We already run a data platform. Where do we start?

With a single use case directly on your platform, typically four to six weeks until the model is productive. Architecture and operations stay as they are unless a specific need for change arises.

When is a central platform worthwhile?

When several source systems come together, several teams need the same figures, AI initiatives are meant to go into production, or data lineage and access control become a requirement. With a single source, the existing BI tool is often enough.

Does our data stay with us?

Yes. The platform runs in your environment, and access is governed per role and per agent. Data sovereignty stays within your company.

How do agents access the data?

Directly on the platform, for example with the AI functions of Snowflake or Databricks, or via MCP servers with clear permissions and logging. Which route fits depends on the use case.

AI Impact Stack · Data platform

One truth for customer, revenue and margin

For your team and your agents.

Customers, products, prices and stock live in ERP, shop, CRM and spreadsheets today, each source with its own truth. A data platform brings them together, with one definition per metric that reporting, forecasts and agents all draw on.

Request an Impact Check Where do you stand?

Data harmonization

Agents decide with full knowledge

Three departments, one number.

Marketing optimizes for gross revenue, controlling calculates with net margin, sales measures pipeline. An agent that draws on these sources decides with half the knowledge.

A data platform on Snowflake or Databricks brings ERP, shop, CRM, marketing and service to a shared state. Every metric is defined once and used identically everywhere. Every project starts with a data audit: which sources exist, how reliable they are, which metric should stand at the end, and in which definition. This alignment with your business teams matters more than any technology choice.

Bring sources togetherERP, shop, CRM, marketing, service and external data on one platform
One definition per metricCustomer, revenue and margin modeled once, versioned and tested with dbt
Governance from the startData catalog, lineage and access control are built in
Ready for predictions and agentsFeature data for forecasts, CLV models and LLM applications

What the platform delivers

Data that impacts costs and revenue

Data work pays off in two directions: on costs and on revenue. We align every use case to this.

One set of figures for everyone.
Marketing, controlling and sales work with the same verified source. New analyses are built as dashboards on modeled data.
Result: decisions based on one number and shorter alignment rounds.
Predict customer value.
Customer lifetime value, churn indicators and next best action on one platform. For HSE, we combined deterministic calculation and AI prediction of customer value in a shared Snowflake architecture.
Result: marketing budget flows to where customer value is highest.
Predictable inventory and provisions.
More precise forecasts, better-matched provisions, predictable returns and cancellations.
Result: less tied-up capital and fewer write-offs.

Productive in six to eight weeks.

Depending on the starting point, that is how long a first productive data platform with two to three source systems takes. If Snowflake is already running, a single use case is typically productive in four to six weeks.

Technology partners

Platform-agnostic, certified

We recommend the platform that fits your landscape and your teams, and we model with dbt. Agents can run directly in Snowflake or Databricks.

All technology partners

FAQ

What matters before building a data platform

: Both work. What matters is your existing landscape, the skills of your teams and the use cases you are planning. The recommendation is settled after the data audit.
: With a single use case directly on your platform, typically four to six weeks until the model is productive. Architecture and operations stay as they are unless a specific need for change arises.
: When several source systems come together, several teams need the same figures, AI initiatives are meant to go into production, or data lineage and access control become a requirement. With a single source, the existing BI tool is often enough.
: Yes. The platform runs in your environment, and access is governed per role and per agent. Data sovereignty stays within your company.
: Directly on the platform, for example with the AI functions of Snowflake or Databricks, or via MCP servers with clear permissions and logging. Which route fits depends on the use case.