What we build

Custom solutions, from the data layer to the decisions made on top of it. Most projects combine two or three of these.

Predictive models

Statistical, machine learning and AI models trained on your data to forecast, classify, score and optimise. Each one comes with a clear account of how it was tested and how accurate it is, so you know how far to trust it.

Typical uses

  • Demand and sales forecasting
  • Consumption and price forecasting
  • Customer churn and propensity
  • Risk and credit scoring
  • Anomaly and fraud detection
  • Optimisation of resources and routes

What you get

  • A model trained and validated on your data
  • Results where you need them: an API, a scheduled job, a spreadsheet or a dashboard
  • Documentation your team can follow
  • Monitoring that catches performance drift

AI agents and automation

Agents built on large language models, and automated workflows, that handle repetitive, rules-heavy tasks. They work with your systems and documents, with a person in the loop wherever it matters.

Typical uses

  • Extracting data from documents, invoices and contracts
  • Answering questions over internal data and documents
  • Generating recurring reports
  • Classifying and routing requests
  • Connecting tools that don’t talk to each other

What you get

  • An agent or workflow connected to your systems
  • Clear limits on what it can and can’t do
  • Logs so every action can be checked
  • Hand-over and training for your team

Dashboards

Dashboards and reports that show the few numbers that matter, built for the people who use them. We work in Power BI, Looker Studio or Tableau, or build a custom web app when off-the-shelf tools fall short.

Typical uses

  • Management and KPI dashboards
  • Operational monitoring
  • Sales and commercial performance
  • Financial and budget tracking
  • Sharing results with your own clients

What you get

  • Dashboards connected to live data
  • Metric definitions everyone agrees on
  • Access set up by role
  • Training so your team can maintain them

Data infrastructure

The foundation everything else runs on. We design databases, build pipelines and model your data so it is consistent, documented and ready to use, whether you’re starting from spreadsheets or a mature stack.

Typical uses

  • Database and data warehouse design
  • Pipelines from your systems and external sources
  • Data cleaning and quality checks
  • Moving from spreadsheets to a proper database
  • Cloud setup

What you get

  • A documented data model
  • Automated, monitored pipelines
  • Quality checks that flag problems early
  • A setup your team can own

Not sure which you need?

That’s normal. Describe the problem and we’ll suggest where to start.

Email us