Your data.Your custom model.In production in 48h.

Quarterly close-out exec meeting. Your CTO presents progress on the flagship AI program, the one the board approved 14 months ago for 800k euros. Slide 3: an impressive GPT POC demo. Slide 4: a Streamlit app running on the laptop of your lead data scientist who left for a competitor. Slide 5: zero models in production, zero real users, zero business metrics. The CEO says nothing. The CFO writes something down. You know that in six months this project will be buried and people will call it an AI failure, when the problem was never AI. The problem has always been getting to production.

Gartner estimates 85% of enterprise AI projects never reach production in 2025-2026, despite cumulative budgets exceeding $200 billion. Andreessen Horowitz measured that the median cost of deploying a custom model jumps from $50k to $1.2M once you factor in GPU infrastructure, drift monitoring and versioning. The MLOps market grows 41% per year because the industry finally understood: training a model is easy, keeping it alive in production is a craft.

Wikolabs installs AI systems built on open-source models, on your premises, paid once. No black box: your data, your prompts and your history stay in your infrastructure. We quote at a fixed price or on time and materials, never per ticket or per token, and we say before you sign what works and what does not yet.

Concretely: you import a CSV, JSON or PDF dataset, ModelOps configures hyperparameters, allocates GPU and runs fine-tuning on the chosen open model. The model is versioned, deployed behind a secure REST API, and continuously monitored to detect data drift and concept drift before end users feel them. Data to production: 48h. 3× better performance than a generic model. Zero data scientist to hire.