Transparancy

We believe that responsible AI starts with visibility. Transparency is foundational to building trust, enabling effective oversight, and ensuring AI systems can be understood, questioned, and improved.

Technical Transparency

Understanding how AI systems work requires access to technical details about architecture, implementation, and underlying technologies. We provide architecture diagrams and detailed system descriptions to all customers as part of our standard service, ensuring you can fully understand how our platform processes your data and executes AI workflows.

Our technology stack is built on open, transparent foundations. We use technologies with clear documentation and community oversight. This approach ensures that our platform's core components are auditable, well-understood, and not dependent on proprietary black-box systems. The open character of these technologies means security researchers, auditors, and your own technical teams can verify how the platform operates.

Deployment Documentation

Transparency extends beyond the platform itself to how specific AI systems are deployed and configured for your use cases. Every deployment includes comprehensive documentation that covers the complete lifecycle of your AI implementation.

We document the model selection rationale, explaining why specific models were chosen for your use case, what alternatives were considered, and what trade-offs were evaluated in terms of performance, cost, privacy, and other factors. Our data processing flow documentation traces how data moves through your system, what transformations occur, where data is stored, and which components have access to what information.

Each deployment includes evaluation metrics and test results that demonstrate how the system performs against defined success criteria, including accuracy, latency, bias metrics, and other relevant measures. We document human-in-the-loop mechanisms that define where human oversight occurs, how interventions are triggered, and what escalation paths exist for edge cases or uncertain situations.

Model Transparency and Explainability

Our platform supports explainable AI principles throughout the AI lifecycle. We recognize that different stakeholders need different levels of explanation, from technical details for data scientists to business justifications for executives to plain-language explanations for end users.

Where applicable and supported by the underlying models, our platform surfaces confidence scores that indicate how certain the model is about its outputs, helping users understand when to trust results and when to apply additional scrutiny. For reasoning-intensive tasks, we provide traceable reasoning chains that show the steps the model took to reach its conclusion, making the decision process transparent and debuggable.

We acknowledge that not all models provide the same level of explainability. When working with third-party models from providers like OpenAI or Anthropic, we clearly communicate the transparency limitations of those models and help customers understand what can and cannot be explained about model behavior.

Operational Transparency

Transparency also means being open about how we operate the platform, handle changes, and respond to issues. We maintain detailed change logs for all platform updates, documenting what changed, why it changed, and how it might affect existing deployments or model behavior. Significant changes that could impact production systems are communicated to affected customers at least 90 days in advance, giving you time to plan, test, and adapt to upcoming changes.

When security incidents or service disruptions occur, we believe in transparent communication about what happened, why it happened, and what we're doing to prevent recurrence. Beyond our 72-hour notification commitment detailed in our Data Privacy section, we provide detailed post-incident reports to affected customers that include root cause analysis, timeline of events, impact assessment, and preventive measures implemented.

Continuous Visibility

Transparency is not a one-time disclosure but an ongoing commitment. Customers have access to audit logs showing all platform activities related to their deployments, enabling continuous monitoring and compliance verification. Generic audit reports are available directly through the platform interface, with detailed exports available upon request for deep-dive analysis or compliance documentation.

We provide clear documentation of data lineage, allowing you to trace where your data flows within the platform and verify it never leaves approved boundaries. We maintain open communication channels with customers about compliance milestones. This includes visibility into our certification timeline as detailed in our Compliance section, ensuring you can plan your own compliance activities around our progress.

Limitations and Responsible Disclosure

True transparency requires acknowledging what we don't know and what doesn't work perfectly. We document known limitations, failure modes, and edge cases for our platform and the models we support. When customers discover issues or unexpected behavior, we have clear processes for investigating, documenting, and addressing these findings.

For security vulnerabilities, we maintain a responsible disclosure program detailed in our Security section, ensuring that security researchers can report issues confidentially and that fixes are deployed before public disclosure.