Typical starting points
When this service is a good fit
- Manual data entry and transfer
- Documents need to be read, assessed or classified
- Approval processes create avoidable loops
- Existing tools are insufficiently connected
AI process automation
AI automation for processes that are still unnecessarily manual.
We combine traditional workflow automation with AI where decisions, text, documents or other unstructured inputs need flexible interpretation.
Strategic guidance before implementation
Custom solution instead of a standard tool
Secure integration into your system landscape
Operation and continuous development considered from the start
Scope
What we deliver
- Process discovery and automation assessment
- Workflow and integration design
- AI-assisted processing steps
- Logging, failure paths, approvals and human-in-the-loop controls
Use cases
Where AI Process Automation can create concrete value.
The right architecture depends on the process, data, existing systems and the cost of a wrong decision. These examples are common use cases, not blanket recommendations.
01
Email and document classification
02
Data extraction from unstructured inputs
03
Preparation of case handling
04
Orchestration across several business systems
05
Quality and plausibility checks
06
Approval processes with AI-assisted preparation
Decision criteria
When the approach fits – and when it does not.
We first assess whether AI genuinely adds value compared with conventional software, search or workflow automation for the specific task.
Questions we clarify early
- Which steps are rule-based and which are variable?
- Where must a person make the final decision?
- Which systems need to be connected?
- What error rate is acceptable for the business process?
Quality and control
AI capabilities need measurable quality criteria.
Non-deterministic outputs should not be judged from a handful of impressive demos. We define representative test cases, observe failure patterns and design fallbacks or human approvals where the business context requires them.
- Keep deterministic steps deterministic
- Use AI only where flexible interpretation is needed
- Design failure paths and manual takeover
- Measure process outcomes before and after automation
How we work
From business problem to robust solution.
We do not start with a model name. We start with the process, users, available data and technical constraints. From there, we design an architecture that balances business value, privacy, cost, maintainability and operations.
Analysis and target architecture
At the start, we clarify the current situation, users, data sources, integrations and quality requirements. If core assumptions are still open, we shape an initial phase specifically to test those assumptions.
Development and integration
The software is developed iteratively against real data and interfaces. Critical integrations and AI capabilities are tested early so technical risks become visible before the end of the project.
Operation and continuous improvement
For production systems, monitoring, cost control, logging, failure paths and model or provider changes are part of the architecture. An AI project does not end with an impressive demo.
Frequently asked questions
Key questions about AI Process Automation.
Direct answers to points that should be clarified before making a technical decision.
Which processes are suitable for AI automation?
Processes with recurring tasks and unstructured inputs such as text, documents or emails are often good candidates. Purely rule-based steps should frequently remain deterministic.
Does the whole process need to run on AI?
No. A hybrid architecture is often more robust: deterministic workflow logic controls the process while AI handles only the steps that need flexible interpretation.
How can the risk be limited?
By defining responsibilities, validation, thresholds, approval steps, logging and a route for uncertain cases to be handed back to people.
Can document processing and email classification be automated with AI?
Yes. Typical steps include classification, extraction, summarisation and routing. Robust implementations combine AI with fixed rules, validation and manual handling for uncertain cases.
What is the difference between AI automation and traditional workflow automation?
Traditional workflows follow explicit rules. AI is particularly useful when unstructured text, documents or variable meanings need to be interpreted. In practice, combining both is often the most reliable approach.
Related services
Relevant building blocks for your project.
Let’s turn your AI idea into a concrete software project.
In an initial conversation, we clarify the objective, current situation and sensible next steps.
To get started, your objective, existing systems and the main open question are enough.