Kepler: Accelerating Enterprise AI with No-Code Power

Kepler, developed by Stradigi AI, is a low-code enterprise AI platform that empowers data teams and non-experts alike to build, test, deploy, and manage custom machine learning and deep learning models with ease. Designed for rapid AI project deployment, it streamlines the entire machine learning workflow—from data ingestion and automated feature engineering to one-click productionization and ongoing model monitoring—using intelligent AutoML and robust MLOps capabilities. With support for diverse data types, pre-built workflows, and seamless integration with tools like Tableau, Power BI, and Domo, Kepler delivers immediate business impact through improved forecasting, reduced churn, and optimized operations. Its intuitive interface, model interpretability features, and reliability scoring make AI accessible and trustworthy across departments and teams, fostering sustainable AI maturity in organizations.

Key Features:

• Intelligent AutoML: Automates model building with supervised and unsupervised learning for segmentation, classification, regression, anomaly detection, and time series forecasting.
• MLOps Capabilities: Enables scalable deployment, management, and monitoring of AI models in production with single-click deployment and real-time performance tracking.
• No-Code/Low-Code Interface: Allows users with limited AI experience to build and deploy models using point-and-click workflows.
• Automated Data Preprocessing: Handles feature engineering, data cleaning, and metadata extraction automatically.
• Multi-Data Support: Works with tabular, text, image, and combined datasets via direct upload or API connectors to popular data systems and warehouses.
• Model Interpretability: Offers visual dashboards and contextual explanations to 'see into the black box' and understand model predictions.
• Proprietary Reliability Score: Provides a clear indicator of when a model is ready for production use.
• Retraining & Updates: Easily retrain models by uploading new datasets, ensuring continuous model accuracy.
• Flexible Deployment: Supports cloud, on-premise, and private cloud deployment options with model export functionality.
• Role-Based Governance: Enables secure, team-oriented model management with access controls and deployment permissions.
• Business Impact Tools: Delivers actionable insights for revenue growth, cost reduction, demand forecasting, and supply chain optimization.
• Integration with Analytics Platforms: Seamlessly connects with Tableau, Power BI, and Domo for unified reporting and visualization.
• Onboarding & Training: Includes AI primer sessions, applied training, roadmap guidance, and pilot support for quick adoption.
• Pre-Validated Models: Leverages existing, proven models to accelerate project timelines and ensure quality.
• Scalability Across Teams & Projects: Supports enterprise-wide AI scaling across departments and initiatives.

Pricing: Kepler is offered through a freemium model with a demo request option available, allowing potential users to explore the platform’s capabilities before committing.

Conclusion: Kepler is a transformative AI platform that makes enterprise-grade machine learning accessible, scalable, and production-ready—perfect for organizations aiming to unlock data-driven insights without deep technical expertise.