An AI Chatbot for Your Business: What It Actually Does

An AI chatbot answers customer questions on your website at any hour using your own documents, and when connected to your tools it can also qualify leads or book appointments. It handles repetitive questions well, it still makes mistakes, and in the EU it must tell people they are talking to an AI unless that is obvious.

What an AI chatbot can do for you

• Answer frequent questions at any hour: lead times, prices, conditions, processes.
• Qualify prospects before they contact you: need, budget, urgency.
• Book appointments through a calendar integration.
• Guide visitors to the right product or service for their situation.
• Handle simple support requests without a person.
• Collect structured information through a conversation rather than a form.

Three types of AI chatbot

The FAQ chatbot

It answers from your documentation: FAQ, pricing policy, terms and product guides. When a customer asks a question, it looks in that material and phrases an answer. It is the simplest to deploy and the most common.

The transactional chatbot

It can act: book an appointment, create a quote, check an order. It is connected to your CRM, booking system or other tools through APIs. It takes more work to build and more care to test.

The full conversational assistant

It combines both, keeps context during a conversation and hands over to a person when it should. It suits complex customer service and B2B platforms.

RAG: why chatbots should answer from your documents

RAG stands for retrieval-augmented generation. The chatbot first searches your documents for relevant passages, then writes its answer from them instead of from the language model's general memory.

This matters because language models can produce plausible but false answers. Grounding the answer in your documents, and instructing the bot to say when it cannot find the information, reduces that risk. It does not remove it, so important answers still need testing and a route to a human.

Where it helps

E-commerce and retail

A shop chatbot can answer questions about delivery times, returns and stock, and point buyers to the right product. Answering a doubt at the right moment can help a purchase, but measure it on your own shop rather than assume it.

Professional services

Imagine a law firm that gets the same questions every week about fees, response times and the types of case it takes. A chatbot can answer those, filter out enquiries outside the practice and propose an appointment for the rest, which leaves the team more time for clients. Anything involving advice on a specific case should go to a person.

B2B and business services

A qualification chatbot can ask about company size, timeline and main problem, then send the answers to your CRM so your sales team can prioritise.

Integration with your existing tools

An isolated chatbot has limited value. Typical connections are:

• CRM (HubSpot, Pipedrive, Salesforce): create contacts and qualified leads.
• Calendar (Google Calendar, Calendly): book appointments inside the conversation.
• Helpdesk (Zendesk, Freshdesk): create tickets with the right priority.
• E-commerce (Shopify, WooCommerce): look up orders and delivery status.
• Internal database: product information, prices and stock.

Metrics worth tracking

A chatbot without reporting is a black box. These are the figures to follow:

• Resolution rate: share of conversations solved without a person.
• Escalation rate: conversations handed to a person. A high rate suggests the bot lacks information.
• Unanswered questions: topics to add to the knowledge base.
• User satisfaction: a rating at the end of the conversation.
• Leads created: qualified prospects that reached your CRM.

What it costs

There are two parts: building and setting up the chatbot, and running it. Setup depends on how many documents it must learn and how many tools it connects to. Running costs include the usage fees charged by the AI model provider, which grow with the number of conversations, plus hosting and maintenance. Ask for both figures in a quote, and ask how usage fees are capped.

A way to judge the return: if your team handles 200 repetitive requests a week at 15 minutes each, that is 50 hours. If a chatbot resolved 70% of them, which you should treat as an assumption to test, it would free about 35 hours a week.

GDPR and the EU AI Act

Conversations often contain personal data, so the GDPR applies: tell visitors what you collect and why, sign a data processing agreement with the chatbot and AI model providers, and check where the data is processed, including any transfer outside the EU.

The EU AI Act adds a transparency rule. Under Article 50(1), AI systems intended to interact directly with people must be designed so that people are informed they are interacting with an AI system, unless that is obvious to a reasonably well-informed person given the context. These transparency rules apply from August 2026. A clear label such as "AI assistant" in the chat window is the simple way to meet this. This is general information, not legal advice.

The limit to know

An AI chatbot is not infallible. It can misread a complex question or answer imprecisely on a topic it knows poorly. Treat it as a good first filter, not a replacement for people, and let complex cases reach a person.

At WCS, we build chatbots connected to your documents and tools, with reporting so you can see what it answers and where it fails.