RAG & Knowledge Assistants
Organization-specific AI assistants grounded in relevant documents and business knowledge.
Put organizational knowledge to work
Risenine builds knowledge assistants using retrieval-augmented generation. RAG retrieves relevant material from a defined information source and supplies it to a language model as context for an answer.
This approach is useful when an assistant needs access to organization-specific information. It does not require describing the model as trained on your documents: document retrieval and model training are different operations.
Define the knowledge scope
A RAG engagement begins with the intended users, source documents, update frequency, and questions the assistant should answer. Data preparation and retrieval quality directly affect whether the generated answer has useful supporting context.
Source attribution, document permissions, stale information, conflicting sources, and unanswered questions are important requirements to establish. The implementation should fit the organization’s information and access policies.
Test retrieval and answers separately
Evaluation should check whether the correct source is retrieved and whether the answer faithfully uses that source. Relevant test questions should include incomplete information and cases where the system should acknowledge uncertainty.
Our organizational RAG chatbot is a related portfolio project. Share sample documents and representative questions to discuss an assistant scope suited to your organization.
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Share the problem, available data, and workflow you want to improve.
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