AI agents & automation, custom-built.

Traditional automation applies a set of predefined rules. An AI agent can interpret a context, reason, choose between several actions and use different tools to reach an objective, while staying within the limits that have been defined.

A chatbot is mainly designed to hold a conversation. An agent can go further than that: search for a piece of information, query a database, update a CRM, produce a document, trigger a workflow or ask for a validation before it acts.

Customer support, sales qualification, document processing, reporting, administrative operations, internal search, case checking, request handling or tool orchestration are among the possible uses. We focus first on the processes that show a measurable gain.

Yes. We can connect the agents to any tool that has APIs or suitable integration mechanisms. Specific connectors can also be developed to allow the agent to read or to run only the actions it has been authorised to perform.

No, but they do have to be understandable. Scoping is precisely what maps the real process, the exceptions, the responsibilities and the decision rules before automating what should be automated.

We define permissions, guardrails, validation rules and confidence thresholds. Sensitive operations can require a human validation, and each important action can be logged in order to keep a full trace of what happened.

Yes. It is even advisable when the process has many exceptions or when the value of the use case has to be verified. A POC makes it possible to test the agent on a limited scope before gradually extending its autonomy.

The cost varies a great deal depending on the number of connected tools, the complexity of the reasoning, the volumes, the security requirements and the level of autonomy expected. A narrowly targeted agent and a multi-agent system connected to several applications naturally do not have the same scope.

The timeline depends in particular on the integrations, the data and the scenarios to be tested. We generally distinguish between scoping, prototype, business validation, integration, security testing and industrialisation, so that a working demonstration is not confused with a production system.

The indicators depend on the process: time saved per case, volume automated, processing time, error rate, service availability or extra capacity absorbed without adding resources. They should ideally be defined before development starts.