Service / AI integrations
AI integrations
An AI integration makes sense when it solves a specific problem: repeated questions, document search, summarization, data classification, or routine task automation.
AI integrations
An AI feature with a clear use case, output control, and safe boundaries.
Price
scoped individually
Duration
1-6 weeks
Included in delivery
- Chatbots & assistants
- Task automation
- AI search
- API integrations
- Solution prototype
Best for
- →Customer support
- →Internal knowledge
- →Content automation
- →Document analysis
- →Repeated questions
Expected outcomes
- →Validated use case
- →Working prototype
- →API integration
- →Answer logging
- →Quality review
What's included
An AI solution with clear boundaries.
The scope depends on the specific use case, but the foundation remains the same — understand the problem, build a prototype, test outputs, and only then integrate the solution into a real process.
Use-case selection
We first define the concrete task where AI saves time or improves quality and separate it from ideas without clear return.
Prototype and integration
I prepare a working prototype, connect the required API or data, and create a basic interface for use in the product or process.
Quality control
The delivery includes safe behavior boundaries, logging, test scenarios, and recommendations for ongoing output review.
Process overview
Problem first, AI second.
With AI integrations, it is important not to start with technology, but with a specific process. Only when it is clear what AI should improve does it make sense to solve the model, data, interface, and deployment.
01
Use-case selection
02
Data and boundaries
03
Solution prototype
04
Testing and integration
Before we start
What helps to prepare before we start
For an AI project, it helps to have a clear problem, input examples, and an idea of what a good output should look like. It does not need to be a finished specification, but it should describe a process that can be tested.
Concrete problem
A description of the task AI should solve, current time cost, input examples, and what a good output looks like.
Data and sources
Documents, FAQs, databases, exports, internal rules, or APIs the integration should use.
Risks and limits
What AI must not do, which answers need human review, and which data should not leave the system.
FAQ
Frequently asked questions
Answers to questions that come up most often for this service.
Next step
Does an AI integration make sense for your project?
Send a few sentences about the problem, the process, and the data you work with. I will get back to you with a recommended approach and estimated scope.
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