Epistemic Corpus Design and Retrieval Stability in Enterprise AI: A Case Study of PROTEX Migration to Microsoft Copilot Studio

This technical report presents a methodological case study examining the migration of PROTEX — an epistemically structured behavioural retrieval environment — from a custom retrieval architecture into native Microsoft Copilot Studio.

The study investigates how retrieval stability, comparative reasoning, hallucination resistance, uncertainty handling, and semantic contamination behaviour are affected when infrastructure complexity is substantially reduced while epistemic corpus structure remains highly organised.

Prior to migration, PROTEX operated through a custom retrieval stack incorporating OpenAI embeddings, Pinecone vector retrieval, orchestration middleware, behavioural routing mechanisms, and controlled contextual segmentation. For the migration experiment, most of this infrastructure was intentionally removed. The corpus was transferred into native Microsoft Copilot Studio using built-in orchestration and native knowledge ingestion without Azure AI Search, external vector databases, or custom retrieval middleware.

The benchmark focused on retrieval behaviour under sustained epistemic pressure rather than conventional document retrieval alone. Evaluation areas included factual retrieval precision, comparative behavioural synthesis, hallucination resistance, semantic contamination testing, moderation-boundary behaviour, false-premise handling, uncertainty preservation, and refusal quality.

The report introduces the concept of semantic nearest-neighbour completion as a distinct form of retrieval instability in which models overextend semantically adjacent structures rather than generating fully fabricated outputs. The study also examines moderation systems as active epistemic constraints influencing abstraction depth, comparative synthesis, and uncertainty expression.

One of the central findings is that epistemic corpus structure — including behavioural decomposition, evidentiary hierarchy, uncertainty-aware segmentation, contextual boundary preservation, and anti-speculative analytical framing — may significantly influence retrieval stability independently of infrastructure complexity.

The report argues that retrieval engineering alone cannot fully compensate for epistemic disorder and suggests that carefully structured knowledge environments may substantially improve native enterprise AI behaviour even within simplified orchestration systems.