AI solution
AI customer support agent: answering 24/7, without making things up
An AI agent that handles your support requests around the clock, answers from your sources and hands off to a human when it should. For support and service desks that overflow at night, on weekends and during peaks.
A chatbot replies, an agent acts
A standard chatbot recites a script and stalls the moment a question falls outside it. An agent acts: it reads your tools, understands the request, fetches the right information, performs an action (check an order, open a ticket, update a record) and knows when to stop and hand off to a human.
That difference is what separates a gadget from real backup for your support team. What I build is an agent, not a dressed-up FAQ.
The problem: support overflows when you are away
Your customers write in the evening, on weekends and during seasonal peaks, exactly when nobody is there to answer. Response times stretch, the ticket queue grows, and Monday morning already starts behind.
Most of those requests are repetitive: where is my order, how do I return an item, where do I find my invoice. Your human agents spend their days on them instead of the cases that truly deserve their attention.
What the agent actually does
The agent takes the first line, at any hour, with no queue.
- It handles routine requests end to end, without a rigid script.
- It answers from its sources: every reply is grounded in your documentation, terms and procedures, not in what the model thinks it knows.
- It acts in your tools via MCP: look up an order, create or update a ticket, trigger a refund within the limits you set.
- It escalates to a human as soon as a request is sensitive, ambiguous or out of scope, with all the context already gathered.
Guardrails, designed before the agent
A support agent that invents an answer or promises a refund that does not exist costs more than the problem it was meant to solve. Guardrails are not an add-on bolted on at the end; they are designed from the start.
- No hallucination tolerated: outside its knowledge base, the agent does not guess, it escalates or points to the right resource.
- Human escalation by default on sensitive cases: disputes, complaints, anything touching money or contracts.
- Permissions respected: the agent only accesses the data of the customer at hand and only performs the actions you have authorised, within the limits you set.
It plugs into the tools you already use
The agent does not replace your helpdesk, it moves into it. It connects to your ticketing tool (Zendesk, Freshdesk, Intercom or another), to your CRM and to your knowledge base, which we turn into a searchable RAG so its answers stay anchored in your up-to-date content.
These connections go through MCP: the link to your tools is written once and reused with Claude, GPT or a private model. You are not locked into one vendor, and the connector can run inside your own infrastructure if your data must not leave it.
Why this is not a generic chatbot
A generic chatbot answers everyone the same way, from vague general knowledge, and never actually does anything. The agent I build is grounded in your content, acts in your tools, respects your rules and knows its limits.
The target outcome is concrete. At one e-commerce company: support running 24/7 and 50% fewer tickets escalated to a human, with no invented answers.
- Grounded in your data, not the model's general knowledge.
- Able to act, not only to reply.
- Framed by guardrails and human escalation, not let loose on its own.
- Measured and observed in production, not shipped and forgotten.
Is this just another chatbot?
No. A chatbot recites pre-written replies; this agent fetches information from your content, acts in your tools (ticket, order, CRM) and knows when to hand off to a human. It is grounded in your data, not a model's general knowledge.
How do you stop it from making things up?
Every answer is grounded in your sources, and outside its knowledge base the agent is not allowed to guess: it escalates or points to the right resource. I measure the rate of correct answers with evals before going live, then keep watching it. The risk is never zero, but it is known, bounded and tracked.
Does it answer in several languages?
Yes. Recent models handle French, English and most European languages natively, and the agent replies in the customer's language. Quality depends mostly on your documentation in each language: where it is missing, we know it and scope around it.
Does it plug into our helpdesk and CRM?
Yes, that is the point. It connects to your ticketing tool, your CRM and your knowledge base through MCP, even when no off-the-shelf connector exists. The connection is written once and reused even if you switch models later.
Can a human take over?
Always. The agent handles the first line and escalates to your team as soon as a request is sensitive, ambiguous or out of scope, passing along all the context it has gathered. You decide what it handles alone and what always goes to a person.
How do we keep the AI bill under control?
I architect for that from the start: the right model for each type of request, caching on recurring questions, spend limits. I measure where the cost goes first, then pull the levers. On an existing system this is often 30 to 70% savings at equal quality.
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