feat(market-intel): Optimize outreach quality and remove hallucination bias. Implemented context-sensitive technographics and strategic bridge prompting.
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@@ -114,24 +114,46 @@ Für eine schnelle Entwicklung ist "Sideloading" für die Python-Logik aktiviert
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---
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## 6. Status Update (2025-12-22) - Campaign Engine & Reporting
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### Status Update (2026-01-14) - Quality & Stability Refinements
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### Erreichte Meilensteine:
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1. **Rollenbasierte Campaign-Engine:**
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* Die Funktion `generate_outreach_campaign` wurde komplett überarbeitet.
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* Sie nutzt nun die volle Tiefe der Knowledge Base (`yamaichi_neu.md`), um **personalisierte Sequenzen für spezifische Rollen** (z.B. "Hardware-Entwickler" vs. "Einkäufer") zu erstellen.
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* Die Ansprache erfolgt strikt im "Partner auf Augenhöhe"-Tonfall.
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* **Social Proof Integration:** Der Absender (`reference_url`) wird als Beweis der Kompetenz inkl. passender KPIs im Abbinder integriert.
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* **"Grit"-Prompting:** Der Prompt wurde massiv geschärft, um operative Schmerzpunkte ("ASNs", "Bandstillstand") statt Marketing-Bla-Bla zu nutzen.
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2. **Report Polishing (Frontend):**
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* Der Markdown-Export (`StepReport.tsx`) wurde erweitert.
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* Er enthält nun die **"Proof-Links"** (Beweise/URLs) direkt in den Tabellenzellen, sauber formatiert. Damit ist die Herleitung der Ergebnisse (z.B. "Warum nutzt der Kunde Ariba?") auch im Export transparent nachvollziehbar.
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3. **Frontend UX & Bugfixes:**
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* **Kein doppelter Upload:** `StepOutreach.tsx` wurde angepasst, um den Strategie-Kontext aus Schritt 1 direkt zu übernehmen.
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* **Lösch-Bug:** `StepReview.tsx` wurde korrigiert, sodass gelöschte Unternehmen sofort aus der UI verschwinden.
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* **Crash-Fix:** Die Behandlung der API-Antwort in `geminiService.ts` wurde gehärtet, um die neue verschachtelte Antwortstruktur der Campaign-Engine korrekt zu verarbeiten.
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**Erreichte Meilensteine:**
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1. **Anti-Halluzinations-Fix (Technographic Audit):**
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* **Problem:** Die KI hat aufgrund von Suggestiv-Prompts ("Look for SAP Ariba") oft Technologien halluziniert oder irrelevante Systeme als Wettbewerber eingestuft.
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* **Lösung:** Entfernung aller festcodierten "Suggestiv-Listen" aus dem Code. Der Audit sucht nun rein faktenbasiert oder basierend auf der expliziten Strategie-Eingabe.
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* **Ergebnis:** Keine "falschen Feinde" mehr. Wenn keine Konkurrenz-Hardware gefunden wird, wird korrekt "Greenfield" (Status Quo: Manuell) erkannt.
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2. **Outreach-Optimierung ("Strategic Observer"):**
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* **Prompting:** Radikale Überarbeitung des Outreach-Prompts.
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* **Stil:** Weg vom "Verkäufer", hin zum "Scharfsinnigen Branchenbeobachter".
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* **Opportunity Bridge:** Die E-Mails schlagen in der ersten Nachricht sofort die Brücke von der Beobachtung (z.B. "Expansion") zur Lösungskategorie (z.B. "Autonome Reinigungsrobotik"), ohne plump Features zu verkaufen.
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* **Kontext-Sensitivität:** Technologische Signale (wie ERP-Systeme) werden nur noch bei Rollen erwähnt, für die sie relevant sind (CIO, CFO), aber bei operativen Rollen (Facility Management) ausgeblendet, um Verwirrung zu vermeiden.
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3. **Produktionsreife:**
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* Der Prozess liefert nun konsistent hochwertige, C-Level-taugliche Ansprachen, die strategische Schmerzpunkte mit operativen Lösungen verbinden.
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### Nächste Schritte:
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* **Stabilitäts-Test:** Ausführung eines Batch-Audits mit >20 Firmen, um Rate-Limits und Fehlerbehandlung unter Last zu prüfen.
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* **Regelbetrieb & Monitoring:** Überwachung der Qualität bei neuen Branchen.
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--- End of content ---
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@@ -388,14 +388,29 @@ def analyze_company(company_name, strategy, target_market, language='de'):
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scraping_note = "(No URL found)"
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tech_evidence = []
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known_incumbents = ["SAP Ariba", "Jaggaer", "Coupa", "SynerTrade", "Ivalua", "ServiceNow", "Salesforce", "Oracle SCM", "Zycus", "GEP", "SupplyOn", "EcoVadis", "IntegrityNext"]
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half = len(known_incumbents) // 2
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group1 = " OR ".join([f'"{inc}"' for inc in known_incumbents[:half]])
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group2 = " OR ".join([f'"{inc}"' for inc in known_incumbents[half:]])
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tech_queries = [f'"{company_name}" ({group1})', f'"{company_name}" ({group2})', f'"{company_name}" "supplier portal" login']
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# NEU: Dynamische Suche basierend auf Strategie statt Hardcoded Liste
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# Wir suchen NICHT mehr proaktiv nach SAP Ariba, es sei denn, es steht in der Strategie.
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# Stattdessen machen wir eine generische "Tech Stack"-Suche.
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tech_queries = [
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f'site:{url.split("//")[-1].split("/")[0] if url and "//" in url else company_name} "software" OR "technology" OR "system"',
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f'"{company_name}" "technology stack"',
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f'"{company_name}" "partners"'
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]
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# Add explicit tech signals from strategy if they exist
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signals = strategy.get('signals', [])
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for signal in signals:
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if "technographic" in signal.get('id', '').lower() or "incumbent" in signal.get('id', '').lower():
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keywords = signal.get('targetPageKeywords', [])
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for kw in keywords:
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tech_queries.append(f'"{company_name}" "{kw}"')
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# Deduplicate queries and limit
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tech_queries = list(set(tech_queries))[:4]
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for q in tech_queries:
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results = serp_search(q, num_results=4)
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results = serp_search(q, num_results=3)
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if results:
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for r in results:
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tech_evidence.append(f"- Found: {r['title']}\n Snippet: {r['snippet']}\n Link: {r['link']}")
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@@ -405,9 +420,10 @@ def analyze_company(company_name, strategy, target_market, language='de'):
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firmographics_results = serp_search(f"{company_name} Umsatz Mitarbeiterzahl 2023")
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firmographics_context = "\n".join([f"- {r['snippet']} ({r['link']})" for r in firmographics_results])
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signals = strategy.get('signals', [])
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for signal in signals:
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if "incumbent" in signal['id'].lower() or "tech" in signal['id'].lower(): continue
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# Skip technographic signals here as they are handled above or via generic search
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if "incumbent" in signal['id'].lower() or "technographic" in signal['id'].lower(): continue
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proof_strategy = signal.get('proofStrategy', {})
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query_template = proof_strategy.get('searchQueryTemplate')
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search_context = ""
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@@ -432,8 +448,8 @@ def analyze_company(company_name, strategy, target_market, language='de'):
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--- STRATEGY (What we are looking for) ---
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{json.dumps(signals, indent=2)}
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--- EVIDENCE 1: EXTERNAL TECH-STACK INTELLIGENCE (CRITICAL) ---
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Look closely here for mentions of competitors like SAP Ariba, Jaggaer, SynerTrade, Coupa, etc.
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--- EVIDENCE 1: EXTERNAL TECH-STACK INTELLIGENCE ---
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Analyze the search results below. Do NOT hallucinate technologies. Only list what is explicitly found.
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{tech_evidence_text}
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--- EVIDENCE 2: HOMEPAGE CONTENT {scraping_note} ---
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@@ -448,15 +464,16 @@ def analyze_company(company_name, strategy, target_market, language='de'):
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TASK:
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1. **Firmographics**: Estimate Revenue and Employees.
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2. **Technographic Audit**: Look for specific competitor software or legacy systems mentioned in EVIDENCE 1 (e.g., "Partner of SynerTrade", "Login to Jaggaer Portal").
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2. **Technographic Audit**: Check if any relevant competitor technology or legacy system is ACTUALLY found in the evidence.
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- **CRITICAL:** If no specific competitor software is found, assume the status is "Greenfield" (Manual Process / Status Quo). Do NOT invent a competitor like SAP Ariba just because it's a common tool.
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3. **Status**:
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- Set to "Nutzt Wettbewerber" if ANY competitor technology is found.
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- Set to "Greenfield" ONLY if absolutely no competitor tech is found.
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- Set to "Nutzt Wettbewerber" ONLY if a direct competitor is explicitly found.
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- Set to "Greenfield" if no competitor tech is found.
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- Set to "Bestandskunde" if they already use our solution.
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4. **Evaluate Signals**: For each signal, provide a "value" (Yes/No/Partial) and "proof".
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5. **Recommendation (Pitch Strategy)**:
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- If they use a competitor, explain how to position against it.
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- If Greenfield, explain the entry point.
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- If Greenfield: Pitch against the manual status quo (efficiency, error reduction).
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- If Competitor: Pitch replacement/upgrade.
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- **Tone**: Strategic, insider-knowledge, specific.
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--- LANGUAGE INSTRUCTION ---
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@@ -579,9 +596,14 @@ def generate_outreach_campaign(company_data_json, knowledge_base_content, refere
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{task_description}
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--- TONE & STYLE GUIDELINES (CRITICAL) ---
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- **Perspective:** Operational Expert & Insider. NOT generic marketing.
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- **Be Gritty & Specific:** Use hard, operational keywords from the Knowledge Base (e.g., "ASNs", "8D-Reports").
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- **Language:** {lang_instruction}.
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1. **Professional & Flowing:** Aim for approx. 500-600 characters per email. Use full sentences and professional courtesies. It should feel like a high-quality human message.
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2. **Stance:** Act as an **astute industry observer** and peer consultant. You have analyzed their specific situation and identified a strategic bottleneck.
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3. **The Opportunity Bridge (Email 1):** Bridge observation to a strategic solution immediately using concrete terms (e.g., "autonome Reinigungsrobotik").
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4. **Context-Sensitive Technographics:** Only mention discovered IT or Procurement systems (e.g., SAP Ariba) if it is highly relevant to the **specific role** (e.g., for CEO, CFO, or Head of Procurement). For **purely operational roles** (e.g., Facility Manager, Head of Operations), AVOID mentioning these systems as it may cause confusion; focus entirely on the operational pain (labor shortage) and growth bottlenecks instead.
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5. **Soft-Sell vs. Hard-Pitch:** Position technology as a logical answer to the bottleneck. Pitch the **outcome/capability**, not features.
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6. **Social Proof as the Engine:** Let the Reference Client ({reference_url}) provide the evidence. Use a role-specific KPI.
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7. **Operational Grit:** Use domain-specific terms (e.g., "ASNs", "8D", "TCO") to establish authority.
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8. **Language:** {lang_instruction}.
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{output_format}
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"""
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