AI in customer contact, without damaging your brand

Customers hate bad chatbots: bots that loop, do not understand questions, and refuse to hand off to a human. We build it differently. One: the bot lives off a real knowledge base (your FAQ, product documentation, policy rules) via RAG, so it does not invent answers. Two: as soon as the bot is unsure, it escalates directly to a human with the full conversation context. Three: we test every bot on hundreds of real questions before it goes live. Four: we keep training on actual conversations, not just on the FAQ. A good AI customer service is invisible when it works, and honest about its limits when it does not know something. Bad bots are a brand risk, so we do not build those.

Not every conversation belongs with AI, and that is exactly the point. We set escalation rules together with you. Standard: when the bot is not sure of the answer (low confidence score), when the customer is angry or disappointed (sentiment analysis), when it is a complex or non-standard question, when the customer explicitly asks for a human, or when it concerns compliance, financial or legal matters. The bot hands off the conversation with full context: what was already asked, which articles were offered, where the customer got stuck. Your colleague does not have to start over. The rule is often simple: AI handles 70-80 percent of the routine, humans pick up the matters that count.

Yes. AI Act article 50 requires customers to be informed when they are interacting with an AI system instead of a human. This applies to all chatbots, voice bots, and automated email replies where the average user would not realise it is AI. We arrange this as standard in every implementation: a brief disclosure at the start of the conversation (for example "Hi, I am the AI assistant for [company], happy to help and I will hand you over to a colleague if you prefer"), and visible branding (chatbot icon, naming). For voice AI a spoken disclosure comes in the first seconds. Not complicated, but mandatory.

Yes, that is usually the starting point. We integrate with Zendesk, Freshdesk, Intercom, HubSpot Service Hub, Salesforce Service Cloud and most other helpdesks. The AI sits on top of your existing system, it does not replace it. Concretely: tickets the AI handles are logged in your helpdesk including the full conversation. Tickets that escalate land automatically in the right queue with the relevant tags and context. Your reporting, SLA tracking, and team work distribution keep working as they were. For clients who do not yet have a helpdesk we advise getting that in order first: AI on top of chaos only amplifies the chaos.

Yes. For tourism, e-commerce, and B2B, multilingual is often the main reason to use AI. A good setup offers 24/7 support in NL, EN, DE, and FR (and other languages if needed) without having to build a team per language. The AI recognises the language automatically and replies in the same language, using the same knowledge base as source. Important: we do advise having native speakers available for escalations in the languages where you expect significant volume. For niche languages the escalation can run via translated text exchange. Works well for B2B, for tourism it is often sufficient.

That is exactly why we do not build on a generic chatbot but on RAG with your own knowledge base. The AI may only give answers based on sources you have supplied, not from "training data". If the source is unclear or incomplete, the bot refuses to answer and escalates. We also log all conversations and review weekly (in the beginning) or monthly (later) where the bot is wrong, where it hesitates, and where the knowledge base has gaps. Errors are fed back into the source: we add an article, sharpen an existing FAQ, or tighten the escalation rule. No "set and forget", but "set and improve".

We build measurement points in from day one. Standard: containment rate (what percentage of conversations does the bot handle independently), CSAT score after each conversation (one-click feedback: was this useful?), escalation rate per topic (where does the human take over), average conversation time, and first response time. We also do conversational analytics: which questions come back often, where does the bot get cut off, which topics generate the most frustration. That data is used to improve the bot and to show you where your products or processes are unclear (customer service data is gold product feedback). You get a monthly dashboard. No reporting on technical metrics that do not help you, just steering on the numbers that matter.