Use cases

Typical projects companies bring to us

Each example describes a starting point from a mid-sized company, our approach and the outcome you receive. The examples show typical tasks, not individual client projects.

Making data usable

Reliable data with clear definitions and regulated maintenance, for steering, planning and AI.

Service: Business Intelligence

Reporting that every department relies on

Starting point: You decide on sales targets, capacities or investments. Each department calculates with its own terms and bases, and meetings end up discussing the numbers instead of the decision.
Approach: We derive the metrics from the decisions they are meant to support. Together with those responsible we define metrics, assign data sources and agree on the calculations.
Outcome: Reports and dashboards with documented definitions that show the relevant developments and allow drilling down into the details.

The question for your company

Which decision should your reporting support better in future, and which information has to come together for it?

Service: Data Warehouse

Analysing ERP, sales and accounting data together

Starting point: The data describes related business transactions but sits in different systems and structures. A common foundation for cross-functional analyses and applications is missing.
Approach: We clarify which data is needed from which systems, how it is connected and how current it must be for your purpose. Then we set up ingestion, processing and quality checks.
Outcome: A maintained, shared data foundation with a documented data model on which analyses and AI applications can build.

The question for your company

Which information must be available together for your next project, and in which systems is it created today?

Service: Data Organisation

Clarifying master data before a system change

Starting point: You are introducing a new ERP, migrating product master data or want to use customer information across departments. What matters is which terms apply, which fields are mandatory and who is responsible for quality.
Approach: With your departments we clarify the meaning of the information your project depends on and define how it is captured, checked and updated. We describe responsibilities so that they can be fulfilled in day-to-day work.
Outcome: Binding definitions, quality criteria and maintenance processes. Data maintenance becomes a manageable task in your company.

The question for your company

For which data would a binding rule for meaning, responsibility and maintenance directly support your current project?

Service: Forecasting Systems

Planning stock with verifiable forecasts

Starting point: You plan sales, inventory or sales capacity from experience and available figures. Seasonal patterns, customer groups and business changes determine how reliable these assumptions are.
Approach: We develop the forecast for a specific decision, such as stock planning for one product group, and include the experience of your departments. We agree on planning horizon and evaluation criteria and test the results against an agreed baseline.
Outcome: Forecasts with stated accuracy, deviations and limits. Those responsible can see what the forecast contributes to their planning.

The question for your company

Which planning decision ties up particularly much capital or capacity, and which assumptions would you like to test systematically?

Embedding AI in everyday work

Making knowledge accessible, automating routines, introducing the right tools.

Service: Process Automation

Checking incoming invoices, with approval staying with people

Starting point: Incoming invoices are captured, matched against purchase order and delivery note and forwarded for approval. This takes time every month, errors let discount periods lapse, and mandatory e-invoicing is a reason to review the process. Similar routines exist in order entry and purchasing.
Approach: First we measure volumes and processing times with your team and agree on a metric, such as lead time per invoice. Then we automate the process in the systems you use today, for example DATEV or your ERP. Routine cases run automatically, cases that need clarification go to the responsible person with a reason.
Outcome: An automated process with documented rules and monitoring. After go-live we measure the same metric again: you see the effect in your own numbers.

The question for your company

Which recurring task noticeably ties up your team's time every month?

Service: AI Knowledge Management

Securing experience before it leaves the company

Starting point: Your experienced staff know what customers need, which solutions have proven themselves and why important decisions were made. Much of this is not written down anywhere. With retirement or a generational change it is at risk of being lost.
Approach: We identify business-critical knowledge areas with you and capture your employees' experience in structured interviews. Your experts classify the content, and we connect it with your documents and business systems.
Outcome: AI-supported access to your company's knowledge. Every answer names its sources, and permissions control access.

The question for your company

Which knowledge must not be lost in a change of staff or generation?

Service: Internal AI Assistant

Research and proposal preparation with an internal AI assistant

Starting point: Your employees see concrete uses for AI: researching information, summarising documents, preparing content for a proposal. To turn this into a usable application, your sources, workflows and access rights have to be taken into account.
Approach: Together we define tasks, sources, permissions and criteria for good results. We check the quality of the answers with your team on real tasks, and the Academy trains staff in using the assistant.
Outcome: An AI assistant for agreed tasks, connected to approved company sources, with documented access rules.

The question for your company

Which task should an internal AI assistant take on first, and how would your team recognise a helpful answer?

Service: AI Implementation

Selecting and introducing the right AI tools

Starting point: The first licences are in place and your departments have concrete applications in mind. Open questions are which AI systems fit your tasks, which can be connected to your data and what costs and dependencies arise in operation.
Approach: Starting from your business goals and use cases, we select AI systems by functional performance, integration, data protection, security and total cost. Existing licences are taken into account.
Outcome: A coordinated AI system landscape with usage rules instead of a loose collection of tools. Vendor choice, hosting location and switching options are made transparent.

The question for your company

Which tasks should AI take on in your company, and how well do your current tools fit the data and workflows required?

Business applications and connected systems

Custom applications on top of your existing systems and modules that connect information from first contact to invoice.

Service: AI App Development

A business application for service, field staff or customers

Starting point: You want to use a service application, a customer portal or a tool for field staff. Development ties up capacity needed in day-to-day business, and the new application has to fit your existing systems.
Approach: Together we define the first usable version: which tasks it fulfils, which systems it connects and how it will be accepted. Our AI infrastructure speeds up development, experienced developers review the code and automated tests secure every change.
Outcome: An application with agreed interfaces, reviewed code, automated tests and documented architecture.

The question for your company

Which application should move your company forward next, and which functions does it need for its first productive use?

Service: Customers, Quotes and Orders

Quotes and orders according to your own rules

Starting point: Your sales team should handle enquiries quickly and create quotes that match your services and terms. For that it needs the customer history, the current processing status and clear handovers to order processing.
Approach: We clarify sales stages, quoting rules, responsibilities and handovers. Proven functions for customer records, tasks and cases form the basis, and we develop custom additions for your quote and order logic.
Outcome: A module for customer records, follow-ups, quotes and orders. Your team sees the process from first contact to order.

The question for your company

Which particular aspect of your quote or order handling should your application support directly in future?

Service: Operational Steering

Metrics where the work happens

Starting point: Your managers want to assess open quotes, running orders or processing times in day-to-day business. For that they need metrics that are linked to the underlying cases and available in their application.
Approach: We translate your steering questions into defined metrics, clarify data sources and calculation rules and link the overview to the individual cases. We agree on what the metrics mean with your managers.
Outcome: Overviews directly in your teams' applications, with documented metrics and the path from overview to individual case.

The question for your company

Which metric should your team be able to use directly in its application to decide on the next processing step?

How each of these projects starts

In the free initial consultation we clarify occasion, starting point and goal. You receive a short playbook with our assessment and the recommended next step. If your project is already clearly defined, we move straight to implementation.

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Completed client projects can be found under success stories.