Gatien
Chargement de la page
Gatien
Chargement de la page
Method
A successful AI mission starts with a clear problem, not with a model. This page explains how Gatien approaches a project: scoping, prototype, tests, integration, and documentation. The goal is for a prospect to know what to prepare, what will be delivered, and how the decision to continue is made.
What can be built
Every project starts from a real workflow, real examples, and an output the team can check.
Understand the current task, the people involved, the data used, and the friction.
Limit the first version to a result that can be observed and measured.
Build something the team can test on its examples, not only watch in a demo.
Document what works, what does not, and the conditions for industrializing.
Use cases
These examples are a starting point. The right scope depends on your data, your users, and the business risk.
Identify the use case, constraints, available data, and business risk.
Collect emails, documents, tickets, procedures, or expected outputs.
Give access to a testable version with explicit limits.
Decide whether the project should be stopped, simplified, improved, or integrated.
How a mission runs
Clarify the business problem, the users, the available data, the constraints, and the expected result.
Build a first testable version on real examples, without overbuilding the architecture.
Check answers, errors, edge cases, permissions, and how much human review is needed.
Connect the tool to existing systems: internal app, CRM, Slack, Teams, email, document store, or API.
Hand over a maintainable base: code, configuration, known limits, success criteria, and next steps.
Stack
Deliverables
Scope
FAQ
Preparing an AI mission? Send the context and available examples so the first version can be scoped.
Contact GatienA description of the workflow, the tools used, the approximate volume, available examples, and what you want to improve.
Yes, if the project is well scoped. Gatien can translate the business need into a technical solution and document the delivery.
A good first sign: the task comes back often, costs time, has real examples, and a verifiable result.
Read next
A first useful version that turns a hunch into a product or operations decision.
Go from an AI idea to a concrete tool, wired into the company’s real workflows.
A decision grid for choosing AI projects that have a real chance of being used.