[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.

company-explorer/backend/app.py

@@ -97,6 +97,8 @@ class ProvisioningResponse(BaseModel):
     website: Optional[str] = None
     vertical_name: Optional[str] = None
     role_name: Optional[str] = None
+    opener: Optional[str] = None # Primary opener (Infrastructure/Cleaning)
+    opener_secondary: Optional[str] = None # Secondary opener (Service/Logistics)
     texts: Dict[str, Optional[str]] = {}
 
 # --- Events ---
@@ -243,6 +245,8 @@ def provision_superoffice_contact(
         website=company.website,
         vertical_name=vertical_name,
         role_name=role_name,
+        opener=company.ai_opener,
+        opener_secondary=company.ai_opener_secondary,
         texts=texts
     )
 
@@ -797,21 +801,29 @@ def run_analysis_task(company_id: int):
 
         logger.info(f"Running Analysis Task for {company.name}")
 
-        # 1. Scrape Website (if not locked)
+        # --- 1. Scrape Website (if not locked) ---
+        # Check for existing scrape data first
         existing_scrape = db.query(EnrichmentData).filter(
             EnrichmentData.company_id == company.id,
             EnrichmentData.source_type == "website_scrape"
         ).first()
 
+        # If it doesn't exist or is not locked, we perform a scrape
         if not existing_scrape or not existing_scrape.is_locked:
-            from .services.scraping import ScraperService
-            scrape_res = ScraperService().scrape_url(company.website)
+            logger.info(f"Scraping website for {company.name}...")
+            scrape_res = scraper.scrape_url(company.website) # Use singleton
+            
+            # Now, either create new or update existing
             if not existing_scrape:
                 db.add(EnrichmentData(company_id=company.id, source_type="website_scrape", content=scrape_res))
+                logger.info("Created new website_scrape entry.")
             else:
                 existing_scrape.content = scrape_res
                 existing_scrape.updated_at = datetime.utcnow()
+                logger.info("Updated existing website_scrape entry.")
             db.commit()
+        else:
+            logger.info("Website scrape is locked. Skipping.")
 
         # 2. Classify Industry & Metrics
         # IMPORTANT: Using the new method name and passing db session

company-explorer/backend/database.py

@@ -150,7 +150,7 @@ class Industry(Base):
     created_at = Column(DateTime, default=datetime.utcnow)
 
 
-class JobRoleMapping(BaseModel):
+class JobRoleMapping(Base):
     """
     Maps job title patterns (regex or simple string) to Roles.
     """
@@ -162,7 +162,7 @@ class JobRoleMapping(BaseModel):
     
     created_at = Column(DateTime, default=datetime.utcnow)
 
-class RawJobTitle(BaseModel):
+class RawJobTitle(Base):
     """
     Stores raw unique job titles imported from CRM to assist in pattern mining.
     Tracks frequency to prioritize high-impact patterns.
@@ -180,7 +180,7 @@ class RawJobTitle(BaseModel):
     created_at = Column(DateTime, default=datetime.utcnow)
     updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
 
-class Persona(BaseModel):
+class Persona(Base):
     """
     Represents a generalized persona/role (e.g. 'Geschäftsführer', 'IT-Leiter')
     independent of the specific job title pattern.

company-explorer/backend/scripts/inspect_therme.py

@@ -0,0 +1,58 @@
+import sys
+import os
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+# Add backend path
+sys.path.append(os.path.join(os.path.dirname(__file__), "../../"))
+
+from backend.database import Company, EnrichmentData
+from backend.config import settings
+
+def inspect_company(company_name_part):
+    engine = create_engine(settings.DATABASE_URL)
+    SessionLocal = sessionmaker(bind=engine)
+    db = SessionLocal()
+    
+    try:
+        print(f"Searching for company containing: '{company_name_part}'...")
+        companies = db.query(Company).filter(Company.name.ilike(f"%{company_name_part}%")).all()
+        
+        if not companies:
+            print("❌ No company found.")
+            return
+
+        for company in companies:
+            print("\n" + "="*60)
+            print(f"🏢 COMPANY: {company.name} (ID: {company.id})")
+            print("="*60)
+            print(f"🌐 Website: {company.website}")
+            print(f"🏗️  Industry (AI): {company.industry_ai}")
+            print(f"📊 Metric: {company.calculated_metric_value} {company.calculated_metric_unit} (Std: {company.standardized_metric_value} m²)")
+            print(f"✅ Status: {company.status}")
+            
+            # Enrichment Data
+            enrichment = db.query(EnrichmentData).filter(EnrichmentData.company_id == company.id).all()
+            print("\n📚 ENRICHMENT DATA:")
+            for ed in enrichment:
+                print(f"   🔹 Type: {ed.source_type} (Locked: {ed.is_locked})")
+                if ed.source_type == "website_scrape":
+                    content = ed.content
+                    if isinstance(content, dict):
+                        summary = content.get("summary", "No summary")
+                        raw_text = content.get("raw_text", "")
+                        print(f"      📝 Summary: {str(summary)[:200]}...")
+                        print(f"      📄 Raw Text Length: {len(str(raw_text))} chars")
+                elif ed.source_type == "wikipedia":
+                    content = ed.content
+                    if isinstance(content, dict):
+                        print(f"      🔗 Wiki URL: {content.get('url')}")
+                        print(f"      📄 Content Snippet: {str(content.get('full_text', ''))[:200]}...")
+
+    except Exception as e:
+        print(f"Error: {e}")
+    finally:
+        db.close()
+
+if __name__ == "__main__":
+    inspect_company("Therme Erding")

company-explorer/backend/scripts/migrate_opener.py

@@ -0,0 +1,31 @@
+from sqlalchemy import create_engine, text
+import sys
+import os
+
+# Add backend path
+sys.path.append(os.path.join(os.path.dirname(__file__), "../../"))
+from backend.config import settings
+
+def migrate():
+    engine = create_engine(settings.DATABASE_URL)
+    with engine.connect() as conn:
+        try:
+            # Check if column exists
+            print("Checking schema...")
+            # SQLite specific pragma
+            result = conn.execute(text("PRAGMA table_info(companies)"))
+            columns = [row[1] for row in result.fetchall()]
+            
+            if "ai_opener" in columns:
+                print("Column 'ai_opener' already exists. Skipping.")
+            else:
+                print("Adding column 'ai_opener' to 'companies' table...")
+                conn.execute(text("ALTER TABLE companies ADD COLUMN ai_opener TEXT"))
+                conn.commit()
+                print("✅ Migration successful.")
+                
+        except Exception as e:
+            print(f"❌ Migration failed: {e}")
+
+if __name__ == "__main__":
+    migrate()

company-explorer/backend/scripts/test_opener_generation.py

@@ -0,0 +1,41 @@
+import sys
+import os
+import logging
+
+# Add backend path
+sys.path.append(os.path.join(os.path.dirname(__file__), "../../"))
+
+# Mock logging
+logging.basicConfig(level=logging.INFO)
+
+# Import Service
+from backend.services.classification import ClassificationService
+
+def test_opener_generation():
+    service = ClassificationService()
+    
+    print("\n--- TEST: Therme Erding (Primary Focus: Hygiene) ---")
+    op_prim = service._generate_marketing_opener(
+        company_name="Therme Erding",
+        website_text="Größte Therme der Welt, 35 Saunen, Rutschenparadies Galaxy, Wellenbad. Täglich tausende Besucher.",
+        industry_name="Leisure - Wet & Spa",
+        industry_pains="Rutschgefahr und Hygiene",
+        focus_mode="primary"
+    )
+    print(f"Primary Opener: {op_prim}")
+
+    print("\n--- TEST: Dachser Logistik (Secondary Focus: Process) ---")
+    op_sec = service._generate_marketing_opener(
+        company_name="Dachser SE",
+        website_text="Globaler Logistikdienstleister, Warehousing, Food Logistics, Air & Sea Logistics. Intelligent Logistics.",
+        industry_name="Logistics - Warehouse",
+        industry_pains="Effizienz und Sicherheit",
+        focus_mode="secondary"
+    )
+    print(f"Secondary Opener: {op_sec}")
+
+if __name__ == "__main__":
+    try:
+        test_opener_generation()
+    except Exception as e:
+        print(f"Test Failed (likely due to missing env/deps): {e}")

company-explorer/backend/services/classification.py

@@ -75,10 +75,12 @@ Source Text:
 {text_content[:6000]}
 
 Return a JSON object with:
-- "raw_value": The number found (e.g. 352 or 352.0). If text says "352 Betten", extract 352. If not found, null.
+- "raw_value": The number found (e.g. 352 or 352.0). If not found, null.
 - "raw_unit": The unit found (e.g. "Betten", "m²").
 - "proof_text": A short quote from the text proving this value.
 
+**IMPORTANT:** Ignore obvious year numbers (like 1900-2026) if other, more plausible metric values are present in the text. Focus on the target metric.
+
 JSON ONLY.
 """
         try:
@@ -159,8 +161,8 @@ JSON ONLY.
             try:
                 args = (company,) if source_name == 'website' else (db, company.id) if source_name == 'wikipedia' else (company, search_term)
                 content_text, current_source_url = content_loader(*args)
-                if not content_text:
-                    logger.info(f"No content for {source_name}.")
+                if not content_text or len(content_text) < 100:
+                    logger.info(f"No or insufficient content for {source_name} (Length: {len(content_text) if content_text else 0}).")
                     continue
                 llm_result = self._run_llm_metric_extraction_prompt(content_text, search_term, industry_name)
                 if llm_result:
@@ -224,13 +226,68 @@ JSON ONLY.
         company.metric_confidence_reason = metrics["metric_confidence_reason"]
         
         company.last_classification_at = datetime.utcnow()
-        db.commit()
+        # REMOVED: db.commit() - This should be handled by the calling function.
         return company
 
     def reevaluate_wikipedia_metric(self, company: Company, db: Session, industry: Industry) -> Company:
         logger.info(f"Re-evaluating metric for {company.name}...")
         return self.extract_metrics_for_industry(company, db, industry)
 
+    def _generate_marketing_opener(self, company_name: str, website_text: str, industry_name: str, industry_pains: str, focus_mode: str = "primary") -> Optional[str]:
+        """
+        Generates the 'First Sentence' (Opener).
+        focus_mode: 'primary' (Standard/Cleaning) or 'secondary' (Service/Logistics).
+        """
+        if not industry_pains:
+            industry_pains = "Effizienz und Personalmangel" # Fallback
+
+        # Dynamic Focus Instruction
+        if focus_mode == "secondary":
+            focus_instruction = """
+   - **FOKUS: SEKUNDÄR-PROZESSE (Logistik/Service/Versorgung).**
+   - Ignoriere das Thema Reinigung. Konzentriere dich auf **Abläufe, Materialfluss, Entlastung von Fachkräften** oder **Gäste-Service**.
+   - Der Satz muss einen operativen Entscheider (z.B. Pflegedienstleitung, Produktionsleiter) abholen."""
+        else:
+            focus_instruction = """
+   - **FOKUS: PRIMÄR-PROZESSE (Infrastruktur/Sauberkeit/Sicherheit).**
+   - Konzentriere dich auf Anforderungen an das Facility Management, Hygiene, Außenwirkung oder Arbeitssicherheit.
+   - Der Satz muss einen Infrastruktur-Entscheider (z.B. FM-Leiter, Geschäftsführer) abholen."""
+
+        prompt = f"""
+Du bist ein exzellenter B2B-Stratege und Texter.
+Deine Aufgabe ist es, einen hochpersonalisierten Einleitungssatz für eine E-Mail an ein potenzielles Kundenunternehmen zu formulieren.
+
+--- KONTEXT ---
+Zielunternehmen: {company_name}
+Branche: {industry_name}
+Operative Herausforderung (Pain): "{industry_pains}"
+
+Webseiten-Kontext:
+{website_text[:2500]}
+
+--- Denkprozess & Stilvorgaben ---
+1. **Analysiere den Kontext:** Verstehe das Kerngeschäft.
+2. **Identifiziere den Hebel:** Was ist der Erfolgsfaktor in Bezug auf den FOKUS?
+3. **Formuliere den Satz (ca. 20-35 Wörter):**
+   - Wähle einen eleganten, aktiven Einstieg.
+   - Verbinde die **Tätigkeit** mit dem **Hebel** und den **Konsequenzen**.
+   - **WICHTIG:** Formuliere als positive Beobachtung über eine Kernkompetenz.
+   - **VERMEIDE:** Konkrete Zahlen.
+   - Verwende den Firmennamen: {company_name}.
+   {focus_instruction}
+
+--- Deine Ausgabe ---
+Gib NUR den finalen Satz aus. Keine Anführungszeichen.
+"""
+        try:
+            response = call_gemini_flash(prompt)
+            if response:
+                return response.strip().strip('"')
+            return None
+        except Exception as e:
+            logger.error(f"Opener Generation Error: {e}")
+            return None
+
     def classify_company_potential(self, company: Company, db: Session) -> Company:
         logger.info(f"Starting classification for {company.name}...")
         
@@ -249,12 +306,29 @@ JSON ONLY.
         suggested_industry_name = self._run_llm_classification_prompt(website_content, company.name, industry_defs)
         logger.info(f"AI suggests industry: {suggested_industry_name}")
         
-        # 4. Update Company
-        # Match back to DB object
+        # 4. Update Company & Generate Openers
         matched_industry = next((i for i in industries if i.name == suggested_industry_name), None)
         
         if matched_industry:
             company.industry_ai = matched_industry.name
+            
+            # --- Generate PRIMARY Opener (Infrastructure/Cleaning) ---
+            op_prim = self._generate_marketing_opener(
+                company.name, website_content, matched_industry.name, matched_industry.pains, "primary"
+            )
+            if op_prim:
+                company.ai_opener = op_prim
+                logger.info(f"Opener (Primary): {op_prim}")
+
+            # --- Generate SECONDARY Opener (Service/Logistics) ---
+            # Only if relevant (could be optimized, but generating always is safer for "Dual Strategy")
+            op_sec = self._generate_marketing_opener(
+                company.name, website_content, matched_industry.name, matched_industry.pains, "secondary"
+            )
+            if op_sec:
+                company.ai_opener_secondary = op_sec
+                logger.info(f"Opener (Secondary): {op_sec}")
+            
         else:
             company.industry_ai = "Others" 
             

company-explorer/frontend/src/components/Inspector.tsx

@@ -57,6 +57,10 @@ type CompanyDetail = {
   // Industry Strategy (V2)
   industry_details?: IndustryDetails
 
+  // Marketing AI (V3)
+  ai_opener: string | null
+  ai_opener_secondary: string | null
+
   // NEU v0.7.0: Quantitative Metrics
   calculated_metric_name: string | null
   calculated_metric_value: number | null
@@ -453,6 +457,43 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
     )
   }
 
+  // Marketing AI Card Renderer
+  const renderMarketingCard = () => {
+    if (!data?.ai_opener && !data?.ai_opener_secondary) return null;
+    
+    return (
+      <div className="bg-orange-50 dark:bg-orange-900/10 rounded-xl p-5 border border-orange-100 dark:border-orange-900/50 mb-6">
+        <h3 className="text-sm font-semibold text-orange-700 dark:text-orange-300 uppercase tracking-wider mb-3 flex items-center gap-2">
+          <Bot className="h-4 w-4" /> Marketing AI (Openers)
+        </h3>
+        
+        <div className="space-y-4">
+           {data.ai_opener && (
+             <div className="p-3 bg-white dark:bg-slate-900 rounded border border-orange-200 dark:border-orange-800">
+               <div className="flex justify-between items-center mb-1">
+                 <div className="text-[10px] text-orange-600 dark:text-orange-400 uppercase font-bold tracking-tight">Primary: Infrastructure/Cleaning</div>
+               </div>
+               <div className="text-sm text-slate-700 dark:text-slate-200 leading-relaxed italic">"{data.ai_opener}"</div>
+             </div>
+           )}
+           
+           {data.ai_opener_secondary && (
+             <div className="p-3 bg-white dark:bg-slate-900 rounded border border-orange-200 dark:border-orange-800">
+               <div className="flex justify-between items-center mb-1">
+                 <div className="text-[10px] text-orange-600 dark:text-orange-400 uppercase font-bold tracking-tight">Secondary: Service/Logistics</div>
+               </div>
+               <div className="text-sm text-slate-700 dark:text-slate-200 leading-relaxed italic">"{data.ai_opener_secondary}"</div>
+             </div>
+           )}
+           
+           <p className="text-[10px] text-slate-500 text-center">
+             These sentences are statically pre-calculated for the "First Sentence Matching" strategy.
+           </p>
+        </div>
+      </div>
+    )
+  }
+
   // CRM Comparison and Data Quality Renderer
   const renderDataQualityCard = () => {
     if (!data) return null;
@@ -754,6 +795,7 @@ export function Inspector({ companyId, initialContactId, onClose, apiBase }: Ins
 
                 {renderDataQualityCard()}
                 {renderStrategyCard()}
+                {renderMarketingCard()}
 
                 <div className="bg-slate-50 dark:bg-slate-950 rounded-lg p-4 border border-slate-200 dark:border-slate-800 flex flex-col gap-2">
                   <div className="flex items-center justify-between mb-1">

connector-superoffice/README.md

@@ -111,3 +111,23 @@ Der Connector ist der Bote, der diese Daten in das CRM bringt.
 Die Prompts für Matrix und Opener liegen in:
 *   Matrix: `backend/scripts/generate_matrix.py`
 *   Opener: `backend/services/classification.py` (oder `enrichment.py`)
+
+## Appendix: The "First Sentence" Prompt
+
+This is the core logic used to generate the company-specific opener.
+
+**Goal:** Prove understanding of the business model + imply the pain (positive observation).
+
+```text
+Du bist ein exzellenter B2B-Stratege und Texter mit einem tiefen Verständnis für operative Prozesse.
+Deine Aufgabe ist es, einen hochpersonalisierten, scharfsinnigen und wertschätzenden Einleitungssatz für eine E-Mail an ein potenzielles Kundenunternehmen zu formulieren.
+
+--- Denkprozess & Stilvorgaben ---
+1. **Analysiere den Kontext:** Verstehe das Kerngeschäft. Was ist die kritische, physische Tätigkeit vor Ort? (z.B. 'Betrieb von Hochregallagern', 'Pflege von Patienten').
+2. **Identifiziere den Hebel:** Was ist der Erfolgsfaktor? (z.B. 'reibungslose Abläufe', 'maximale Hygiene').
+3. **Formuliere den Satz (ca. 20-35 Wörter):**
+   - Wähle einen eleganten, aktiven Einstieg wie 'Speziell im Bereich...' oder 'Der reibungslose Betrieb...'.
+   - Verbinde die **spezifische Tätigkeit** mit dem **Hebel** und den **geschäftlichen Konsequenzen**.
+   - **WICHTIG:** Formuliere immer als positive Beobachtung über eine Kernkompetenz. Du implizierst die Herausforderung durch die Betonung der Wichtigkeit.
+   - **VERMEIDE:** Konkrete Zahlen (z.B. "35 Rutschen"), da diese veraltet sein können. Nutze abstrakte Größen ("weitläufige Anlagen").
+```

inspect_sqlite_native.py

@@ -0,0 +1,56 @@
+import sqlite3
+import json
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+
+def inspect(name_part):
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"Searching for '{name_part}' in {DB_PATH}...")
+        cursor.execute("SELECT id, name, website, industry_ai, calculated_metric_value, standardized_metric_value FROM companies WHERE name LIKE ?", (f'%{name_part}%',))
+        companies = cursor.fetchall()
+        
+        if not companies:
+            print("No hits.")
+            return
+
+        for c in companies:
+            cid, name, website, industry, metric, std_metric = c
+            print("\n" + "="*40)
+            print(f"🏢 {name} (ID: {cid})")
+            print(f"   Vertical: {industry}")
+            print(f"   Website: {website}")
+            print(f"   Metric: {metric} (Std: {std_metric})")
+            
+            # Fetch Enrichment Data
+            cursor.execute("SELECT source_type, content FROM enrichment_data WHERE company_id = ?", (cid,))
+            rows = cursor.fetchall()
+            print("\n   📚 Enrichment Data:")
+            for r in rows:
+                stype, content_raw = r
+                print(f"   - {stype}")
+                try:
+                    content = json.loads(content_raw)
+                    if stype == "website_scrape":
+                        summary = content.get("summary", "")
+                        raw = content.get("raw_text", "")
+                        print(f"     > Summary: {summary[:150]}...")
+                        print(f"     > Raw Length: {len(raw)}")
+                        if len(raw) > 500:
+                             print(f"     > Raw Snippet: {raw[:300]}...")
+                    elif stype == "wikipedia":
+                        print(f"     > URL: {content.get('url')}")
+                        intro = content.get("intro_text", "") or content.get("full_text", "")
+                        print(f"     > Intro: {str(intro)[:150]}...")
+                except:
+                    print("     > (Content not valid JSON)")
+
+    except Exception as e:
+        print(f"Error: {e}")
+    finally:
+        if conn: conn.close()
+
+if __name__ == "__main__":
+    inspect("Therme Erding")

migrate_opener_native.py

@@ -0,0 +1,29 @@
+import sqlite3
+import sys
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+
+def migrate():
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"Checking schema in {DB_PATH}...")
+        cursor.execute("PRAGMA table_info(companies)")
+        columns = [row[1] for row in cursor.fetchall()]
+        
+        if "ai_opener" in columns:
+            print("Column 'ai_opener' already exists. Skipping.")
+        else:
+            print("Adding column 'ai_opener' to 'companies' table...")
+            cursor.execute("ALTER TABLE companies ADD COLUMN ai_opener TEXT")
+            conn.commit()
+            print("✅ Migration successful.")
+            
+    except Exception as e:
+        print(f"❌ Migration failed: {e}")
+    finally:
+        if conn: conn.close()
+
+if __name__ == "__main__":
+    migrate()

migrate_opener_secondary.py

@@ -0,0 +1,29 @@
+import sqlite3
+import sys
+
+DB_PATH = "/app/companies_v3_fixed_2.db"
+
+def migrate():
+    try:
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        
+        print(f"Checking schema in {DB_PATH}...")
+        cursor.execute("PRAGMA table_info(companies)")
+        columns = [row[1] for row in cursor.fetchall()]
+        
+        if "ai_opener_secondary" in columns:
+            print("Column 'ai_opener_secondary' already exists. Skipping.")
+        else:
+            print("Adding column 'ai_opener_secondary' to 'companies' table...")
+            cursor.execute("ALTER TABLE companies ADD COLUMN ai_opener_secondary TEXT")
+            conn.commit()
+            print("✅ Migration successful.")
+            
+    except Exception as e:
+        print(f"❌ Migration failed: {e}")
+    finally:
+        if conn: conn.close()
+
+if __name__ == "__main__":
+    migrate()