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Services · Data engineering

Data engineering

We get the data out of any system and ensure it passes through all transformations to end up in your reports, or any other system. We work with traditional and modern data stack, centralized and mesh models. Creating the only platform you need to reconcile data across different platforms, systems and AI solutions.

One dictionary of metrics instead of five opinions.

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You are in the right place if

  • You need a data platform for the whole company or for one domain, and want it to outlive the first project.
  • Data has to be extracted, loaded and transformed from many sources, including the ones nobody wants to touch: old ERPs, cubes, retailer files, partner APIs.
  • Reports and KPIs are assembled by hand every period, and the definitions differ between the people who assemble them.
  • You want to move from descriptive to predictive analytics, or to put an AI assistant over your data, and the data is not ready for it.
  • Your BI has dozens of dashboards and still no agreed number at the review.
  • You want decisions made on data, and the first step is making the data trustworthy.

What's included

Pipelines and data platforms

From the first pipeline to a platform the whole company runs on. Centralised warehouse or data mesh, depending on how you are organised. Classic and modern stacks; on-premise, your cloud or ours.

  • Data integration from any source: ERP, cubes, files, APIs, retailers and partners
  • Processing and transformation pipelines with monitoring
  • Data architecture for a domain or the enterprise
  • Data security and access
  • Migration between platforms

Semantic layer for LLM

The layer that lets an agent, a dashboard and a planning model give the same number. One dictionary of metrics, hierarchies and definitions, with permissions, owned by your analyst. Built so that an AI assistant composes queries from it instead of guessing at raw tables.

  • Metric dictionary and definitions
  • Hierarchies and dimensions shared across BI, planning and AI
  • Permissions inherited from your identity provider
  • A review queue for the questions the dictionary cannot answer yet

Master data management and mapping

Customers, products and locations are called different things in every system and in every retailer's file. We build the mappings and the rules that keep them current, so sell-in, sell-out, promo and plan line up by customer and SKU.

  • Customer and product master data across systems
  • Retailer-to-manufacturer mapping
  • Data quality rules and exception handling

Analytics and reporting

A model of indicators for the business, the dashboards and reports built on it, and the automation that produces them on schedule. Analytics inside your applications, self-service BI for the teams that want it, AI assistants on top of the dashboards.

  • Dashboards and automated periodic reporting
  • Embedded analytics in business applications
  • Self-service BI and training for your analysts
  • AI assistants in dashboards

How we work

  1. Data discoverySources, owners, volumes, the three numbers people argue about most. A written scope.
  2. First pipeline and first dictionaryOne domain end to end: extraction, transformation, definitions, one report that reconciles.
  3. Scale outMore sources and domains on the same platform; monitoring and data quality in every release.
  4. RunSupport and evolution of the platform, with named people.

Why us

  • We have built data platforms, not only reports.Infrastructure and logic, from ingestion to the dashboard.
  • Consumer goods is our domain.Sell-in and sell-out, promo and trade terms, assortment and logistics: we know what the data means before we load it.
  • Classic and modern stacks.We choose the stack the task needs, not the one we like.
  • Security by design.Access, audit and data residency are part of the architecture.
  • We help you use the data, not only store it.Definitions, training and a dictionary your analysts own.

Start with the three numbers you argue about most

Tell us which three, and we will come back with a scope for the first pipeline and the first dictionary.

Discuss a project