International Journal on Science and Technology

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Memory Keeper: AI for Organizational Decision Traceability

Author(s) Mr. Aryan Yadav, Ms. Chanchal Budhadeo, Mr. Sagar Zope, Prof. Ganesh Wadmare
Country India
Abstract Critical business decisions do not happen in isolation in a particular location; rather, the operational environment in which such business decisions take place gets dispersed in messaging conversations, email threads, and meeting discussions. It may not be feasible to follow the rationale for a particular business decision taken months ago. In the end, the process becomes obscure, and there is a marked lack of traceability.

A hybrid solution named Memory Keeper is proposed to overcome the problem of data silos. By combining Knowledge Graphs (KG) with vector stores to build an improved Retrieval-Augmented Generation (RAG) model, the framework continuously feeds distributed communication flows into the model. It processes unstructured information to identify essential decisions and maps structural relationships between stakeholders, timelines, and topics into a queryable KG.

Although conventional semantic search approaches usually ignore the interdependencies of events, Memory Keeper can retrieve historical context with high accuracy by using graph-based reasoning to route vector embeddings. This approach makes it possible to generate traceable and explainable responses to user queries, thus making enterprise knowledge an active tool that supports rapid decision-making with well-documented evidence.
Keywords Organizational Memory, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Semantic Search, Enterprise Knowledge Management, Decision Traceability.
Field Engineering
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-03-20
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