Ground your AI in verified company data for accurate responses.
Generic AI models hallucinate. They invent facts, cite non-existent sources, and confidently deliver wrong answers. Retrieval-Augmented Generation (RAG) solves this by grounding every AI response in your verified company data — documents, policies, product catalogues, support tickets, and knowledge bases.
Our RAG systems ingest your existing documentation, index it for lightning-fast retrieval, and ensure every answer comes with source citations your team and customers can verify. The result is an AI that knows your business as well as your best employee.
Enterprise-grade knowledge retrieval that your team can trust.
Ingest PDFs, Word documents, spreadsheets, web pages, Confluence wikis, and more. Automatic chunking, embedding and indexing.
Semantic search finds the most relevant passages in milliseconds, even when the user's question uses different terminology to your documents.
Every response includes clickable references to the original source document, page, and passage — so users can verify and explore further.
Role-based access control ensures users only see information they are authorised to view. SOC 2-ready architecture with data encryption at rest and in transit.
Any organisation sitting on valuable knowledge that's hard to find.
Give agents instant access to product documentation, troubleshooting guides, and policy documents — reducing average handle time by 40%.
Search across thousands of contracts, regulations, and policy documents to find relevant clauses and precedents in seconds.
Replace your dusty intranet with an AI assistant that actually finds the right answer from your company's collective knowledge.
What happens when your AI actually knows your business.
Response accuracy when grounded in verified company data
Fewer escalations to senior staff or subject-matter experts
Access to any document across your entire knowledge base
Every response traceable to its source for compliance and trust
From document dump to intelligent knowledge assistant.
We audit your existing knowledge sources, identify gaps, and design the retrieval architecture. We agree on access controls, update frequency, and success metrics.
Documents are ingested, chunked, and embedded into a vector database. We fine-tune retrieval parameters and build the response generation pipeline with citation logic.
The RAG system goes live with real users. We measure accuracy, gather feedback, and continuously improve retrieval quality and response relevance.
Let's turn your company knowledge into an AI-powered competitive advantage.
Get in TouchServices that complement RAG systems beautifully.
Pair RAG with a conversational interface for a knowledge-powered chatbot.
Learn More →Build agents that use RAG to reason over your data and take action.
Learn More →Extract structured data from documents to feed into your knowledge base.
Learn More →