AI chatbots have evolved. We're far from the rigid decision trees of the 2010s. With modern LLMs, a chatbot can understand context, respond with nuance, and automate real tasks. Here's what that concretely changes for a business.
What an AI chatbot can do for you
- —Answer FAQs 24/7, turnaround times, pricing, conditions, processes.
- —Qualify prospects before they contact you, budget, need, urgency.
- —Book appointments automatically via calendar integration.
- —Guide visitors to the right product or service based on their situation.
- —Handle simple customer support requests without human intervention.
- —Collect structured information (conversational forms) in a natural way.
The three types of AI chatbots to know
The FAQ chatbot (the most common)
It's trained on your documentation: FAQ, pricing policy, terms and conditions, product guide. When a customer asks a question, it searches your knowledge base and formulates a response. Simple to deploy, very effective at reducing support ticket volume.
The transactional chatbot
It can perform actions: book an appointment, create a quote, place an order, check delivery status. It's connected to your business tools via API, CRM, ERP, booking system. More complex to develop, but with an immediate and measurable ROI.
The full conversational assistant
It combines both: answers questions AND performs actions. It manages long conversations, remembers context within a session, and knows when to escalate to a human. This is the most advanced form, suited to complex customer service operations or B2B platforms.
RAG: why it's the key
RAG stands for Retrieval-Augmented Generation. In plain terms: the chatbot searches your document base first, before formulating a response. It doesn't generate content from its own knowledge, it retrieves relevant information from your documents and reformulates it.
Why does this matter? Because LLMs without RAG "hallucinate", they invent plausible but false information. With RAG, if the answer isn't in your documents, the chatbot says it doesn't know rather than making something up. That's the difference between a reliable assistant and a dangerous one.
Use cases by sector
E-commerce & retail
An e-commerce chatbot can answer questions on delivery times, return policies, stock availability, and guide the buyer towards the right product based on their criteria. It significantly reduces cart abandonment by addressing doubts at the right moment, before the customer clicks away.
Professional services (lawyers, doctors, accountants)
A law firm receives 30 calls a week for questions that always have the same answer: fees, response times, types of cases handled. An AI chatbot on their site answers these in 30 seconds, filters cases that don't match their practice, and books appointments for the rest. Result: fewer interruptions, more qualified clients.
B2B & business services
A lead qualification chatbot can collect: company size, estimated budget, timeline, main pain point. This information is sent directly to your CRM. Your sales team stops wasting time on unqualified leads, they only call prospects that match your ideal customer profile.
Integration with your existing tools
An isolated AI chatbot has limited value. Its real power comes from its integrations. Here's what we typically connect:
- —CRM (HubSpot, Pipedrive, Salesforce), automatic creation of qualified contacts and leads.
- —Calendar (Google Calendar, Calendly), appointment booking directly within the conversation.
- —Helpdesk (Zendesk, Freshdesk), ticket creation with the right priority level.
- —E-commerce (Shopify, WooCommerce), order lookup, delivery status checks.
- —Internal database, real-time access to product information, pricing, stock levels.
Metrics to track
An AI chatbot without an analytics dashboard is a black box. We systematically instrument these metrics to let you optimise continuously:
- —Resolution rate, percentage of conversations resolved without human intervention.
- —Escalation rate, conversations transferred to an agent (too high = poorly trained chatbot).
- —Unanswered questions, topics the chatbot admits not knowing (to be added to the knowledge base).
- —User satisfaction, rating given at the end of the conversation.
- —Leads generated, qualified prospects created in the CRM through the chatbot.
What it actually costs
A custom AI chatbot, trained on your data and integrated into your site, typically costs between €1,500 and €5,000 depending on integration complexity. Solutions involving CRM and calendar can reach €8,000. On top of that, a monthly API cost (OpenAI or Anthropic): between €50 and €300/month depending on conversation volume.
It's an investment that pays off quickly if you handle a significant volume of repetitive enquiries. A support team managing 200 requests per week at 15 minutes each represents 50 hours of human work. A chatbot that handles 70% automatically frees up 35 hours per week, the equivalent of a half-time position.
The limitation to know
An AI chatbot isn't infallible. It can make mistakes, misinterpret a complex question, or give an imprecise answer on a topic it doesn't know well. Think of it as an effective first filter, not a complete replacement for human interaction. Complex cases should always escalate to a person.
At WCS, we build AI chatbots connected to your database, FAQ, and existing tools. Not a generic copy-paste widget, an assistant built specifically for your business, with a dashboard to track its performance and improve it over time.