AI & Digitalisation Consulting
AI only creates value when it works inside the process
AI connects data, rules and systems to a specific work step.
The operational reality
A general AI tool does not know how your business works
A licence can make individual tasks easier. Business value only emerges when data, rules, handoffs and ownership meet in the same workflow.
No business context
A chat does not know customer status, product data, open cases or internal rules unless that information is connected in a controlled way.
No place in the process
A good answer saves little time if people still have to copy, verify, forward and re-enter it in another system.
No clear ownership
When an outcome becomes an order, pricing decision or customer case, it needs permissions, approvals and a traceable status.
Specialised applications
An AI application needs a clear job to do
We do not build generic interfaces with an AI label. We connect a specific work step to the information and rules it needs.
Support and service
Classify specialist questions, retrieve knowledge, safely consider customer data and hand complex cases to people with context.
Documents and cases
Read emails, PDFs and records, propose relevant data and prepare cases for review.
Sales and field service
Structure conversations, prepare tasks and draft orders, and make information from existing systems usable.
Internal knowledge work
Make approved documents searchable by meaning without turning search into uncontrolled data access.
AI does not sit beside your systems. It works with them.
The model is only one part of the application. What matters is how data, business rules, approvals and target systems work together.
Business data
ERP, CRM, documents, product data and knowledge sources provide the context for the case.
Secure integration
APIs, events and permissions define which information is available and what the application may do.
Retrieval and embeddings
Only approved content is prepared for semantic retrieval. Permissions still apply when an answer is generated.
Workflow
The application classifies, extracts or prepares a decision. It does not replace a business process with a prompt.
Approval and handoff
Where consequences arise, a person decides. Reliable results can continue as a draft or structured case.
Traceability
Status, access and handoffs remain visible. That makes the process operable, correctable and ready to evolve.
Choosing the right starting point
Not every process needs AI. Some are worth tackling now.
We do not begin with ten ideas at once. We look for a workflow where value, available data and the impact of errors are clear enough.
Repetition
There are frequent questions, similar documents or repeated data entry.
Rules and data
The team can explain decisions and escalation paths. Documents, system data and permissions can be connected cleanly and with clear limits.
Manageable failure modes
Results can be reviewed, corrected or routed for approval.
Measurable value
Handling time, questions, lead time or quality can be compared before and after rollout.
Evidence and further detail
From a concrete problem to an application that holds up
These examples cover different use cases: specialist support, document-driven ERP processes and the product backbone for data, permissions and workflows.
Secure AI support agent
RAG knowledge base, workflow orchestration, secure user verification and human handoff with context.
EpilaYer
Process documents, prepare results, obtain approvals and hand cases to specialised ERP systems in a controlled way.
Payload CMS as the control layer
Data models, admin, roles and workflow status as the foundation for applications where AI must not operate in isolation.
Customer portals and commerce
Place AI functions where customer, product and order data already meet.
Field service and domain knowledge
Connect conversation notes, domain knowledge and cases for teams that do not spend all day at a desk.
Digital products
PWAs, mobile applications and custom interfaces for workflows that do not fit into standard tools.
Control and rollout
When AI has consequences, it needs boundaries
We do not treat AI as a special exception. Data access, permissions, approvals and operations belong in the application from the start.
Limit data access
The application receives only the data sources and information it needs for its job.
- Approved sources
- Role-based access
Build in approvals
Where an outcome has consequences, a business decision or review remains in the process.
- Drafts instead of blind execution
- Escalation when uncertain
Keep workflows traceable
Status, handoffs and corrections remain visible so teams can manage the application day to day.
- Visible process status
- Correctable outcomes
Operate appropriately
Model, infrastructure and integrations follow the data, risk and existing system landscape.
- No one-size-fits-all hosting
- Extensible with the process
Which workflow costs unnecessary time today?
Tell us about the process, existing systems and the point where work regularly gets stuck. We will tell you plainly whether a specialised AI application makes sense and where to begin.