RAG Systems & Enterprise Knowledge Assistants
We build retrieval-augmented generation (RAG) assistants that search your company documents, policies and databases and answer questions with references to the original sources, so your team finds information in seconds instead of hours.
Problems we solve
- Knowledge is scattered across drives, wikis and email
- New employees spend weeks finding the right documents
- Generic AI tools do not know your internal information
- Answers without sources cannot be trusted
Typical use cases
- Internal knowledge assistant for policies, procedures and manuals
- AI search across contracts, offers and technical documentation
- Customer-facing assistants grounded in approved content
- Sales and support enablement with instant product answers
- Document comparison and summarisation
How implementation works
- 1Identify document sources and access rules
- 2Prepare, clean and index content in a vector database
- 3Build retrieval and answer generation with source citations
- 4Evaluate answer accuracy on real questions from your team
- 5Deploy with access control, monitoring and content updates
Systems and integrations
- SharePoint, Google Drive, Confluence and Notion
- PDF, Word and spreadsheet documents
- Vector databases such as pgvector, Pinecone or Qdrant
- Large language models from OpenAI, Anthropic or Google
- Your intranet, Slack or Microsoft Teams
Security, privacy and human oversight
Users only see answers from documents they are allowed to access. Every answer links to its sources, and we measure retrieval and answer quality before and after launch. Where needed, data stays in EU-hosted infrastructure.
Frequently asked questions
What is retrieval-augmented generation (RAG)?
RAG combines search with a language model: the system first finds the most relevant passages in your documents and then writes an answer based only on them, citing the sources.
When should we use RAG instead of fine-tuning?
RAG suits knowledge that changes often and must be traceable to sources. Fine-tuning suits changing a model's style or format. Most business knowledge assistants work best with RAG.
Is our data used to train public AI models?
No. We use model providers and settings that do not train on your data, and we can keep documents and indexes in infrastructure you control.
Related services
Discuss your use case
Book a free 30-minute AI consultation. We will look at your process and tell you honestly whether automation is worth it.
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