Does our data need to be perfect first?

No. We clarify which data the prioritised process genuinely needs, which quality gaps matter, and how they can be handled visibly.

Do you always start with AI?

No. If a conventional integration, better data pipeline, or rules-based automation solves the problem more reliably, that is the better architecture.

Will you work with our existing platform?

Yes. The target design begins with your current system landscape. New services or platforms are added only when they provide a clear business and operational benefit.

How do you address governance?

Data access, accountability, logging, tests, and approvals are integrated into the architecture and process instead of being documented afterwards.

Data. Platform. Applied AI.

Turn your data landscape into a dependable business process.

Datenschaftler connects data engineering, data platforms, and applied AI. The result works in your existing system landscape, remains traceable, and stands up in production.

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From system to process

01
Source systems
ERP, CRM, business systems
02
Data foundation
Contracts, quality, lineage
03
AI & automation
Approvals, tests, evaluation
04
Business process
Use, monitoring, accountability

Service system

No AI initiative without a sound data foundation.

We address the layers that determine a reliable outcome, from operational data flows to accountable system actions.

Data engineering

Sources, interfaces, and transformations become documented, monitored data flows.

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Data platforms

Lakehouse, warehouse, and cloud platforms gain clear product boundaries, governance, and a dependable operating model.

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Applied AI

Search, assistants, and agents are integrated into processes with data access, approvals, and evaluation.

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Before the technology

Four questions that make an initiative hold.

A useful system starts with business context. We make critical dependencies visible early.

01Which business process should improve in concrete terms?
02Which data is available, current, and approved?
03Which decision or action remains with a person?
04Who owns quality, operation, and further development?

Engagement

From a shared view of reality into production.

We work in reviewable steps. Scope and architecture follow the process, not a prefabricated technology.

01
Clarify context
Map the business process, system landscape, data situation, and responsibilities together.
02
Shape the target
Define the smallest dependable scope, interfaces, and quality criteria.
03
Build in context
Implement data flows, platform components, and the application in the real environment.
04
Secure operations
Embed monitoring, approvals, failure paths, and handover to accountable teams.

Industry reality

Technology must fit the process and regulatory environment.

We connect industry-specific workflows to the existing data and system landscape.

01Manufacturing 02Banking 03Public sector 04Healthcare 05Real estate 06Legal

10+

Years of experience across data engineering, data platforms, and applied AI.

Experience & evidence

Technical depth without showcase metrics.

10+ years of experience across data and AI platforms are supported by named team profiles and technical qualifications. What matters is how that experience is applied in your system context.

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Certifications & accreditations