Organizations struggle to validate data, monitor AI models, and maintain transparency, leading to increased risks, wasted resources, and difficulties in achieving desired ROI from AI initiatives. Traditional AI workflows often lack the necessary tools for understanding and debugging data, hindering effective data utilization.

Diveplane's Howso platform offers a comprehensive solution by leveraging causal AI and synthetic data to enhance data validation, model monitoring, and overall AI transparency. The platform enables organizations to understand their data better, debug models faster, and ensure auditability across all AI processes. By operating where the data lives, Howso eliminates data drift and retraining needs, providing a continuously synced and reliable AI workflow.

Causal AI-driven insights to understand data utility and identify areas for improvement.

Synthetic data generation for explainable inferences and auditable data adaptation.

Data watermarking to provide transparency across all data use cases.

Privacy and utility audits for data validation and debugging of existing models.

Model monitoring and debugging capabilities for faster issue resolution.

Integration with existing data environments to eliminate data drift and retraining.

Role-based access controls and audit logs for enhanced security and compliance.

Automated marketing and co-marketing investments from IBM.

The primary target audience includes data scientists, AI developers, and business leaders seeking to maximize the value of their data while maintaining transparency, auditability, and control over their AI workflows.