diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
-# Source Sentinel - 2025 @sjDev - LICENSE: MIT
+# Citation Sentinel - 2025 @sjDev - LICENSE: MIT
 
 
-![SOURCE Sentinel UI showing the source sidebar, natural language answer with citations to the source, and groundedness badge](demo.jpg)
+![Citation Sentinel UI showing the source sidebar, natural language answer with citations to the source, and groundedness badge](demo.jpg)
 
 Image one:  source-grounded research assistant for applied use of LLMs. 
 
-**Source Sentinel's leverages multiple Large Language Models for analyzing and conveniently working with large data corpora, when query-result accuracy is of the highest value.  Examples include complex litigation (i.e. Patent/IP), technical medical data exploration (reviewing health records, test results, or studies to find hidden patterns, trends, and answers), advanced LLM development and refinement.**
+**Citation Sentinel leverages multiple Large Language Models for analyzing and conveniently working with large data corpora, when query-result accuracy is of the highest value.  Examples include complex litigation (i.e. Patent/IP), technical medical data exploration (reviewing health records, test results, or studies to find hidden patterns, trends, and answers), advanced LLM development and refinement.**
 
-Source Sentinel empowers users to transform an otherwise-unmanageably-large corpus of data on any topic of interest into refined, easily-digested subtopics and pose focused inquiries, to instantly receive concise, quantifiably-evaluated, accurate responses.  The system also provides suggested follow-up questions further refining in user inquiries by detecting the query goals.
+Citation Sentinel empowers users to transform an otherwise-unmanageably-large corpus of data on any topic of interest into refined, easily-digested subtopics and pose focused inquiries, to instantly receive concise, quantifiably-evaluated, accurate responses.  The system also provides suggested follow-up questions further refining in user inquiries by detecting the query goals.
 
 ALSO NOTE IN DEMO: clickable inline citations (in blue) that take user to source-grounded research basis/knowledge corpus supporting query response, with rated “groundedness” score.
 
@@ -15,7 +15,7 @@ ALSO NOTE IN DEMO: clickable inline citations (in blue) that take user to source
 # Reliability and Understandability
 
 
-Source Sentinel's Retrieval-Augmented Generation (RAG) pipeline uses two-stage pass-through to Voyage AI embedding and ranking models, which uses cosine similarity methodology to scoring to evaluate query-source embeddings in multi-dimensional vector space.  This yields  a "groundedness" score.
+Citation Sentinel's Retrieval-Augmented Generation (RAG) pipeline uses two-stage pass-through to Voyage AI embedding and ranking models, which uses cosine similarity methodology to scoring to evaluate query-source embeddings in multi-dimensional vector space.  This yields  a "groundedness" score.
 
 
 Groundedness metrics quantify how well an AI-generated answer is supported by retrieved context. It measures "faithfulness" to source documents, ensuring the answer is not hallucinated, inaccurate or stale (pulled from the model's training data).