joke to something planning actually relies on."
Head of Commercial Analytics, consumer goods distributor
"Their data scientists sat with our own analysts instead of disappearing into a lab."
Director of Data, regional bank
— FAQ
Frequently asked questions
What is data science, and how is it different from analytics?
Data science builds models — forecasts, classifications, recommendations — that are statistically validated and shipped into production systems, going further than dashboards or descriptive analytics by predicting and automating decisions.
How much data do we need before a data science project is worthwhile?
Less than people think, but the data must be clean and connected to a decision. We answer this in week one.
How long from idea to a model in production?
Typical predictive use case: three to five months. Faster for well-scoped, well-instrumented problems.
What about model governance?
We document assumptions, validation, performance and limits, with monitoring and review cycles built in.
Can you work with our existing data team?
Yes — most engagements are blended, with our data scientists working alongside your analysts and engineers.
How much does a data science engagement cost?
Pricing depends on data readiness and complexity. Typical predictive use cases take three to five months; we quote after a feasibility assessment, with fixed fee for scoped phases and T&M for ongoing model support.
— Explore more
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Learn moreGot a project you want to talk about?
No matter how complex the challenge or specific the need, we have the expertise, scale and flexibility to deliver. Let's build something exceptional together.
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