Stap 01
Choose the process
We choose one bounded process area: e-commerce operations, support, finance, reporting or another workflow with enough volume.
AI Quickscan
Discover where AI can add value.
In two to four hours we make the AI question concrete: which process deserves attention, which data is usable, where the risks are and what makes sense as a first build step.
- Duration
- 2-4 hours
- Output
- Concrete improvement plan
- Price
- Fixed fee up front
What you get
A list you can act on.
Opportunities
A clear view of where AI can add value in the chosen process.
Structure
An analysis of workflow, teams, systems, data, dependencies and risks.
Order
A proposal for what should come first, what can wait and what should not be built.
Next step
A first direction for solution, scope, operation and the people who need to be at the table.
The Quickscan does not commit you to a follow-up. Sometimes the useful conclusion is that a data definition or integration needs improvement first. Sometimes the honest answer is that an AI solution does not yet create enough value.
Approach
No brainstorm. A decision route.
Stap 02
Read data and systems
We map CRM, ERP, ticketing, exports, databases and ownership. The data does not need to be perfect, but it must become clear.
Stap 03
Define risk
We define which decisions stay with people, what needs logging and where AI is too early or too sensitive.
Stap 04
First build step
You get a proposal for the first workflow that deserves build time, including dependencies, operation and what should wait.
When to request it?
Better prepared means faster decisions.
Good fit
One process under real pressure
For example support, operations, finance, reporting or a workflow where time disappears every week.
Preparation
Bring context
Think systems, data sources, volumes, exceptions, team pressure and what is currently manual or error-prone.
Decision
Scope and price first
We only start after it is clear which process we inspect and which fixed fee applies.
Example output
This is how concrete it should get.
Process
Support inbox: return questions, order status and delivery exceptions.
Data sources
CRM, ERP, ticketing, carrier portal and historical support tickets.
First solution
Classifies messages, retrieves order context and suggests a reply or next step.
Do not build
Fully automatic refund decisions while return rules differ by country.
Decision support
What becomes clear up front.
Price
Fixed price before we start
The Quickscan starts only after scope and price are confirmed. You know up front which process we inspect and what the analysis will deliver.
Data
No blind model choice
We discuss which data is needed, where it may run, who gets access and when human approval must remain mandatory.
Output
Useful for decisions
The outcome is concrete enough to discuss internally: build, improve data first, start smaller or deliberately avoid AI for now.