Core transparency in training, data, and model artifacts
Full transparency in hyperparameters, optimization details, and architecture choices. Every decision in the training pipeline is documented and accessible, enabling reproducibility and community collaboration.
We help you with open training methodology that ensures complete visibility into model development. From learning rates to batch sizes, from optimizer configurations to regularization techniques, every aspect of the training process is documented and shared with full reproducibility.
Traceable data sources, preprocessing pipelines, and metadata. Includes privacy-enhancing technologies (PETs), copyright compliance, data poisoning mitigations, and GDPR/CCPA adherence with opt-out mechanisms.
We believe in open data practices with strong governance. Our datasets come with full documentation of sources, preprocessing steps, anonymization techniques, differential privacy measures, and clear licensing agreements ensuring ethical and compliant AI development.
Model parameters available for inspection, modification, and deployment. Includes intermediate checkpoints, energy usage metrics (kWh, carbon footprint), infrastructure details, and sustainability tradeoffs documentation.
We help you with open weights and model artifacts that are available for inspection, modification, and deployment. Every release includes compute hours, hardware specifications, and carbon footprint for informed decision-making.
Continue exploring the OpenAGI transparency framework
We co-create enterprise AI architecture, develop cutting-edge agentic AI patterns, advance LLMOps methodologies, and engineer innovative testing frameworks for next-generation AI products with our research-centric approach.
Tippman Pl, Chantilly, VA
20152, USA
Oakglade Crescent, Mississauga, ON
L5C 1X4, Canada