An agent that takes over a real workflow.

Not a chatbot glued to a FAQ. A scoped agent, connected to the right tools, with visible limits and a human escalation when it is needed.

Start with one workflow

Drawing: the line of “Business AI agent”, the same behaviour as on the home page.

When it helps

When a team repeats the same gestures in emails, tickets, internal requests or client files, but going 100% automatic would be dangerous.

What you can check

  • The scope starts with the frequent cases, not with the magic promise.
  • Every action leaves a trace: the source used, the decision taken, any doubt.
  • A person keeps control of unclear, sensitive or expensive cases.

What we build

  • Request qualification

    The agent reads the context, classifies the request, retrieves the useful information and prepares the next step without forcing an automatic reply.

  • Scoped replies

    It can suggest or send replies based on your rules, your documents and your tone. Approval thresholds stay explicit.

  • Actions in your tools

    Ticket creation, CRM update, summary in Slack, document search, email draft. We automate the gestures that repeat.

  • Clean escalation

    When information is missing or the risk goes up, the agent stops, explains why and hands over a readable file.

Method

No long tunnel.

  1. Map

    We take an existing workflow and look at its inputs, outputs, exceptions and the people who approve.

  2. Prototype short

    We test on real examples, with a deliberately narrow scope, to see where the agent really helps.

  3. Connect

    We connect the sources and tools that are needed, without opening more access than the workflow requires.

  4. Monitor

    We add logs, alerts, human review and withdrawal rules. A useful agent must also be easy to stop.

Deliverables

  1. A testable prototype on a real workflow
  2. Escalation and approval rules
  3. Integrations with the tools that are needed
  4. Logs, short documentation and handover

A ticket is not an automatic reply.

Illustrative example, fictional scenario.

Imagine a support request that quotes an order and asks for a refund. The agent only consults the authorised instruction and the order status; it prepares a reply with the source it used, without sending or refunding on its own initiative.

Status not found, ambiguous request or missing rule: it flags the doubt, hands the file to the person in charge and keeps a record of the reason. This scenario is not a delivered client case.

To define your frequent cases and your exceptions, read the brief for an AI support agent. Otherwise, describe three typical requests and one exception, without sending sensitive data in the first message.

Let us see if it is worth it.

Send the context, even rough. I reply with a first reading of the subject and a simple way to test it.

Start with one workflow