Custom AI assistants for companies
Looking for less. Work better prepared. I develop AI assistants who will find your shared information, prepare answers and fit into your processes.
Using your sources. For a specific task. Reviewed by your team.
What do I need for a project handover?
Draft answer · with source references
A well-prepared handover.The handover guide lists three key points:
We consider the task, the available information and the effort in everyday life. This results in a meaningful first use.
This helps with the start
This is how we evaluate the benefits
For the pilot test, we compare selected tasks with the previous work route. Together we determine how you see an improvement.
Select a use case. The examples show which information flows in and what can result from it.
Use case 01 / 03
For teams that need information from manuals, internal guides or project documents. The assistant helps to narrow down a question and bring together suitable positions.
“How does the handover of a new project proceed?”
Use case 02 / 03
For recurring customer questions where information must first be gathered. An assistant can classify the request, name missing information and prepare a response.
“What information are we missing for this offer?”
Use case 03 / 03
For the transfer between employees or project phases. From selected notes and documents, a structured overview can be created that makes open points visible.
“Summary the project status for the next handover.”
Possible individual implementation. Functions and connections are coordinated for your project.
From use case to use
A clearly limited pilot project makes it visible at an early stage whether the assistant helps with your task – before further functions are added.
A recurring task that determines the people involved and the desired outcome.
Result: a clear project scopeSample documents, updates, accesses and suitable interfaces together clarify.
Result: a coordinated data basisPlay through typical questions and difficult cases. Compare answers, sources and open points.
Result: a comprehensible evaluationBased on the results, decide on introduction, support and possible extensions.
Result: a concrete next stepServices in detail
An custom AI assistant should support a named task: finding knowledge, arranging information or preparing a draft. The starting point is shared sources and concrete example requests. Together, we determine which results are needed and which decisions remain with your team.
Documents require a traceable origin and handling of updates. We consider formats, responsibilities and access rights. Responses may include a reference to the sources used so that the team can more easily verify relevant statements.
A general demonstration is not sufficient for assessment in the establishment. We agree on typical questions, difficult cases and expected results. This also includes how the assistant handles missing information and when a query or human examination is required.
Technical implementation
The technical implementation combines knowledge sources, model selection and access rules. These parts are planned together based on your task.
Formats, text quality and updates determine how documents are accessed. We check how suitable sections for a request are found and how source references are maintained. Deleted or modified documents must also be taken into account in the search inventory.
The assistant may only use information that is shared with the respective user. Document content is considered data and must not trigger any additional permissions. Reading, drafting and actual modification of a task are planned separately.
Processing location, response time, usage limits and ongoing costs influence the selection. Typical questions and difficult cases form the test basis. Model or source changes are re-evaluated; failures and missing evidence require a clear feedback channel.
Useful are shared sample documents, typical questions, user groups and whether the assistant should only read or prepare actions.
Which of these questions is crucial for your project? Briefly describe your process and the existing systems.
Discuss technical requirementsThis is a possible use case. We check the format, update, access rights and the appropriate technical connection of your released sources.
We set specific example tasks and quality criteria. AI output can contain errors; relevant results are therefore associated with sources and appropriate human testing steps.
This depends on the chosen solution. Processing location, provider, data scope and accesses are clarified before implementation and included in the technical selection.
We agree on a delimited task, a manageable resource base and concrete test cases. In addition, there is the required interface area and the criteria for the evaluation. A complete introduction, further connections and ongoing support will be agreed separately.
This can be implemented via suitable interfaces. Reading, drafting and actual modification are considered separately. Which action is allowed after which release is expressly part of the definition of the scope.
We distinguish the development of ongoing expenses for model use, operation and support. Development depends, among other things, on sources, functions and interfaces. For ongoing operation, we consider frequency of use, data volume and updating. These assumptions form the basis for joint planning.
Describe to me a recurring task. Together we clarify whether an AI assistant makes sense for this and what a first manageable step can look like.
A brief overview is enough. We clarify the technical details in conversation.
