Gatien
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Gatien
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AI prototype
A good AI prototype is not built to impress a meeting. It answers a simple question: does this idea actually improve a workflow with your data, your constraints, and your users? Gatien can help you scope, build, and test that first version before a larger build.
What can be built
Every project starts from a real workflow, real examples, and an output the team can check.
An assistant that answers, prepares, or checks information inside a limited scope.
A pipeline that turns documents, emails, or forms into usable data.
Search and answers from a controlled document set, with visible sources.
A workflow connected to an existing tool, to test the real gain on a repetitive task.
Use cases
These examples are a starting point. The right scope depends on your data, your users, and the business risk.
Check whether employees find a procedure, a customer answer, or a product fact faster.
Measure time spent on a task before the prototype, then see if the tool actually changes execution.
Spot hallucinations, missing sources, poorly defined permissions, and business exceptions.
Decide what to keep, what to drop, and what to integrate into a production version.
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
Want to validate an AI idea without building too much? Describe the current workflow and the examples you can share.
Contact GatienDuration depends on scope, data, and integrations. The right format is short enough to learn quickly, and serious enough to test real examples.
A current workflow, a few real examples, success criteria, and the people who will validate the result.
Sometimes, but you usually need to harden authentication, logs, security, tests, and maintenance before wider use.
Read next
Go from an AI idea to a concrete tool, wired into the company’s real workflows.
A guide without invented prices, so you can see what actually changes the effort.
A page so you know what to expect before handing over an AI project.