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One data foundation for your business.Shop, ERP and marketing data in one model.

We build the pipelines and the data model that bring shop, ERP and marketing data together: synchronised on a schedule, reconciled against the source systems and ready for dashboards, forecasts and AI. We run a platform like this ourselves – on PostgreSQL, with hourly catalogue syncs and a unified order history from Shopify and plentymarkets.

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  • Reconciled against the source systems
  • Your database, your data
  • Monitoring for every pipeline
  • Documented data model

Tell us about your data

Tell us which systems hold your data and which questions you cannot answer today. You get a written answer within one business day.

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Every source in one model

Example
Data platform

Sources

  • Shoporders · customers
  • ERPstock · purchasing
  • Emailsends · clicks
  • Adsspend · campaigns

One model, one source of truth

orders_unified

hourly sync

datechannelcountrycustomer
  • 11-12shopDEnew
  • 11-12marketplaceATreturning
  • 11-12shopNLnew
  • 11-11shopDEreturning
  • 11-11marketplaceDEnew
  • 11-11shopFRreturning

Orders per week · all channels

PostgreSQL
The data platform we run
Hourly
Catalogue sync
Shopify + ERP
One order history
One model
For reports, forecasts and AI

Is this you?

Every system has numbers. They do not agree.

The data is there – spread over the shop, the ERP, the marketing tools and a few spreadsheets. What is missing is one place where it fits together.

Three systems, three truths

The shop reports one revenue figure, the ERP another, the marketing tool a third. Every meeting starts with the question of whose number is right.

Reports built by hand

Someone exports CSV files and merges them in a spreadsheet. The report is out of date when it is finished, and nobody else can reproduce it.

History that is gone

Many interfaces return only the current state or a limited window. Without your own storage, the picture from last year cannot be reconstructed.

Forecasts and AI with nothing to stand on

Forecasting and AI need clean, joined, historical data. Without that foundation every model is guessing.

What we build

From the source systems to a model you can query.

We build the layer between your operational systems and everything that wants to read from them. Six areas we work in:

Data pipelines and sync

Scheduled and event-driven pipelines from shop, ERP, marketing and other systems – incremental where possible, with retries, paging within the limits of each API and backfills for history.

Data model and database

One relational model on PostgreSQL in which orders, products, customers and stock from different systems mean the same thing. Documented, with keys that match across sources.

Unified order and product data

One order history and one product catalogue across shops and ERP, with sales figures per product – the way we run it for the stores we operate.

Data quality and reconciliation

Counts and totals checked against the source systems, duplicates and gaps detected, differences reported instead of silently carried along.

Dashboards and reporting

Dashboards and scheduled reports on top of the model: revenue, stock, customers, marketing – with the same definitions in every view.

Foundation for forecasts and AI

Prepared, historical data sets that forecasting models and AI agents can work with – including rules for who and what may read which data.

How it works

From inventory to a running data platform.

We agree on the model before we build pipelines, and we connect one source at a time. Each load is checked against the system it came from.

  1. Step 01

    Data inventory

    We list the systems, their interfaces and limits, and the questions the data should answer. You get a written plan: sources, target model, order of work.

  2. Step 02

    Data model

    Entities, keys and definitions – what counts as an order, as revenue, as a customer – agreed in writing before the first pipeline is built.

  3. Step 03

    Pipelines and backfill

    Pipelines are built source by source, history is loaded and each load is reconciled against the source system.

  4. Step 04

    Dashboards and access

    The first dashboards and reports on the new model, plus access for your tools and your team.

  5. Step 05

    Operate and extend

    Monitoring with alerts for failed or late loads, scheduled reconciliations and new sources when you need them. Or a hand-over to your team.

Proof

Stores whose data runs through our platform.

Stores we operate. Their daily business runs on the platform we built for our own operations – production, not a demo setup.

All case studies
Klean Kanteen – Case study

Migration · Long-term partnership

Klean Kanteen

Klean Kanteen Germany, operated since 2017: Shopify store connected to warehouse management and the fulfilment partner, with catalogue, orders and campaigns handled in daily operations.

Platform
Shopify
Type
Migration
Status
Completed
Case study
MADSea – Case study

Migration

MADSea

MADSea: a Shopify store we run operationally – catalogue and order sync into our operations platform, loyalty programme computed from order data, events passed on to Klaviyo.

Platform
Shopify
Type
Migration
Status
Completed
Case study

Working together

Model first. Then source by source.

A data platform is useful when people trust its numbers. So every pipeline is reconciled against its source before anyone builds a report on it. Scope and price are agreed in writing before we build.

Getting started

Data inventory and model

Sources, interfaces, definitions and the target model – written down before anything is built.

  • List of sources with their limits
  • Agreed definitions for the key figures
  • Plan with the order of work

The foundation

Data platform build

Database, pipelines, backfill of history and the first dashboards, connected source by source.

  • PostgreSQL in your own account
  • Reconciliation for every pipeline
  • Documented model and pipelines

Keep it running

Operation and extension

We run the pipelines, react when an interface changes and add sources, reports and data sets as you need them.

  • Monitoring with alerts
  • Scheduled reconciliations
  • New sources and reports on request

What we commit to

  • The database and the data belong to you
  • Documented data model and definitions
  • Reconciliation against the source system for every pipeline
  • Monitoring with alerts for failed or late loads
  • Standard technology: PostgreSQL and SQL, no proprietary format
  • Scope and price agreed in writing before the build

FAQ

Questions companies ask before they build a data platform.

Do we need our own data platform, or is a reporting tool enough?
A reporting tool shows what one system knows. As soon as a question needs two systems – revenue from the shop against stock from the ERP, or orders against marketing contacts – the data has to be joined somewhere first. If your questions stay inside one system, we tell you that a reporting tool is enough.
Which systems can you connect?
Shopify, plentymarkets and Klaviyo we run in production ourselves. Other systems with a REST or GraphQL API, CSV exports or database access we connect the same way. If a system has no interface at all, we tell you so in the inventory.
Why PostgreSQL?
It is open, widely supported and well documented. Reporting tools can read it and your developers already know SQL. If your data volumes or your existing setup call for something else, we say so in the inventory.
How current is the data?
As current as the use case needs. On our own platform the catalogue syncs hourly and the order table is updated after each order sync; events can be processed as they arrive. More frequent is possible, but not always worth the effort.
What about historical data?
We load it. Where an interface limits how much a single query returns, we load in windows – month by month, for example – and reconcile the counts afterwards.
Where does the data live? Is it GDPR-compliant?
In a database in your own account, in an EU region if you want. Access is restricted per role and credentials are stored encrypted. We sign a data processing agreement and document which data flows where.
How does this relate to AI and forecasting?
It is the precondition. An AI agent that should answer a question about orders needs a place to look them up; a forecast needs history in a consistent form. We build the data foundation so that both can be added later without starting over.
What does it cost?
It depends on the number of sources, their interfaces and how much history is needed. After the first call you get a written scope and price for the inventory and the model; the build is quoted once the model is agreed.

Which question can your data not answer today?

Tell us. We come back with a slot for a first call and a first take on which sources and which model it would need – no obligation.

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