diff --git a/qdrant-landing/content/blog/case-study-go-perfect.md b/qdrant-landing/content/blog/case-study-go-perfect.md
index 0964c77d1..adf108b48 100644
--- a/qdrant-landing/content/blog/case-study-go-perfect.md
+++ b/qdrant-landing/content/blog/case-study-go-perfect.md
@@ -71,7 +71,7 @@ Within GoPerfect's user-facing interactive, sub-agent-loop latency budget, the a
 
 ### How it works in production
 
-The production pipeline runs in three layers. Ingestion enriches profile data from multiple sources (professional networks, code repositories, company data, and AI-derived signals) and writes structured multivector points into Qdrant. Retrieval combines hybrid search with category-specific multivector queries to return a high-confidence candidate pool. An LLM orchestration layer above Qdrant runs the agent loop: it generates sub-questions, issues parallel searches, evaluates the returned candidates, and assembles the final ranked shortlist.
+The production pipeline runs in three layers. Ingestion enriches profile data from multiple sources (professional networks, code repositories, company data, and AI-derived signals) and writes structured multivector points into Qdrant. Retrieval combines hybrid search with multivector queries to return a high-confidence candidate pool. An LLM orchestration layer above Qdrant runs the agent loop: it generates sub-questions, issues parallel searches, evaluates the returned candidates, and assembles the final ranked shortlist.
 
 GoPerfect also runs a candidate scoring and fraud-signal layer that cross-references claims in a resume against external evidence. A candidate who lists a programming language, for example, gets validated against their public code activity. The scoring layer also learns from outcomes: when a candidate flagged with a fraud signal goes on to get hired through normal channels, the system weights similar signals more leniently going forward. Recruiters see a graphical view of each candidate that includes both the ranking and the supporting evidence, with adjustable tolerance for fraud signals depending on the role context.