diff --git a/agent/evolution/data_lake_optimizer.py b/agent/evolution/data_lake_optimizer.py
new file mode 100644
index 000000000..8fbe0d735
--- /dev/null
+++ b/agent/evolution/data_lake_optimizer.py
@@ -0,0 +1,50 @@
+"""Phase 16: Sovereign Data Lake & Vector Database Optimization.
+
+Builds and optimizes a massive, sovereign data lake for all Timmy-related research.
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+
+logger = logging.getLogger(__name__)
+
+class DataLakeOptimizer:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+
+    def deep_index_document(self, doc_content: str, metadata: Dict[str, Any]) -> Dict[str, Any]:
+        """Performs deep semantic indexing and metadata generation for a document."""
+        logger.info("Performing deep semantic indexing for document.")
+        
+        prompt = f"""
+Document Content:
+{doc_content}
+
+Existing Metadata:
+{json.dumps(metadata, indent=2)}
+
+Please perform a 'Deep Indexing' of this document.
+Identify core concepts, semantic relationships, and cross-references to other Timmy Foundation research.
+Generate high-fidelity semantic metadata and a set of 'Knowledge Triples' for the SIKG.
+
+Format the output as JSON:
+{{
+  "semantic_summary": "...",
+  "key_concepts": [...],
+  "cross_references": [...],
+  "triples": [{{"s": "subject", "p": "predicate", "o": "object"}}],
+  "vector_embedding_hints": "..."
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Data Lake Optimizer. Your goal is to turn raw data into a highly structured, semantically rich knowledge base.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        indexing_data = json.loads(result["text"])
+        return indexing_data