[2ff88f42] feat(GTM-Engine): Implement Dual Opener Strategy & Harden Analysis

Completed the GTM engine setup:\n\n- Implemented 'Dual Opener' generation (Primary/Secondary) in ClassificationService.\n- Migrated DB to support two opener fields.\n- Updated API and Frontend to handle and display both openers.\n- Fixed bug creating duplicate website_scrape entries.\n- Hardened metric extraction by improving the LLM prompt and adding content length checks.

GEMINI.md

@@ -143,10 +143,33 @@ Since the "Golden Record" for Industry Verticals (Pains, Gains, Products) reside
 
 3.  **`list_notion_structure.py` (Schema Discovery):**
     -   **Purpose:** Lists all property keys and page titles. Use this to debug schema changes (e.g. if a column was renamed).
-    -   **Usage:** `python3 list_notion_structure.py`
-
-## Next Steps
-*   **Marketing Automation:** Implement the actual sending logic (or export) based on the contact status.
-*   **Job Role Mapping Engine:** Connect the configured patterns to the contact import/creation process to auto-assign roles.
-*   **Industry Classification Engine:** Connect the configured industries to the AI Analysis prompt to enforce the "Strict Mode" mapping.
-*   **Export:** Generate Excel/CSV enriched reports (already partially implemented via JSON export).
+        - **Usage:** `python3 list_notion_structure.py`
+    
+    ## Next Steps
+    *   **Marketing Automation:** Implement the actual sending logic (or export) based on the contact status.
+    *   **Job Role Mapping Engine:** Connect the configured patterns to the contact import/creation process to auto-assign roles.
+    *   **Industry Classification Engine:** Connect the configured industries to the AI Analysis prompt to enforce the "Strict Mode" mapping.
+    *   **Export:** Generate Excel/CSV enriched reports (already partially implemented via JSON export).
+    
+    ## Company Explorer Access & Debugging
+    
+    The **Company Explorer** is the central intelligence engine.
+    
+    **Core Paths:**
+    *   **Database:** `/app/companies_v3_fixed_2.db` (SQLite)
+    *   **Backend Code:** `/app/company-explorer/backend/`
+    *   **Logs:** `/app/logs_debug/company_explorer_debug.log`
+    
+    **Accessing Data:**
+    To inspect live data without starting the full stack, use `sqlite3` directly or the helper scripts (if environment permits).
+    
+    *   **Direct SQL:** `sqlite3 /app/companies_v3_fixed_2.db "SELECT * FROM companies WHERE name LIKE '%Firma%';" `
+    *   **Python (requires env):** The app runs in a Docker container. When debugging from outside (CLI agent), Python dependencies like `sqlalchemy` might be missing in the global scope. Prefer `sqlite3` for quick checks.
+    
+    **Key Endpoints (Internal API :8000):**
+    *   `POST /api/provision/superoffice-contact`: Triggers the text generation logic.
+    *   `GET /api/companies/{id}`: Full company profile including enrichment data.
+    
+    **Troubleshooting:**
+    *   **"BaseModel" Error:** Usually a mix-up between Pydantic and SQLAlchemy `Base`. Check imports in `database.py`.
+    *   **Missing Dependencies:** The CLI agent runs in `/app` but not necessarily inside the container's venv. Use standard tools (`grep`, `sqlite3`) where possible.