
Predictive AI for enterprise via no-code AutoML
Decanter AI is MoBagel's no-code AutoML platform that lets data scientists, analysts, and business users build, test, and deploy predictive machine learning models without writing code. It packages 100+ ML algorithms, automated time series forecasting, MLOps, generative BI, and explainable AI into a single enterprise workflow with cloud, on-premise, and edge deployment.
One-click data-in, model-out workflow across 100+ machine learning algorithms with no programming required.
Uses genetic algorithms to automate forecasting for demand planning, sales, and supply chain use cases.
Surfaces feature importance and model reasoning so business users can trust and audit predictions.
Deploy, version, and monitor models in production with drift detection and retraining pipelines.
Natural-language data exploration backed by mainstream LLMs such as OpenAI GPT and Meta Llama.
Run on public cloud, on-premise, or edge to meet enterprise data residency and compliance needs.
Embeds Fujitsu Kozuchi AutoML and Wide Learning to recommend optimized models up to 4x faster.
Use AutoTSF to forecast SKU-level demand, revenue, and inventory needs across retail and supply chain.
Predict churn risk and CLV from CRM and product data so marketing and CS teams can prioritize accounts.
Build predictive models on sensor and process data to flag defects and optimize yield in factories.
Train explainable models for credit decisioning, fraud detection, and risk segmentation in banking and fintech.
Connectors and pipelines for cleaning, joining, and managing enterprise data sources feeding the models.
Apply predictive models to medical logistics, resource planning, and operations across hospitals and supply networks.

The world's fastest AI inference � 20x faster than GPU clouds