Find company knowledge more easily with AIRelevant passages. Answers with sources.
We help you assess whether your existing search is sufficient or a knowledge assistant would help. RAG (Retrieval-Augmented Generation) means that AI finds relevant passages in approved documents and uses them to produce an answer. We develop a clearly scoped application where the data, interfaces and permissions support it.
- Agreed requirements and an initial scope: the questions, documents and users the application will cover.
- A usable solution for the agreed workflow, checked against specialist questions, error cases and access rights.
- Documentation and handover; hosting, model usage, licences and maintenance are addressed separately in the proposal.
Suitable tasks and occasions
Manuals and instructions
Retrieve relevant passages for a technical question. Check safety-critical information against the original.
Past quotations and projects
Find useful examples more easily. Check currency, customer rights and terms before reusing them.
Internal policies
Make approved guidance accessible to colleagues. Refer missing or conflicting information to the responsible person.
How we agree the approach with you
Understand the search task and documents
You show us typical questions and your current repositories. Together we check whether improving existing search is sufficient. For a custom application, we define the sources and users and assess readability, current versions, interfaces and costs.
Develop the first application
We implement the agreed scope. Your subject experts check answers and source passages against familiar questions. This includes questions with no suitable source, conflicting documents and access under different user permissions.
Introduce the application and hand over responsibility
You receive the agreed solution with documentation and an introduction for your team. Together we establish who updates sources, manages access and handles errors. Ongoing support and maintenance are agreed separately if needed.
Answers to help you plan
When is a knowledge assistant worthwhile compared with existing search?
Which documents and permissions need preparation?
Does RAG prevent incorrect answers?
How do documents stay current, and who operates the application?
Further reading and examples
Search company documents with an AI knowledge assistant
Answers to recurring technical questions are spread across manuals, policies and project documents. An assistant should make relevant passages easier to find and support checking against the original.
RAG for company knowledge: answers with sources and clear access rules
Make approved documents easier to search while keeping source quality, permissions and review in view.
Software development
View scope, approach and contact options.
What an answer could look like
Fictional example using invented internal documents.
- A question from the team
- “Who approves an express delivery?”
- Answer with a source passage
- “The logistics team lead approves express deliveries. Source: Shipping Manual, version 3, section 4.2.”
- Unanswered follow-up
- “Who acts as their deputy?” — “The approved documents do not name a deputy. Please ask the logistics team.”
Sources and technical background
These sources explain the underlying principles. Our recommendations provide practical guidance and do not promise a particular project result. Editorial review: 18 October 2026.
Which questions should your company knowledge answer?
Bring a few typical questions, the repositories you use and an overview of access rights. Using examples approved for sharing, we can assess whether existing search is sufficient and what a first custom application would cover.
Free initial conversation