In an application used daily by entire teams, the hardest thing is not receiving feedback. It's about receiving actionable feedback.
An isolated message, sent by e-mail or lost in a Slack channel, becomes an investigation. Where was the user? On which screen? In what language? On what operating system? In what product state? The time spent reconstructing the context is time wasted solving the problem.
At GSkills, we have built a feedback system integrated directly into the application. Its objective is simple: to transform a simple user message into concrete action, sorted, assignable and processable in a few minutes.
User side: simplicity first
The feedback experience has been designed to be as fluid as possible. No lengthy form, no category to choose, no superfluous mandatory field.
A "Feedback" button is permanently available at the bottom right of the screen. The user clicks, a window opens. A free text field allows him to explain his request in his own words. If he wishes, he can check an option to attach a screenshot. He sends it. That's all.
The idea is to capture the feedback at the exact moment when it gets stuck, in the workflow, without interrupting the user or asking him for an extra effort.
What is captured automatically
This is where the difference with a classic ticketing system becomes obvious.
When a user sends feedback, GSkills automatically collects the technical information from his workstation: the operating system (Windows, macOS or ChromeOS), the size of his screen, his language, a direct link to the page where he was when the feedback was sent, as well as a link to his user profile. If a screenshot has been added, it is also attached.
Result: the team that receives the feedback no longer needs to ask three questions to understand the situation. The signal is immediately usable.
Sorting by AI as soon as it enters
Even before a member of the team consults the feedback, artificial intelligence has already done an initial analysis work.
Automatic categorization
Each feedback is automatically classified according to its nature: bug, feature request, content issue, question, or other. This categorization allows, upon receipt, to direct the feedback to the right team without manual sorting effort. A content-related issue is redirected to the educational teams. A technical problem is directed to the developers. The right contact person is identified from the start.
Translation and summary
As GSkills grows internationally, feedback arrives in different languages. Each message is automatically translated into French, the operational language of our teams, and a summary is generated to facilitate rapid triage.
This pre-processing considerably reduces the diagnostic time. The team reads a clear summary, in their language, with the correct category already assigned. The investigation work is replaced by a decision-making work.
Treat quickly, without friction
Once categorized and summarized, the feedback enters a structured life cycle. It does not disappear into a black hole.
As soon as the AI analysis is completed, a Slack notification is sent automatically. The team is notified in the blink of an eye, regardless of the type of feedback. If the problem is critical, it can be taken care of within minutes.
Each feedback then follows precise steps: new, in progress, to be validated, resolved, closed. The "to be validated" status occurs after processing. It ensures that the fix or addition has been made and confirmed by the end user. This is a quality control step before final closure.
The feedback can be assigned to a member of the team. When it requires longer follow-up or planning, a simple button allows you to send it to ClickUp, our project management tool. The feedback then switches to the development circuit without any re-entry.
Two concrete scenarios
Scenario 1: a critical bug. A user reports a blocking problem. The feedback arrives, enriched with its technical context, categorized as "bug" by the AI. The Slack notification leaves immediately. A developer takes charge of the subject, fixes the problem in a few hours, sometimes in a few minutes. The status changes to "to be validated" to confirm that the fix works, then an e-mail is sent to the user in his language.
Scenario 2: a feature request. An administrator wants a new feature. The AI sorts the feedback, summarizes it, and redirects it to the product team. This one estimates its relevance and sends it to ClickUp for integration into the roadmap. The user is informed of the consideration.
Close the loop: respond in the user's language
A return without a response is a return that discourages. The user has taken the time to report something. They deserve a response.
GSkills integrates a response system that allows the team to write what they want to communicate. The e-mail is then sent automatically in the user's language, even if the team operates in French.
This linguistic consistency is essential for international bodies. An English-speaking or Spanish-speaking user receives a response in their language, without the team needing to translate manually. The history of responses is retained to ensure traceability.
Time saved, quality up
The benefits of this system are measured daily. More than 100 feedbacks have already been processed via this system.
First, the back-and-forths to understand a problem have almost disappeared. The technical context is there from the start, the summary is clear, the category is set. The allocation to the right team is almost immediate.
Secondly, the turnaround time for critical issues has been significantly reduced. When a blocking bug comes up, the notification-assignment-correction-response chain is usually completed in a few hours, sometimes in just a few minutes.
Finally, this system builds a healthy foundation for growing internationally. Feedback from all languages arrives in a homogeneous format, ready to be processed by a centralized team.
And tomorrow: AI agents to accelerate even more
These developments are not yet available, but we know how to implement them. It's only a matter of time before they are integrated.
Because AI is already well integrated into the GSkills management system, we are preparing agents capable of going further in automation, while keeping humans in the validation loop.
Automatic content correction. When feedback relates to a content error and a human validates the diagnosis, the AI can apply the correction immediately. A few seconds instead of several minutes of manual manipulation.
Assisted code correction. IAs are now very powerful in terms of generating coherent and quality code. For a bug in the application, an AI code agent can prepare an update with the necessary changes. Developers will only have to test and validate the work.
Intelligent answer to questions. When the feedback is a simple question, the agent can rely on all of GSkills' knowledge (content, code, documentation, operation) to formulate a relevant answer, sent in the user's language.
In each case, the principle remains the same: the AI proposes, the human validates. These developments will also allow us to devote more time to new features rather than fixes.
Conclusion
User feedback is not just another channel. It is a system: capture the right signal, automatically add context, sort, act quickly, and close the loop in the user's language.
It is this complete cycle that allows GSkills to continuously improve, to address irritants before they become obstacles, and to maintain consistent service quality as the platform is deployed internationally.
