RAG & Enterprise Knowledge Assistants
Give your teams accurate, cited answers from your own documents and systems, with the permissions they already have.
Answers You Can Trust.
Retrieval-augmented generation (RAG) grounds AI answers in your organisation’s own content, so responses are accurate, current and traceable to a source. We build knowledge assistants that search across documents, wikis and databases, respect existing access rights, and show where every answer came from.
What This Includes
Ingestion pipelines for PDFs, SharePoint, Confluence, wikis and databases
Chunking, embeddings and vector search tuned to your content
Vector stores including Amazon OpenSearch, pgvector and Azure AI Search
Citation-backed answers that link to the source document
Permissions-aware retrieval that respects existing access controls
Answer-quality evaluation and user feedback loops
Chat, Microsoft Teams, Slack or API front ends
How an Engagement Works
We start with a defined set of documents and the questions your teams ask most. We measure answer quality against that set, then add sources and users once accuracy meets the agreed bar.
Why Tech Teams Trust Us
Data engineering depth: pipelines, data quality and governance
Security and access control treated as requirements, not add-ons
Built on Amazon Bedrock or Microsoft Foundry to suit your cloud
Let’s Put Your Knowledge to Work
Book a free discovery session to identify the knowledge sources and questions where an assistant would help most.