AI knowledge base: your company knowledge, finally queryable
Your company's knowledge is scattered across folders, wikis, email, and the heads of a few colleagues. An AI knowledge base makes it queryable: employees ask questions in plain language and get substantiated answers from your own documents - with citations and permission checks. Olio builds these systems production-ready and GDPR-compliant. Boutique agency from Mönchengladbach, Germany, working remotely across the EU.
What an AI knowledge base does day to day
The new hire finds the onboarding answer themselves instead of interrupting a colleague. Sales pulls technical details from hundreds of past quotes and project reports. Support answers questions from manuals and ticket history in seconds. Management asks what contracts say about a topic. In short: the hours your team loses daily to searching and asking become seconds - and the knowledge of departing employees stays usable.
How it works: RAG instead of training
The technology behind it is retrieval-augmented generation (RAG): your documents are indexed, the most relevant passages are retrieved for each question, and the language model formulates an answer with citations. The model is not trained on your data - your content stays in your infrastructure and is current within seconds of a document changing. Answers without support in your sources are flagged as such instead of invented.
Permissions and data protection first
The hardest question of a company knowledge base isn't the AI - it's: who is allowed to see what? Salary lists must not surface in an answer to an intern. We carry your existing permissions from SharePoint, Confluence, and friends into retrieval - every answer only uses sources the asking person could open directly. Operations on EU servers, models via EU endpoints or self-hosted, data processing agreements and deletion concepts included.
Your sources, connected
We connect what you actually use: SharePoint and Microsoft 365, Confluence, Google Drive, network drives full of PDFs and Office files, email inboxes, ticket systems, wikis, and via APIs also databases and business applications. The craft is in the processing - handling tables, scanned documents, and version states cleanly is what decides answer quality. That's exactly where we invest the engineering time.
Why Olio
RAG demos are built in an afternoon - systems a company trusts are not. We build with evaluation sets of real questions, measure answer quality before and after every update, and treat permissions as an architectural principle instead of an afterthought. You work directly with senior engineers, get a documented handover, and operations that run GDPR-compliant in the EU. And if your case is better served by an off-the-shelf product, we'll tell you so in the audit.
What you get with us
- Substantiated answers with citations - no invented facts from a black box
- Permissions carried over from your systems: everyone sees only what they're allowed to
- Connected to SharePoint, Confluence, Drive, email, ticket systems, and network drives
- GDPR-compliant operations: EU servers, EU endpoints, or self-hosted models
- A pilot on your real documents in production within 3 to 5 weeks - from €10,000
- Measurable answer quality through evaluation sets instead of gut feeling
Core Technologies
Let's make your knowledge queryable
Frequently asked questions
What does an AI knowledge base cost?
A pilot with one or two data sources and an evaluation set runs €10,000 to €25,000. Scaling up with multiple sources, permission mapping, and integration into Teams or Slack lands between €25,000 and €60,000. Add running model and hosting costs, which we calculate transparently up front.
How fast is the system ready to use?
The pilot on your real documents is in production within 3 to 5 weeks - deliberately scoped to one knowledge area so quality is measurable. After that we expand source by source, prioritized by value.
Won't the AI just hallucinate answers?
The system is built against that: answers are generated from retrieved passages of your documents and backed with source links. If retrieval finds nothing substantive, the system says so - instead of guessing. We measure answer quality continuously with an evaluation set of real questions from your team.
How do permissions work?
Your existing permissions from SharePoint, Confluence, and friends are carried into retrieval: every answer uses only sources the asking person could open directly. Sensitive areas like HR or management can additionally be excluded entirely.
Will our data be used to train AI models?
No. RAG doesn't train a model on your data - your documents stay in your infrastructure and are only used to answer the question at hand. With EU endpoints of the model providers, use for training is contractually excluded; on request the model runs fully self-hosted.
Which data sources can you connect?
SharePoint, Microsoft 365, Confluence, Google Drive, network drives with PDFs and Office files, email inboxes, ticket systems like Zendesk or Jira, wikis - and via APIs also databases and business applications. Scanned documents are processed with OCR.
How does the knowledge base stay current?
Sources are synchronized automatically: when a document changes, the new state is queryable within minutes. There is no second knowledge base to maintain manually - the system reads where your team already works.
How is this different from ChatGPT Enterprise or Microsoft Copilot?
Ready-made assistants are good for general productivity but hit limits with source variety, fine-grained permissions, answer evaluation, and data sovereignty. A dedicated knowledge base pays off when the knowledge is business-critical, spans several systems, or the data must not leave your infrastructure. In the audit we compare both paths honestly - sometimes the off-the-shelf product is the right answer.
Can we use the knowledge base in Teams or Slack?
Yes - we integrate the query experience where your team works: as a bot in Microsoft Teams or Slack, as a web interface, or embedded in your intranet or business application.
Our documentation is a mess - is this still worth it?
Usually yes, with realistic expectations: the system finds a surprising amount even in unstructured archives, and the citations quickly reveal where documentation is missing or outdated. The pilot makes the true state visible - many clients use exactly that to clean up in a targeted way.
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