When should we use RAG instead of fine-tuning?
If the problem depends on changing internal knowledge, source citations, or permission-aware access, RAG is usually the better starting point.
RAG and knowledge systems - Grounded AI systems for enterprise knowledge and internal support.
Knowledge-heavy organizations often have the right information, but it is spread across PDFs, policies, SharePoint folders, ticketing systems, and team-specific repositories.
We help clients turn that fragmented landscape into grounded AI workflows with strong retrieval quality, permission-aware access, and clear user expectations.
What we deliver - Practical outputs, not generic AI strategy
- Document ingestion, chunking, metadata strategy, and content refresh workflows.
- Retrieval design across vector, keyword, and metadata-aware search patterns.
- Knowledge assistant interfaces with citations, feedback collection, and role-aware access control.
- Evaluation of answer quality, retrieval coverage, and failure modes before broader rollout.
Best fit - Who this service is for
- Engagement fit. Internal support teams that need faster answers across policy documents, SOPs, and technical documentation.
- Engagement fit. Organizations with multilingual knowledge bases, fragmented repositories, or high document turnover.
- Engagement fit. Programs where source citation, data access control, and answer reliability are essential.
Typical architecture - Designed for reliability, grounding, and operational handover
The exact stack depends on the problem, but these are the design principles we usually optimize for.
- Architecture principle. Pipelines that normalize and enrich source content before it reaches the model layer.
- Architecture principle. Retrieval strategies chosen for the data, not copied from generic tutorials.
- Architecture principle. Permission-aware system design so assistants reflect the same access boundaries as the underlying content sources.
- Why Super AI Labs. A focus on grounded answers and production behavior rather than superficial demo quality.
- Why Super AI Labs. Strong fit for Swiss organizations that need clear source provenance and internal stakeholder trust.
- Why Super AI Labs. Experience connecting retrieval systems to broader operational workflows, not just standalone chat UIs.
FAQs - Questions we hear early in the conversation
These are the kinds of questions that usually matter before a team commits to scope, architecture, and rollout.
Can a knowledge assistant work across several internal repositories?
Yes. A big part of the work is designing ingestion, metadata, permissions, and retrieval so different content sources behave coherently in one experience.
How do you measure whether the system is actually useful?
We look at retrieval quality, groundedness, task success, escalation patterns, and whether users can trust and operationalize the answers they receive.
Related proof - Case studies, articles, and next steps
If this is close to what your team needs, these pages are the best next places to look.
Operating document intelligence systems with governance
A practical look at operational controls for document-heavy AI systems.
Our delivery process
How we move from discovery and architecture to production-ready implementation.
Talk through your knowledge-system architecture
Useful if your team is deciding between search, RAG, and agentic workflow patterns.