{
  "slug": "antwort-schleife",
  "category": "workflow",
  "name": "The Reply Loop",
  "domaene": "Kundensupport & Service",
  "typTags": [
    "automatisierend",
    "recherchierend",
    "schreibend"
  ],
  "teaser": "Searches the FAQ knowledge base for every new support ticket, drafts a personalized reply, and hands it to a human for review before it goes out.",
  "hat": {
    "schritte": true,
    "beispiel_szenario": false,
    "ausgabebeispiel": false,
    "konfiguration": false,
    "betrieb": true,
    "arbeitsprompts": true,
    "einrichtung": true,
    "umsetzung": false,
    "export": true,
    "staerken": false,
    "ki_funktionen": false,
    "einschraenkungen": false,
    "weniger_geeignet_fuer": false
  },
  "sections": [
    {
      "id": "description",
      "title": "Description",
      "html": "<p>Standard support requests cost time that is rarely proportionate to the request: a password reset, an invoice request, a delivery status check. The Reply Loop takes the first draft off your hands. As soon as a ticket comes in, the workflow searches the connected knowledge base for matching FAQ entries and drafts a personalized reply that addresses the customer by name and names concrete next steps.</p>\n<p>The draft lands in the ticket as an internal comment, not as a sent reply. The responsible person is notified in the team chat, reviews the draft, adjusts it if needed, and sends it. This keeps the decision with the human while the drafting work for recurring cases falls away. For requests the knowledge base has nothing on, the case stays entirely with the support person: a draft without a factual basis should not be generated automatically.</p>\n<ul>\n<li><strong>Draft in seconds:</strong> The personalized reply draft is ready right after the ticket comes in.</li>\n<li><strong>Consistent quality:</strong> Every reply follows the same tone and the approved FAQ content.</li>\n<li><strong>Agents freed up:</strong> Standard requests run automatically, so focus stays on the complex cases.</li>\n<li><strong>Knowledge base actively used:</strong> The FAQ gets used consistently, and gaps in it become visible.</li>\n</ul>\n"
    },
    {
      "id": "builder-prompt",
      "title": "Builder Prompt",
      "html": "<p>Copy the prompt below in full into your automation tool or agent builder. As a file: <a href=\"/ai-library/antwort-schleife.en.json\">antwort-schleife.en.json</a></p>\n"
    }
  ],
  "schritte": [
    {
      "nr": 1,
      "titel": "Trigger: new ticket",
      "beschreibung": "A new support ticket arrives via webhook or integration with the ticketing system.",
      "rolle": "mensch"
    },
    {
      "nr": 2,
      "titel": "Retrieve ticket",
      "beschreibung": "Subject, message, requester name and further metadata are loaded.",
      "rolle": "automatisch"
    },
    {
      "nr": 3,
      "titel": "Search FAQ",
      "beschreibung": "The knowledge base is searched using full text or semantic search, and the most matching entries are extracted.",
      "rolle": "automatisch"
    },
    {
      "nr": 4,
      "titel": "Draft reply",
      "beschreibung": "A personalized reply draft is created from the ticket content and FAQ matches, with a salutation, matching tone and concrete next steps.",
      "rolle": "automatisch"
    },
    {
      "nr": 5,
      "titel": "Save draft in ticket",
      "beschreibung": "The draft is stored as an internal comment or draft in the ticket, ready for review.",
      "rolle": "automatisch"
    },
    {
      "nr": 6,
      "titel": "Notify agent",
      "beschreibung": "A message in Slack, Teams or a comparable channel informs the agent, with a ticket link and a note about the draft that is ready.",
      "rolle": "ergebnis"
    }
  ],
  "herausgeber": "Voyage Digital",
  "version": "2.0",
  "stand": "2026-07-26",
  "nutzen": [
    {
      "titel": "Draft in seconds",
      "text": "The personalized reply draft is ready right after the ticket comes in."
    },
    {
      "titel": "Consistent quality",
      "text": "Every reply follows the same tone and the approved FAQ content."
    },
    {
      "titel": "Agents freed up",
      "text": "Standard requests run automatically, so focus stays on the complex cases."
    },
    {
      "titel": "Knowledge base actively used",
      "text": "The FAQ gets used consistently, and gaps in it become visible."
    }
  ],
  "voraussetzungen": [
    {
      "label": "Ticketing system",
      "text": "Zendesk, Freshdesk or Intercom, with API access and write permissions.",
      "icon": "support_agent"
    },
    {
      "label": "Knowledge base",
      "text": "Notion, Confluence or GitBook with the maintained FAQ content.",
      "icon": "menu_book"
    },
    {
      "label": "Team chat",
      "text": "Slack, Teams or Discord for the note to the responsible person.",
      "icon": "notifications"
    },
    {
      "label": "LLM access",
      "text": "API access to OpenAI, Claude or Gemini.",
      "icon": "key"
    }
  ],
  "betrieb": {
    "testing": [
      {
        "label": "Test ticket",
        "text": "Set up a typical request, for example \"Password reset?\".",
        "icon": "confirmation_number"
      },
      {
        "label": "FAQ search",
        "text": "Check whether matching entries are found for the request.",
        "icon": "search"
      },
      {
        "label": "Reply quality",
        "text": "Assess the draft for personalization, matching tone and concrete solution steps.",
        "icon": "fact_check"
      },
      {
        "label": "Edge cases",
        "text": "Check the behavior without a matching FAQ entry, with multilingual requests, and with very long or very short requests.",
        "icon": "rule"
      }
    ],
    "goLive": [
      {
        "label": "Approval to start",
        "text": "Activate the workflow only once search, drafting and storage run cleanly.",
        "icon": "check_circle"
      },
      {
        "label": "Pilot",
        "text": "Start with a single product area or ticket queue.",
        "icon": "playlist_add_check"
      },
      {
        "label": "Maintain the knowledge base",
        "text": "Add FAQ entries on an ongoing basis and update outdated content.",
        "icon": "edit_note"
      },
      {
        "label": "Refine tone",
        "text": "Fine tune the tone in the prompt.",
        "icon": "record_voice_over"
      }
    ],
    "haeufigeFehler": [
      {
        "label": "No FAQ matches",
        "text": "Switch to semantic search instead of exact keyword matches.",
        "icon": "search_off"
      },
      {
        "label": "Draft not saved",
        "text": "Check the write permissions for comments in the ticketing system.",
        "icon": "lock"
      },
      {
        "label": "Notification not received",
        "text": "Check the channel ID or webhook.",
        "icon": "notifications_off"
      },
      {
        "label": "Wrong salutation",
        "text": "Validate the name extraction and set a fallback such as \"Hello there\".",
        "icon": "badge"
      }
    ]
  },
  "arbeitsprompts": [
    {
      "titel": "FAQ reply generator",
      "beschreibung": "The prompt for the AI step that generates the reply draft from the ticket content and FAQ matches. Usable on its own, also outside the full workflow specification.",
      "prompt": "You are a professional support agent for [COMPANY]. Analyze the following customer inquiry and create a helpful, friendly reply draft.\n\nCustomer inquiry:\nSubject: [TICKET_SUBJECT]\nMessage: [TICKET_MESSAGE]\nRequester name: [CUSTOMER_NAME]\n\nRelevant FAQ entries:\n[FAQ_RESULTS]\n\nTask:\nCreate a reply draft that\n1. addresses the inquiry directly and calls the requester by name,\n2. uses the information from the FAQ entries but tailors it to the specific situation,\n3. is written in the tone [TONE], for example professional and friendly, casual or formal,\n4. names concrete next steps or solutions,\n5. includes a matching closing and signature.\n\nFormat:\nOutput only the finished reply draft, without additional explanations or meta commentary."
    }
  ],
  "prompt": "GOAL\nBuild a workflow that automatically searches the FAQ knowledge base for every new support ticket, creates a personalized reply draft from it, and places it in the ticket for the responsible agent to review. No automatic dispatch to the customer.\n\nAdapt the structure to the concepts of your automation tool, for example nodes, steps, zaps or agent actions. While doing so, preserve the trigger logic, the sequence and the data flow.\n\nTRIGGER\nType: webhook or direct integration with the ticketing system (Zendesk, Freshdesk or Intercom), fires on every new ticket.\n\nSTEPS\n1. Load ticket data: subject, message, requester name and metadata such as queue and assigned agent.\n2. Search the FAQ: query the knowledge base using the core terms of the request, preferring semantic search over exact keyword matches. Collect the most matching entries together with content and links.\n3. AI step: create the reply draft, using the prompt below exactly.\n4. Save the draft: as an internal comment or draft in the ticket, no automatic dispatch to the customer.\n5. Output: notify the responsible agent in Slack or Teams, with a ticket link and a note about the draft that is ready.\n\nPROMPT (use exactly as written)\nFAQ reply generator:\n\"\"\"\nYou are a professional support agent for [COMPANY]. Analyze the following customer inquiry and create a helpful, friendly reply draft.\n\nCustomer inquiry:\nSubject: [TICKET_SUBJECT]\nMessage: [TICKET_MESSAGE]\nRequester name: [CUSTOMER_NAME]\n\nRelevant FAQ entries:\n[FAQ_RESULTS]\n\nTask:\nCreate a reply draft that\n1. addresses the inquiry directly and calls the requester by name,\n2. uses the information from the FAQ entries but tailors it to the specific situation,\n3. is written in the tone [TONE], for example professional and friendly, casual or formal,\n4. names concrete next steps or solutions,\n5. includes a matching closing and signature.\n\nFormat:\nOutput only the finished reply draft, without additional explanations or meta commentary.\n\"\"\"\n\nDATA FLOW\nStep 1 to step 3: subject, message and requester name.\nStep 2 to step 3: the most matching FAQ entries.\nStep 3 to step 4: the finished reply draft.\nTrigger and step 1 to step 5: assigned agent and ticket link.\n\nERROR HANDLING\nNo FAQ matches: create the draft anyway, but flag it clearly as without an FAQ basis so the agent can decide. This also signals a gap in the knowledge base.\nRequester name not extractable: use a neutral fallback, for example \"Hello there\", instead of an incorrect salutation.\nDraft not saved: check the write permissions for comments and drafts in the ticketing system.\nNotification not received: check the channel ID or webhook.\nRequest in a different language: create the draft in the language of the request.\n\nPLACEHOLDER CONVENTION\nAll placeholders in square brackets are variables. Mapped dynamically are [TICKET_SUBJECT], [TICKET_MESSAGE], [CUSTOMER_NAME] and [FAQ_RESULTS], as described in the data flow. Not dynamic are [COMPANY] and [TONE]: set these manually before activation. List all placeholders that require manual configuration separately at the end.\n\nSELF CHECK BEFORE COMPLETION\nCheck, before you report the workflow as finished:\n1. Are all steps connected correctly, and is the draft stored exclusively internally, without being sent to the customer?\n2. Is the prompt inserted in full and unchanged, and are all dynamic placeholders mapped?\n3. Does the FAQ search use semantic matching, and are the matches passed to the prompt correctly?\n4. Are all points listed that require manual configuration: the ticketing system webhook and write permissions, access to the knowledge base, the notification channel, [COMPANY], [TONE] and the LLM access?",
  "einrichtung": {
    "intro": "Two routes lead to the same result. Pick the one that fits your setup.",
    "varianten": [
      {
        "variante": "Builder-Prompt",
        "einleitung": "Suitable when you want to build the workflow in your own automation tool.",
        "schritte": [
          "Open your workflow tool, for example n8n, Make, Zapier or Langdock.",
          "Create a new workflow and give it a name.",
          "Choose the AI-assisted build option if your tool offers one.",
          "Paste the builder prompt from above in full.",
          "Check the result against the step sequence in this document, then add your access credentials and channels."
        ]
      },
      {
        "variante": "JSON-Import",
        "einleitung": "Suitable when you want to take the workflow over unchanged.",
        "schritte": [
          "Download the JSON file from the JSON export section.",
          "Open the workflow area of your tool and create a new workflow.",
          "Give it a name and confirm.",
          "Open the menu on the workflow name and choose to import a JSON file.",
          "Upload the file, then add your own access credentials.",
          "Run a test before you activate the workflow."
        ]
      }
    ]
  },
  "exportMeta": {
    "datei": "w07_antwort-schleife__workflow.json",
    "format": "voyage-workflow/1"
  },
  "download": {
    "schema": "voyage-workflow/1",
    "name": "Die Antwort-Schleife",
    "beschreibung": "Durchsucht bei jedem neuen Support-Ticket automatisch die FAQ-Wissensbasis, erstellt daraus einen personalisierten Antwort-Entwurf und legt ihn dem zuständigen Agenten zur Prüfung ins Ticket. Kein automatischer Versand an den Kunden.",
    "trigger": {
      "typ": "Webhook",
      "bedingung": "Der Workflow startet automatisch, sobald im angebundenen Ticket-System ein neues Support-Ticket angelegt wird. Der Webhook liefert mindestens die Ticket-ID, den Betreff, die Beschreibung und die Kontaktdaten der anfragenden Person (Felder im Quellsystem: ticket_id, subject, description, requester). In der Referenzimplementierung ist das eine Zendesk-Verbindung, andere Ticket-Systeme wie Freshdesk oder Intercom senden vergleichbar aufgebaute Payloads."
    },
    "schritte": [
      {
        "nr": 1,
        "titel": "Trigger: neues Ticket",
        "rolle": "mensch",
        "aktion": "Ein neues Support-Ticket wird im angebundenen Ticket-System angelegt und löst per Webhook automatisch den Workflow aus. Übergeben werden die Ticket-ID sowie Betreff, Beschreibung und Kontaktdaten der anfragenden Person.",
        "prompt_ref": null,
        "eingang": [],
        "ausgang": [
          "Ticket-ID (ticket_id)",
          "Betreff (subject)",
          "Beschreibung (description)",
          "Kontaktdaten der anfragenden Person (requester)"
        ]
      },
      {
        "nr": 2,
        "titel": "Ticket abrufen",
        "rolle": "automatisch",
        "aktion": "Mit der Ticket-ID (Feld ticketId) werden die vollständigen Ticket-Daten nachgeladen: Betreff, Beschreibung, Kontaktdaten der anfragenden Person (Name, E-Mail), der komplette Konversationsverlauf, Status, Priorität, Tags und Anhänge.",
        "prompt_ref": null,
        "eingang": [
          "Ticket-ID (ticket_id)"
        ],
        "ausgang": [
          "Vollständige Ticket-Daten (Betreff, Beschreibung, Konversationsverlauf, Kontaktdaten, Status, Priorität, Tags, Anhänge)"
        ]
      },
      {
        "nr": 3,
        "titel": "FAQ durchsuchen",
        "rolle": "automatisch",
        "aktion": "Aus Betreff und Beschreibung des Tickets wird eine Suchanfrage an den hinterlegten Wissens-Ordner gestellt. Die Suche zielt auf semantische Ähnlichkeit statt auf reinen Keyword-Abgleich und liefert standardmäßig die zehn relevantesten FAQ-Einträge als Text-Ausschnitte zurück. Die Trefferzahl lässt sich zwischen eins und fünfzig einstellen.",
        "prompt_ref": null,
        "eingang": [
          "Betreff und Beschreibung des Tickets"
        ],
        "ausgang": [
          "Passende FAQ-Einträge als Text-Ausschnitte"
        ]
      },
      {
        "nr": 4,
        "titel": "Entwurf erstellen",
        "rolle": "automatisch",
        "aktion": "Ein KI-Agent verarbeitet die FAQ-Treffer zusammen mit den Ticket-Daten aus Schritt 2 über den Arbeitsprompt und liefert ein strukturiertes Ergebnis mit drei Feldern: response_draft (der Antwort-Entwurf als Text), success (ob die Erstellung erfolgreich war) und confidence (die Sicherheitseinschätzung des Agenten zur Qualität der Antwort).",
        "prompt_ref": "arbeitsprompt-1",
        "eingang": [
          "Vollständige Ticket-Daten",
          "Passende FAQ-Einträge als Text-Ausschnitte"
        ],
        "ausgang": [
          "response_draft (Antwort-Entwurf)",
          "success (Erfolgsstatus)",
          "confidence (Sicherheitseinschätzung)"
        ]
      },
      {
        "nr": 5,
        "titel": "Entwurf im Ticket speichern",
        "rolle": "automatisch",
        "aktion": "Der Antwort-Entwurf (response_draft) wird über das Feld comment als interner Kommentar im Ticket gespeichert. Das Feld isPublic ist fest auf false gesetzt, sodass die anfragende Person den Entwurf nicht sieht. Die Zuordnung erfolgt über die Ticket-ID (ticketId).",
        "prompt_ref": null,
        "eingang": [
          "response_draft (Antwort-Entwurf)",
          "Ticket-ID (ticketId)"
        ],
        "ausgang": [
          "Gespeicherter interner Kommentar im Ticket"
        ]
      },
      {
        "nr": 6,
        "titel": "Agent benachrichtigen",
        "rolle": "ergebnis",
        "aktion": "Eine Nachricht geht an den Team-Chat-Kanal des Supports (Feld channelId). Der Nachrichtentext (Feld text) verweist auf die Ticket-Daten aus Schritt 2, nennt die Sicherheitseinschätzung (confidence) aus Schritt 4 und fordert die zuständige Person auf, den gespeicherten Entwurf zu prüfen und zu versenden.",
        "prompt_ref": null,
        "eingang": [
          "Vollständige Ticket-Daten",
          "confidence (Sicherheitseinschätzung)",
          "Gespeicherter interner Kommentar im Ticket"
        ],
        "ausgang": [
          "Benachrichtigung im Team-Chat"
        ]
      }
    ],
    "prompts": {
      "arbeitsprompt-1": {
        "titel": "FAQ-Antwort-Generator",
        "text": "Sie sind ein professioneller Support-Agent für [UNTERNEHMEN]. Analysieren Sie das folgende Kundenanliegen und erstellen Sie einen hilfreichen, freundlichen Antwort-Entwurf.\n\nKundenanfrage:\nBetreff: [TICKET_SUBJECT]\nNachricht: [TICKET_MESSAGE]\nName der anfragenden Person: [CUSTOMER_NAME]\n\nRelevante FAQ-Einträge:\n[FAQ_RESULTS]\n\nAufgabe:\nErstellen Sie einen Antwort-Entwurf, der\n1. die Kundenanfrage direkt adressiert und die anfragende Person namentlich anspricht,\n2. die Informationen aus den FAQ-Einträgen nutzt, aber auf die konkrete Situation zuschneidet,\n3. im Ton [TONALITÄT] geschrieben ist, zum Beispiel professionell freundlich, locker oder förmlich,\n4. konkrete nächste Schritte oder Lösungen aufzeigt,\n5. eine passende Grußformel und Signatur enthält.\n\nFormat:\nGeben Sie ausschließlich den fertigen Antwort-Entwurf aus, ohne zusätzliche Erklärungen oder Metakommentare."
      }
    },
    "datenfluss": [
      {
        "von": 1,
        "nach": 2,
        "nutzlast": "Ticket-ID (Feld ticketId aus dem Webhook-Payload)"
      },
      {
        "von": 2,
        "nach": 3,
        "nutzlast": "Betreff und Beschreibung des Tickets als Grundlage der Suchanfrage an den Wissens-Ordner"
      },
      {
        "von": 3,
        "nach": 4,
        "nutzlast": "FAQ-Treffer als Text-Ausschnitte. Der Agent-Knoten referenziert daneben direkt die Ticket-Daten aus Schritt 2 (Betreff, Beschreibung, Kontaktdaten), um die Platzhalter des Arbeitsprompts zu füllen"
      },
      {
        "von": 4,
        "nach": 5,
        "nutzlast": "response_draft wird in das Feld comment übernommen, isPublic bleibt fest auf false, dazu die Ticket-ID (ticketId)"
      },
      {
        "von": 5,
        "nach": 6,
        "nutzlast": "Bestätigung des gespeicherten internen Kommentars. Der Benachrichtigungstext referenziert zusätzlich direkt die Ticket-Daten aus Schritt 2 und die Sicherheitseinschätzung confidence aus Schritt 4"
      }
    ],
    "voraussetzungen": [
      "Ticket-System mit API-Zugang und Schreibrechten, zum Beispiel Zendesk, Freshdesk oder Intercom",
      "Wissensbasis mit gepflegten FAQ-Inhalten in einem indexierten Wissens-Ordner, zum Beispiel Notion, Confluence oder GitBook",
      "Team-Chat für die Benachrichtigung, zum Beispiel Slack, Teams oder Discord",
      "LLM-Zugang per API-Key, zum Beispiel OpenAI, Claude oder Gemini"
    ],
    "fehlerbehandlung": [
      {
        "fall": "Fehler in einem der Workflow-Knoten",
        "massnahme": "Alle Knoten sind auf die Fehlerstrategie stop gesetzt. Der Workflow bricht bei einem Fehler vollständig ab, ohne automatischen Retry und ohne Fallback-Pfad. Der Lauf muss manuell im Automations-Tool geprüft und neu gestartet werden."
      },
      {
        "fall": "Keine passenden FAQ-Treffer",
        "massnahme": "Semantische statt reine Keyword-Suche sicherstellen, den Wissens-Ordner auf vollständige Indexierung prüfen und bei Bedarf die maximale Trefferzahl erhöhen. Der Agent erstellt den Entwurf laut Konfiguration trotzdem, sollte ihn aber über eine niedrige confidence als unsicher kennzeichnen, damit die zuständige Person entscheidet."
      },
      {
        "fall": "Name der anfragenden Person nicht extrahierbar",
        "massnahme": "Neutralen Fallback verwenden, zum Beispiel Hallo zusammen, statt einer falschen Anrede. Das Feld requester aus dem Webhook-Payload auf Vollständigkeit prüfen."
      },
      {
        "fall": "Entwurf wird nicht gespeichert",
        "massnahme": "Schreibrechte für Kommentare im Ticket-System prüfen sowie die korrekte Zuordnung über das Feld ticketId."
      },
      {
        "fall": "Benachrichtigung kommt nicht an",
        "massnahme": "Die Kanal-ID im Feld channelId sowie die Verbindung zum Team-Chat prüfen."
      },
      {
        "fall": "Anfrage in einer anderen Sprache",
        "massnahme": "Entwurf in der Sprache der Anfrage erstellen."
      }
    ],
    "platzhalter": [
      {
        "name": "ticket_id",
        "quelle": "Webhook-Payload-Feld: Ticket-ID bei Ticket-Erstellung"
      },
      {
        "name": "subject",
        "quelle": "Webhook-Payload-Feld: Betreff des Tickets"
      },
      {
        "name": "description",
        "quelle": "Webhook-Payload-Feld: Beschreibung beziehungsweise Nachricht des Tickets"
      },
      {
        "name": "requester",
        "quelle": "Webhook-Payload-Feld: Kontaktdaten der anfragenden Person"
      },
      {
        "name": "ticketId",
        "quelle": "Eingabefeld der Knoten Ticket abrufen und Entwurf speichern, referenziert die Ticket-ID im weiteren Ablauf"
      },
      {
        "name": "comment",
        "quelle": "Eingabefeld des Speicher-Knotens, nimmt response_draft auf"
      },
      {
        "name": "isPublic",
        "quelle": "Eingabefeld des Speicher-Knotens, fest auf false für einen internen statt öffentlichen Kommentar"
      },
      {
        "name": "response_draft",
        "quelle": "Ausgabefeld des Agent-Knotens: der fertige Antwort-Entwurf als Text"
      },
      {
        "name": "success",
        "quelle": "Ausgabefeld des Agent-Knotens: Erfolgsstatus der Entwurfserstellung"
      },
      {
        "name": "confidence",
        "quelle": "Ausgabefeld des Agent-Knotens: Sicherheitseinschätzung, wird im Benachrichtigungstext zitiert"
      },
      {
        "name": "text",
        "quelle": "Eingabefeld des Benachrichtigungs-Knotens: Nachrichtentext für den Team-Chat"
      },
      {
        "name": "channelId",
        "quelle": "Eingabefeld des Benachrichtigungs-Knotens: Ziel-Kanal des Team-Chats"
      },
      {
        "name": "TICKET_SUBJECT",
        "quelle": "Betreff aus den Ticket-Daten (Schritt 2)"
      },
      {
        "name": "TICKET_MESSAGE",
        "quelle": "Beschreibung beziehungsweise Nachricht aus den Ticket-Daten (Schritt 2)"
      },
      {
        "name": "CUSTOMER_NAME",
        "quelle": "Name aus den Kontaktdaten der anfragenden Person (Schritt 2)"
      },
      {
        "name": "FAQ_RESULTS",
        "quelle": "Passende Treffer aus der FAQ-Suche (Schritt 3)"
      },
      {
        "name": "UNTERNEHMEN",
        "quelle": "Manuell vor Aktivierung festzulegen: Name des Unternehmens"
      },
      {
        "name": "TONALITÄT",
        "quelle": "Manuell vor Aktivierung festzulegen: gewünschter Ton der Antwort"
      }
    ],
    "herausgeber": "Voyage Digital",
    "version": "2.0",
    "stand": "2026-07-26"
  },
  "itemIcon": "reply",
  "recommended": [
    {
      "slug": "ticket-schiene",
      "category": "workflow",
      "name": "The Ticket Pipeline",
      "teaser": "The Ticket Pipeline automatically categorizes new IT support tickets, searches the knowledge base for matching solutions, and routes them to the responsible team for a final human check before a reply goes out.",
      "domaene": "Kundensupport & Service",
      "itemIcon": "ticket"
    },
    {
      "slug": "selfservice-staffel",
      "category": "workflow",
      "name": "The Self-Service Relay",
      "teaser": "Automatically analyzes incoming customer requests, searches the FAQ database for matching articles, and decides based on the solution probability: a self-service reply to the customer or a support ticket with full context to the team.",
      "domaene": "Kundensupport & Service",
      "itemIcon": "life-buoy"
    },
    {
      "slug": "ticket-route",
      "category": "workflow",
      "name": "The Ticket Router",
      "teaser": "A workflow that automatically sorts incoming support tickets by category and urgency, assigns them to the right team, and prepares a personalized first reply.",
      "domaene": "Kundensupport & Service",
      "itemIcon": "route"
    },
    {
      "slug": "faq-kurator",
      "category": "assistent",
      "name": "The FAQ Curator",
      "teaser": "Answers recurring HR, IT, and customer service questions from your stored FAQ documents, cites the source for every answer, and escalates cleanly to a human whenever the answer is not covered there.",
      "domaene": "Kundensupport & Service",
      "itemIcon": "help-circle"
    },
    {
      "slug": "feedback-route",
      "category": "workflow",
      "name": "The Feedback Route",
      "teaser": "A daily automated report that sorts incoming customer feedback by sentiment and topic and immediately flags critical cases to the team.",
      "domaene": "Kundensupport & Service",
      "itemIcon": "message-square-quote"
    },
    {
      "slug": "cognigy-ai",
      "category": "tool",
      "name": "Cognigy AI",
      "teaser": "Düsseldorf based enterprise platform for conversational AI, with multilingual voice and chat agents for customer service in regulated industries.",
      "domaene": "Kundensupport & Service",
      "logoFile": "cognigy-ai.svg",
      "logoEinzug": 1
    },
    {
      "slug": "octonomy",
      "category": "tool",
      "name": "Octonomy",
      "teaser": "Octonomy is a platform for autonomous support agents that handles customer inquiries in e-commerce and SaaS independently and escalates complex cases to a human team.",
      "domaene": "Kundensupport & Service",
      "logoFile": "octonomy.png",
      "logoEinzug": 0.78
    },
    {
      "slug": "parloa",
      "category": "tool",
      "name": "Parloa",
      "teaser": "Parloa is a German voice AI platform that gives enterprise customer service teams in regulated industries phone and chat agents with deep CRM and ERP integration.",
      "domaene": "Kundensupport & Service",
      "logoFile": "parloa.png",
      "logoEinzug": 0.78
    },
    {
      "slug": "zendesk-ai-agents",
      "category": "tool",
      "name": "Zendesk AI Agents",
      "teaser": "Zendesk AI Agents are Zendesk's native AI agents for customer service, resolving tickets across email, chat, voice and social directly within Zendesk's existing tickets, workflows and knowledge base.",
      "domaene": "Kundensupport & Service",
      "logoFile": "zendesk-ai-agents.svg",
      "logoEinzug": 1
    },
    {
      "slug": "compliance-regelwerk",
      "category": "skill",
      "name": "The Compliance Rulebook",
      "teaser": "The Compliance Rulebook turns regulatory requirements into a structured, plain-language compliance policy complete with rules, responsibilities and reporting channels, ready for legal review before it takes effect.",
      "domaene": "Recht & Compliance",
      "itemIcon": "gavel"
    }
  ]
}