Can we build an internal AI assistant with our own knowledge?
An internal assistant sounds logical: ask a question and get an immediate answer from your own knowledge. But whether that works well depends heavily on your sources, permissions, required reliability and the kind of questions employees ask.
Does this sound familiar?
This usually comes up when knowledge does exist, but is hard to find or widely spread across the organisation.
Employees keep asking the same questions
Answers sit in documents, manuals, SharePoint, databases or with experienced colleagues.
Search returns too much noise
People know the information exists somewhere, but can't quickly find the right passage or version.
A general chatbot is not enough
Good answers require knowledge that is not public or generally available.
Permissions make it complicated
Not every employee may see the same documents or data.
Where do you want to be?
You first want to know whether an internal assistant can really become usable before having a complete system built.
Know what is technically feasible
You get insight into which sources are usable and how AI could get access to them.
Understand what reliable enough means
We determine where source references, validation or human review are needed.
A realistic solution direction
You know how data, retrieval, permissions and user experience can come together technically.
Why does this happen?
An internal AI assistant is not simply ChatGPT with a folder of documents. Its quality is largely determined by sources, retrieval, access rights and the question of what a reliable answer is.
See how we approach thisInternal knowledge is rarely neatly structured
Documents differ in quality, freshness and format.
More context is not automatically better
Too much irrelevant information can actually make answers worse.
Access remains a security matter
The assistant must not become a new route to information a user normally may not see.
AI Reality Check
With an AI Reality Check we examine the desired questions, sources, access rules and required reliability. If the biggest uncertainty then needs practical testing, an AI Prototype Sprint can prove the most important end-to-end flow.
View this offer€1,950 excl. VAT
Fixed price · one process or up to three AI ideas
- Result
- A reasoned technical direction and build / buy / don't build advice
- Duration
- About two weeks
- Next step
- Build, validate with a prototype first, or deliberately stop
Why 9to5 for this?
A good knowledge assistant calls for both AI expertise and regular software engineering. Sources, data, permissions and integrations are at least as important as the model.
View casesWe start with the information
First understand what knowledge is available and what users want to do with it.
Permissions belong in the design
Security is not treated as a later add-on.
We can go from research to implementation
If the idea proves feasible, the technical context doesn't have to be rebuilt from scratch at another party.
What else fits your situation?
AI Prototype Sprint · €6,950 excl. VAT
From one concrete AI idea to a working end-to-end prototype that lets you actually test the key technical assumptions.
View offerCan AI perform this task reliably enough?
You can see that AI can in principle perform the task, but want to know whether the quality is predictable enough for serious use.
View use caseHow do we add AI to our existing product?
You see opportunities to make your current software smarter, but first want to determine which AI functionality really adds something, technically and commercially.
View use case