RAG consulting Switzerland
RAG Consultant in Switzerland for Internal Knowledge Systems
RAG is the right pattern when your company needs AI answers grounded in its own documents, policies, reports, and tools. Fanktank designs and builds retrieval systems that prioritize source quality, traceability, permissions, and measurable answer reliability.
Experience with hybrid retrieval, BM25, vector search, reranking, chunking, metadata design, and citation workflows.
Production-grade thinking around access control, document updates, evaluation sets, and failure modes.
Practical focus on business users who need trusted answers, not demos that only work on clean sample PDFs.
RAG is not just uploading PDFs
A reliable RAG system needs document preparation, chunking strategy, metadata, retrieval evaluation, prompt design, citation handling, and a workflow for updates. Without those pieces, the system may look impressive in a demo but fail on real internal questions.
The architecture depends on risk
A sales knowledge assistant, a healthcare guideline assistant, and an ERP-integrated workflow assistant should not be built the same way. The right design depends on data sensitivity, required citations, permissions, latency, cost, and how wrong answers will be detected.
How Fanktank builds RAG systems
A typical project starts with a content and security audit, then moves into a pilot corpus, retrieval evaluation, answer quality checks, and a production architecture that can be maintained as documents change.
Good RAG use cases
- Internal policy, process, and documentation assistants.
- Customer support copilots with source-backed answers.
- Clinical, compliance, or technical guideline search with citations.
- Search and Q&A across reports, PDFs, wikis, and structured records.
Common Questions
What does RAG mean?
RAG stands for retrieval-augmented generation. It lets an AI system retrieve relevant company information before answering, so responses can be grounded in current sources.
Can a RAG system cite sources?
Yes. Source citation should be designed into the retrieval and answer pipeline from the beginning, especially for business-critical systems.
Can you integrate RAG with existing tools?
Yes. Depending on access and security requirements, a RAG system can connect to document stores, databases, wikis, support tools, or ERP workflows.