Learning from use
Proposes a governed learning loop that turns user corrections and usage signals into improvements across knowledge, search, instructions, memory and model training, with evaluation and human approval before release.
Read the paperRESEARCH / ENTERPRISE AI
Grounded answers from enterprise knowledge
Describes how Corporate GPT combines specialist agents, document search, source citations and permissions to answer enterprise questions, with human document selection, traceability and limits on retrieval.
Read on this pageAt a glance
A useful enterprise assistant must find relevant knowledge, show where an answer came from and respect who is allowed to see it. This paper describes the retrieval architecture behind Corporate GPT, including how it coordinates specialist agents and chooses the material used to answer a question.
The workflow can preview matching sources and let a person select documents before generating an answer. It combines different search methods, optional deeper ranking and permission checks, while recording the retrieval path for inspection. The paper also follows documents through ingestion and into the index. Its relevance is the complete knowledge workflow: source selection, authorisation and traceability are treated as part of answering, rather than details added after a fluent response has already been generated.
What the paper covers
A preview-and-select flow gives users a role in choosing the evidence for an answer.
Hybrid search and optional reranking help locate relevant passages in enterprise documents.
Permissions and retrieval traces are part of the described architecture throughout the flow.
Research context
This paper documents an implemented architecture and its intended grounding and access-control contracts. It is not a universal accuracy or security certification. Answer quality depends on available source content, retrieval behaviour, configuration and validation in the target deployment.
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