Capture tacit expertise before it leaves—our AI knowledge base combines RAG with governed search. See matching AI project references for practical outcomes.
Knowledge Transfer from Experts
Capture and preserve expert knowledge.
Intelligent Search
AI-powered search in documents and processes.
Chatbot Access
Natural language access to company knowledge.
Every organization faces the same invisible crisis. Decades of institutional knowledge sit in the heads of experienced employees. They will eventually retire or move on. Our AI project references show how costly that gap becomes in support and operations.
Wikis and manuals capture only part of that expertise. Written content also goes stale quickly.
Our AI-powered knowledge bases use Retrieval-Augmented Generation (RAG). They turn scattered documents, process guides, and expert insight into a living, searchable layer. New team members can query it in natural language from day one—often via chatbot access.
We connect large language models to your company data sources. Sensitive information does not need to go to external training pools.
We index content from SharePoint, Confluence, file servers, ticketing systems, and databases through API integration. Chunking keeps context and relationships intact in governed database infrastructure.
When someone asks a question, the system retrieves the most relevant passages. It generates a precise, source-cited answer. Users can verify it against the original documents.
Access control is critical. Many knowledge base projects overlook it.
Our systems respect permissions from your identity infrastructure—aligned with GDPR and optional EU or on-prem hosting. Users only see answers built from documents they may read.
That granular model lets one knowledge base serve multiple departments. You reduce the risk of HR content surfacing in engineering queries or finance data reaching the wrong audience.
The value of an AI knowledge base grows over time. It scales with your organization.
Scheduled sync indexes new documents from source systems. User interactions feed signals that improve retrieval—see knowledge transfer with AI for the content side.
We build evaluation pipelines that score answers against curated test sets. That helps catch quality drift before users feel it. Business cases compare via AI ROI and the knowledge base cost calculator.
For teams facing demographic change or rapid scaling, this turns knowledge preservation into a strategic advantage within our artificial intelligence services. The next maturity step is often AI agents inside broader AI solutions for businesses—with EU AI Act consulting where risk classification matters.
Project references
Concrete examples with measurable outcomes — swipe through matching references or open the full case study.
Definition: An AI knowledge base is a searchable system that connects company documents with a language model via RAG (Retrieval Augmented Generation) and answers plain-language questions with a source citation—while honouring existing access rights from your directory service. It does not replace document maintenance, but it makes existing knowledge findable and usable.
Which build-out of an AI knowledge base fits depends on your situation, data sources and compliance needs. The matrix maps typical situations to the recommended variant, data sources, result and an effort indication.
| Situation | Recommended variant | Data sources | Result | Effort (indication) |
|---|---|---|---|---|
| Knowledge lost when staff retire | Expert interviews plus RAG index | Manuals, interviews, process docs | Preserved, searchable expert knowledge | 4–6 weeks pilot |
| Documents scattered across systems | Enterprise search with connectors | SharePoint, Confluence, Jira | Central semantic search with sources | 4–8 weeks |
| GDPR / compliance critical | On-premise or EU hosting with role rights | HR and contract data, internal sources | Auditable, rights-aware search | project-dependent |
| Answers should act, not just inform | AI agents on your corpus | Business systems, APIs, knowledge base | Agent that triggers workflows | medium–high |
| Customer self-service wanted | Chatbot on the knowledge base | Approved FAQs and documents | Customer-facing AI assistant | medium |
| Cost frame unclear | Calculate costs | Document count, integrations | Transparent cost indication | intro call |
The AI knowledge base is the factual layer for RAG—not the same as a finished chatbot, but often its core.
Data quality: data analytics. Delivery: AI solutions for businesses. Overview: AI services overview.
Frequently Asked Questions
The usual enterprise standard is RAG (Retrieval Augmented Generation): your content is chunked and indexed with vector embeddings; at query time the best-matching passages are passed to the language model and the reply cites those sources. We connect SharePoint and Jira via connector or API; search honours roles from your directory (e.g. Microsoft Entra ID) so nobody sees more via the AI than in the source system.
You avoid training use by choosing API terms that prohibit providers from training on your inputs and by never uploading raw documents to consumer UIs like free ChatGPT. Technically, chunk stores and metadata stay under your control (EU hosting or on-prem); organisationally we align data flows, roles and audit logging for the EU AI Act and GDPR.
Use median handling time per ticket (minutes), first-level resolution rate without escalation to tier two, and time to first correct answer in the pilot team; measure a four- to eight-week baseline before go-live and the same metrics for eight weeks after. Add sampled quality checks against a curated question list (correct source, no fabrication).
Clarify scope and sources for your pilot
Concrete next steps—not a slide deck pitch.
RAG delivers reliable answers from approved documents. Chatbots add channels and dialogue. Agents run multi-step actions with approvals. We often start with RAG and extend selectively.
An AI knowledge base pays off when employees get lost in documents, wikis, and SharePoint—not when classic reporting is missing.
An AI knowledge base is a searchable company knowledge system with retrieval-augmented generation (RAG): the model answers only from approved documents, cites sources, and respects access rights—instead of generating freely from the internet.
| Use case | Benefit | Differentiation | Outcome | Next step |
|---|---|---|---|---|
| Expert knowledge leaves with retirement | RAG search with source citations and access rights | No chatbot without a knowledge foundation | Discoverable company knowledge | AI knowledge base costs |
| Support manually searches PDFs and Confluence | Enterprise search with audit log | Not a BI dashboard—knowledge access | Shorter search and onboarding time | AI implementation for business |
| Compliance requires traceable AI answers | GDPR-compliant hosting and role model | Not generic IT consulting | Audit-ready answers with evidence | Data & AI hub |
RAG and enterprise knowledge – not AI chatbot development. Data: data analytics.
Overview: AI & machine learning (overview).
Service overview: AI & machine learning (overview)
Use our funding calculator to see which government grants may apply to your project.
Björn Groenewold – Managing Director
Service cluster
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Related topics
Complementary services from other areas
These services are frequently requested together with AI Knowledge Base or complement it thematically.
Data analysis business intelligence: entry to BI & KPIs
The project check captures goals, constraints and follow-up questions in writing. Prefer talking first? Book a call anytime—the order is up to you.
Björn Groenewold – Managing Director
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From the field: KI-Wissensdatenbank für Maschinenbauer. All references.
Use our interactive calculator for a first budget indication—free and non-binding.
Thorsten Frieling – Projektmanagement
On the scheduling page, pick a free slot for a 30-minute intro call about Ai Knowledge Base – straightforward next steps.
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Book next available slotAI Knowledge Base is most effective when it is aligned with your business goals, existing systems, and team capabilities. At Groenewold IT Solutions we combine product thinking, clear architecture, and hands-on delivery so that every project delivers measurable value. We address operational, compliance, and performance aspects early so that later releases stay on track.
Our approach to AI Knowledge Base emphasises transparent backlogs, close collaboration with your stakeholders, and incremental delivery. Whether you need a discovery workshop, an MVP, or a full-scale implementation, we define scope, effort, and success criteria up front. With over 250 completed projects we have the experience to recommend the right level of investment and the right next steps for your situation.
Explore our services overview for the full portfolio, our topic pages for in-depth articles linked to each service, and the IT Glossary for key terms. For books and practical guides by Björn Groenewold, see publications. If you would like to discuss your project, we are happy to clarify scope, priorities, and a realistic timeline in a short consultation.
Our topic overview links related articles and entry points alongside this service page.
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