Custom AI wiki development for teams that need answers, not just storage.
I build AI knowledge systems that turn internal documents, standards, training material, policies, and project history into a searchable source of truth.
Ideal for enterprises, technical teams, onboarding-heavy businesses, and multilingual operations.
What this includes
This work often includes custom RAG pipelines, permissions-aware retrieval, document processing, and German/English knowledge support.
Document ingestion
Bring together PDFs, docs, spreadsheets, manuals, standards, and historical files into one AI-ready pipeline.
Search plus citations
Combine semantic retrieval and grounding so answers are fast, defensible, and easy to verify.
Internal assistant UX
Design the chat, search, filters, and role-based interactions so teams can actually use the system daily.
Knowledge governance
Support access control, source freshness, and content quality as the knowledge base grows.
Approach
From raw material to running AI.
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01
Map your knowledge sources
Identify the documents, owners, formats, languages, and retrieval constraints that shape the solution.
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02
Design the retrieval layer
Structure metadata, chunking, ranking, and citation logic around real user questions.
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03
Build for your workflows
Fit the AI wiki into support, onboarding, engineering, field operations, or sales enablement processes.
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04
Improve with usage data
Refine content quality and retrieval performance based on real questions and failure patterns.
Outcomes
What gets better after launch.
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Faster internal answers
Less time spent searching through fragmented files, email chains, and tribal knowledge.
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Better onboarding
New team members can find context faster without depending on a few senior employees.
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Stronger operational consistency
Teams work from a shared answer layer instead of conflicting documents and memory gaps.
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Enterprise-ready foundation
A custom AI wiki that can expand into copilots, proposal generation, and other applied AI workflows.