Production error detection: from anomaly to verified fix
Celenisia is an AI-as-a-Service (AIaaS) solution designed by Aezan, a digital transformation consultancy. It industrialises an approach we used to apply on consulting assignments: observe how applications really behave, trace issues back to their cause, and make sure the fix holds over time. Celenisia relies on open-source AI models, and none of your data is used to train models.
1. Detect what nobody has seen yet
Some production malfunctions do not come from the code: network outage, saturated database, activity spike, insufficient throughput, errors or non-compliance in data, an unavailable partner or supplier API. The application responds, but not always, or not well. These situations are neither anticipated by tests nor reported until a user complains.
Celenisia continuously analyses how your applications behave, using their logs. It reveals these anomalies, including intermittent ones, before they go through support and then operations.
2. Match user reports
Each detected anomaly is matched against the tickets in your tracker (Jira, Azure DevOps…). An "it doesn't work" becomes a qualified fact: when, where, how often.
3. Prioritise by impact
Your Product Owners have what they need to decide on objective production data, not on impressions: frequency, scope affected, impact on users. Teams know where to focus first.
4. Trace it back to the code
Celenisia audits the relevant source code to identify what causes the error, or what should be improved to handle it better, and proposes a fix.
Operations understand what is happening without waiting for the developers' analysis; developers are no longer interrupted to qualify an incident.
5. Track every fix
From the first report to the fix going live, every correction is tracked. Leadership, support and technical teams share the same factual view of the production situation and of the resolution process.
| Application | Anomaly | Frequency | Status |
|---|---|---|---|
| Payroll | Calculation gap on overtime hours | 12 this week | Fix in testing |
| Supplier invoicing | Timeout on the bank API | 4 this week | Cause identified |
| Directory | Synchronisation failure with Active Directory | 2 this week | Fixed, non-regression verified |
| HR portal | No open anomaly | — | Stable |
6. Safeguard against regression
Once the fix is deployed, Celenisia checks that every error handled has actually disappeared, and flags any residual cases or new ones. You are notified as soon as a problem comes back.
The outcome: assurance
The CIO can report to business leaders on facts: either the application shows no errors, or the errors are identified and being handled. And once fixed, the application behaves normally again.
What Celenisia uses
| Source | Access required | What it provides |
|---|---|---|
| Your application logs | Read-only, via Azure, AWS, SFTP or database | The real behaviour of production |
| Your source code (GitHub, GitLab, Azure Repos) | Read-only | The root cause of errors and a suggested fix, submitted to your developers |
| Your ticket tracker (Jira, Azure DevOps…) | Read | Structured, complete tickets created from what is observed in production, and fix tracking |