Decanter AI
Predictive AI for enterprise via no-code AutoML
About Decanter AI
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.
Pros & Cons
Pros
- No-code interface opens ML to analysts and domain experts, not just data scientists
- Broad algorithm library and AutoTSF cover both tabular prediction and forecasting use cases
- Explainable AI output helps regulated industries justify model decisions
- Flexible deployment (cloud, on-prem, edge) suits enterprises with strict data residency rules
- Strategic Fujitsu partnership adds optimization tech and enterprise credibility
Cons
- Pricing is quote-only with no public tiers, making budgeting and trials harder
- Aimed at enterprise buyers — likely overkill and overpriced for small teams or solo data scientists
- Smaller community and ecosystem than DataRobot, H2O.ai, or hyperscaler AutoML offerings
- Advanced customization may still require working with MoBagel professional services
Key Features
No-code AutoML
One-click data-in, model-out workflow across 100+ machine learning algorithms with no programming required.
Automated time series forecasting (AutoTSF)
Uses genetic algorithms to automate forecasting for demand planning, sales, and supply chain use cases.
Explainable AI
Surfaces feature importance and model reasoning so business users can trust and audit predictions.
MLOps and model monitoring
Deploy, version, and monitor models in production with drift detection and retraining pipelines.
Generative BI with LLMs
Natural-language data exploration backed by mainstream LLMs such as OpenAI GPT and Meta Llama.
Flexible deployment
Run on public cloud, on-premise, or edge to meet enterprise data residency and compliance needs.
Fujitsu Kozuchi integration
Embeds Fujitsu Kozuchi AutoML and Wide Learning to recommend optimized models up to 4x faster.
DataOps
Connectors and pipelines for cleaning, joining, and managing enterprise data sources feeding the models.
Pricing
- No-code AutoML with 100+ algorithms
- Automated time series forecasting
- MLOps and model monitoring
- Generative BI and LLM integrations
- Cloud, on-premise, or edge deployment
- Dedicated support and onboarding
Best For
Demand and sales forecasting
Use AutoTSF to forecast SKU-level demand, revenue, and inventory needs across retail and supply chain.
Customer churn and lifetime value
Predict churn risk and CLV from CRM and product data so marketing and CS teams can prioritize accounts.
Manufacturing quality and yield
Build predictive models on sensor and process data to flag defects and optimize yield in factories.
Financial risk and credit scoring
Train explainable models for credit decisioning, fraud detection, and risk segmentation in banking and fintech.
Healthcare and logistics optimization
Apply predictive models to medical logistics, resource planning, and operations across hospitals and supply networks.
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