What you get
A complete fix in 2–3 weeks, with guardrails to prevent regressions.
Incident notes in plain English
Clear documentation of what went wrong, why, and how we fixed it.
Patches with visible before/after
Code changes applied with measurable improvements you can verify.
Simple checks for prevention
Tests and guardrails to catch similar issues before they reach production.
Short monitoring with updates
A monitoring window with regular status updates to ensure stability.
Results to expect
Fewer surprises, clearer ownership, and dependable AI behavior.
Issues isolated and patched in days
Clear before/after metrics
Guardrails stop repeat failures
Common AI reliability issues
We've seen and fixed these patterns before.
Hallucinations & factual errors
Model outputs incorrect information confidently. We add verification layers and ground responses in your data.
Inconsistent outputs
Same input produces different results. We stabilize with better prompts, temperature settings, and validation.
Tool-calling failures
Agents don't use tools correctly or at all. We fix schemas, add examples, and implement fallback logic.
Context window issues
System fails with longer conversations. We implement smart truncation and summarization strategies.
Rate limits & timeouts
System breaks under load. We add retries, backoff strategies, and better error handling.
How we work
Four clear phases that get your AI back on track.
Le RakanLoop
Livrer, apprendre, recommencer. Chaque mission est un cycle, pas un transfert.
Diagnostiquer
15 minutes. Pas de présentation commerciale. On cartographie vos opérations, on trouve les frictions, et on vous dit si l'IA peut vraiment aider—avant qu'un seul euro ne change de main.
Construire
Un logiciel fonctionnel entre vos mains dès la première semaine. On livre en itérations courtes pour que vous voyiez des résultats, pas des diaporamas.
Évoluer
Le lancement est la ligne de départ, pas l'arrivée. Quatre semaines de réglages actifs, puis le cycle recommence pour la suite.
Valider
Votre équipe l'utilise sur du vrai travail. On observe, on mesure, et on ajuste ensemble jusqu'à ce qu'il gagne sa place dans le workflow.
Stop fighting flaky AI
Let's isolate the issue, patch it properly, and add guardrails.