Forecasting
Demand, revenue, headcount and cash — with intervals, backtests and an honest account of where the model is weak.
Talk this throughData science & machine learning
Models that survive contact with production
Forecasting, segmentation and lifetime value — deployed, monitored and retrained, rather than left in a notebook with a good validation score.
MWRKJT
Delivery leads online — talk to a human, not a botp10–p90Bands, not point estimates
TestedPyTest & Great Expectations
MonitoredDrift & retraining
Forecast with intervalBacktested on history
100% delivery success — delivered worldwide across seven regions
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
AWS
Microsoft Azure
Google Cloud
Databricks
Snowflake
dbt
Power BI
Looker Studio
Azure Synapse
MongoDB
Python
LangChain
What you get
A model is a decision aid, not a deliverable
We work backwards from how the output gets used. If a forecast cannot change a purchase order, the accuracy does not matter — so we design the intervention alongside the model.
- Baseline first: naive forecast, current rule, human judgement
- Backtesting on real historical windows, not random splits
- Uncertainty published as intervals so people can act on risk
- Drift monitoring and a retraining schedule agreed up front
- The intervention designed with the team who will run it
Model in contextDrivers and contribution
Capabilities
Modelling we do most often
Chosen because they change a decision somebody makes weekly.
Customer Segmentation
Segments defined by behaviour and value, sized so a marketing team can act on them without a data scientist in the room.
Talk this throughLifetime Value Modelling
Predicted value by cohort and channel, so acquisition spend is set against future contribution rather than last-click revenue.
Talk this throughDeep Learning & Vision
Document extraction, image and signal models where classical methods genuinely fall short — and only then.
Talk this throughMLOps
Feature stores, model registries, monitoring and retraining pipelines so the model still works in month nine.
Talk this throughBy the numbers
Machine learning in numbers
Grounded in real ML work — client segmentation and predictive maintenance, running in production.
0Named ML builds: wealth segmentation & IoT predictive maintenance
0%Models shipped with drift monitoring attached
0%Pipelines validated with PyTest & Great Expectations
0Client PII exposed — integrity built in
Delivery model
How the work actually runs
One delivery model, whatever the engagement. You always know what is shipping this week and what it is worth.
01
Discovery
We map your sources, decisions and blockers, understand the business goal, and agree what success looks like before anything is built.
02
Solution design
We design the architecture, data models and use cases — right-sized to your team and your cloud — and cost the work before you commit.
03
Development
Weekly sprints against a visible backlog: pipelines, models, dashboards and applications, all in version control and yours from day one.
04
Testing & validation
Every pipeline and model is tested — with frameworks like PyTest and Great Expectations — so the numbers are trustworthy before they ship.
05
Deployment & training
We deploy into your cloud and train the people who will run it, so the capability lands with your team, not just our code.
06
Support & optimisation
Observability, alerting and continuous improvement keep the system fast, accurate and cost-efficient long after go-live.
Proof
Recent work
Marketing · EMEA
Marketing and CRM data unified across 20+ EMEA countries
A digital-transformation programme that consolidated marketing and CRM data across the region on Azure and Power BI, with a custom lead-management automation built on Power & Logic Apps.
80%Manual workload cut
20+EMEA countries
FMCG · Nestlé
Workforce analytics for Nestlé’s supply & demand planning
HR data marts on SAP feeding automated daily ingestions through AWS Glue and Databricks, on a medallion architecture, so planners could see available resource against production targets.
DailyAutomated ingestions
MedallionData architecture
Data strategy · Germany
One source of truth from 50+ scattered sources
Data strategy and cloud architecture on Google Cloud, ingesting 50+ sources through Stitch, modelled in dbt and surfaced to the CEO and Head of Product in Looker Studio.
50+Sources unified
ExecLive dashboards
FAQ
Questions buyers actually ask
Straight answers. If yours is not here, ask us on a call — we will tell you if we are the wrong fit.
Ask a questionLess than people assume for forecasting, more than people hope for deep learning. We will tell you in the first two weeks whether the data supports the question, and we have told clients no.
That is the point of the MLOps work. Registries, monitoring and retraining are automated, and the runbook tells your analysts what to do when drift alerts fire.
Always. Custom modelling is expensive to build and more expensive to maintain. If a well-tuned baseline or a managed service gets you 90% of the value, we will say so.
We test performance across the segments that matter for your context, document known weaknesses, and design a human review path for decisions that affect individuals.
Next step
Ready to find out what your data is worth?
Thirty minutes with a delivery lead, not a salesperson. You leave with a point of view on your highest-value use case and what it takes to ship it.