diff --git a/core/elasticsearch/__init__.py b/core/elasticsearch/__init__.py
index 047b33f4..f3b2d7a9 100644
--- a/core/elasticsearch/__init__.py
+++ b/core/elasticsearch/__init__.py
@@ -34,54 +34,60 @@ functions = [
         "field_value_factor": {
             "field": "brand_priority",
             "modifier": "log1p",
-            "factor": 1.2,
+            "factor": 0.2,
             "missing": 0,
         },
+        "weight": 0.6,
     },
     {
         "filter": Q("term", **{"_index": "products"}),
         "field_value_factor": {
             "field": "rating",
             "modifier": "log1p",
-            "factor": 1.2,
+            "factor": 0.15,
             "missing": 0,
         },
+        "weight": 0.5,
     },
     {
         "filter": Q("term", **{"_index": "products"}),
         "field_value_factor": {
             "field": "total_orders",
             "modifier": "log1p",
-            "factor": 1.3,
+            "factor": 0.25,
             "missing": 0,
         },
+        "weight": 0.7,
     },
     {
         "filter": Q("term", **{"_index": "products"}),
         "field_value_factor": {
             "field": "category_priority",
             "modifier": "log1p",
-            "factor": 1.2,
+            "factor": 0.2,
             "missing": 0,
         },
+        "weight": 0.6,
     },
     {
         "filter": Q("term", **{"_index": "categories"}),
         "field_value_factor": {
             "field": "priority",
             "modifier": "log1p",
-            "factor": 1.5,
+            "factor": 0.25,
             "missing": 0,
         },
+        "weight": 0.8,
     },
     {
         "filter": Q("term", **{"_index": "brands"}),
         "field_value_factor": {
             "field": "priority",
             "modifier": "log1p",
-            "factor": 1.5,
+            "factor": 0.25,
             "missing": 0,
         },
+        "weight": 0.8,
     },
 ]
 
@@ -93,8 +99,8 @@ def process_query(query: str = "", request: Request | None = None) -> dict[str,
     query = query.strip()
     try:
         exact_shoulds = [
-            Q("term", **{"name.raw": query}),
-            Q("term", **{"slug": slugify(query)}),
+            Q("term", **{"name.raw": {"value": query, "boost": 3.0}}),
+            Q("term", **{"slug": {"value": slugify(query), "boost": 2.0}}),
         ]
 
         lang = ""
@@ -142,18 +148,31 @@ def process_query(query: str = "", request: Request | None = None) -> dict[str,
             minimum_should_match=1,
         )
 
-        function_score_query = Q(
-            "function_score",
-            query=query_base,
-            functions=functions,
-            boost_mode="multiply",
-            score_mode="sum",
+        search = (
+            Search(index=["products", "categories", "brands", "posts"])
+            .query(query_base)
+            .extra(
+                rescore={
+                    "window_size": 200,
+                    "query": {
+                        "rescore_query": Q(
+                            "function_score",
+                            query=Q("match_all"),
+                            functions=functions,
+                            boost_mode="sum",
+                            score_mode="sum",
+                            max_boost=2.0,
+                        ).to_dict(),
+                        "query_weight": 1.0,
+                        "rescore_query_weight": 1.0,
+                    },
+                    "track_scores": True,
+                }
+            )
+            .extra(size=100)
         )
-
-        search = Search(index=["products", "categories", "brands", "posts"]).query(function_score_query).extra(size=100)
         response = search.execute()
 
-        # Batch-load related image data to avoid N+1 queries
         results: dict = {"products": [], "categories": [], "brands": [], "posts": []}
         uuids_by_index: dict[str, list] = {"products": [], "categories": [], "brands": []}
         hit_cache: list = []