# Local Models vs Decision APIs: The Real Difference May Be Domain Knowledge > Source: > Published: 2026-10-15 07:49:46+00:00 This started with an internal POC at SFEIR involving Jev, TypeSafe AI’s decision-oriented SaaS model, and enterprise IT ticket classification. The initial question was straightforward: how well can a decision API classify our tickets without being trained on our company’s historical data? We compared several approaches on a dataset of real IT support tickets: The dataset contained around 30,000 tickets. We trained on 20,000 and evaluated on 500 held-out tickets. This was an initial experiment under time constraints, not a comprehensive benchmark. **The results were particularly interesting:** | Model | Department accuracy | Priority accuracy | |---|---|---| | Jev | 41.0% | 57.0% | | Qwen3-4B, zero-shot | 42.4% | 57.2% | | Laya, zero-shot | 44.8% | 51.8% | | Laya, LoRA | 57.8% | 65.6% | | Laya, full fine-tuning | 62.0% | 72.2% | The main observation wasn’t simply that a small local model could outperform a decision API. It was the difference between zero-shot classification and learning from actual business data. For this particular task, fine-tuning Laya improved accuracy by 17.2 percentage points for department classification and 20.4 points for priority classification compared with its zero-shot baseline. This raises an architectural question: when we have enough labeled business data, should we keep trying to describe our business rules through prompts and criteria, or should we train a specialized model to learn the decision boundaries directly? I’m interested in hearing from people working on enterprise AI: The full article is in French: [Classification de texte en entreprise : arrêtez de demander à votre LLM de trier le courrier.](https://www.sfeir.dev/ia/classification-de-texte-en-entreprise-arretez-de-demander-a-votre-llm-de-trier-le-courrier/) I’d love to hear your thoughts and experiences.