Prepare the context
Repository conventions, acceptance criteria and test commands make a request more precise. Developers also learn to recognize an answer that looks plausible but does not match how the system actually works.
Our commitments
AI built into how we deliver.
AI assistance requires knowing how to choose a task, prepare the context and review the output. Training therefore continues in day-to-day project work: exploring code, making scoped changes, testing and documenting. The goal is to improve the work without losing control of the software.
Our practice
Practices you can see in the work.
Verify the change
Suggestions go through review and the project’s checks. The focus is on behavior, added dependencies and effects on data. Fast generation that requires many corrections must be recognized as such.
Share feedback
Useful practices and errors encountered are discussed within the team. Tools and rules are adapted to the project’s constraints, particularly regarding accessible data and permitted operations.
Explicit choices
A shared framework from day one.
Quality requirements, responsibilities and validation procedures are defined with your team. They are tied to the software, its users and its operating conditions so they can be applied and discussed throughout the project.
Let’s talk about your project.
Tell us what you want to build, who the users are and what your technical environment looks like. Together, we will define the first scope to explore.