RAG and Knowledge Systems
Building knowledge systems that let language models work with your organisation’s own documentation, data, and operational context.
A general-purpose language model knows a great deal about the world and very little about your organisation. Retrieval-augmented generation changes that. It connects the model to your own knowledge base; documents, manuals, policies, case histories, operational data, so that outputs are grounded in what your organisation actually knows, not what the model was trained on.
The difference is significant. A model answering from your documentation produces specific, verifiable, auditable outputs. A model answering from training data produces approximations.
Conqorde designs and builds RAG systems scoped to your knowledge environment. This includes document ingestion and processing, vector database architecture, retrieval pipeline design, and integration with the language model and user interface.
What this covers:
- Document ingestion, processing and chunking
- Vector database selection and configuration
- Retrieval pipeline design and optimisation
- Integration with language models and existing interfaces
- Access control and document governance
- Ongoing maintenance and knowledge base updates
Who this is for:
Organisations with large volumes of internal documentation that cannot be effectively searched or used by standard tools. Legal, compliance, and regulatory teams that need AI outputs grounded in specific source documents. Operations and technical teams that need fast, accurate access to internal knowledge without manual search.