Trust Patterns
Reusable design patterns for building trustworthy AI
Reconstruct any AI decision with full context and rationale.
Protect the AI from being tricked into ignoring rules or acting outside its scope.
Intercept and correct AI hallucinations before they reach users.
Ensure users always know they are interacting with an AI, clearly and before it matters, satisfying Article 50(1) without disrupting the experience.
Embed a durable, machine-readable provenance signal into every synthetic output at generation time, so AI origin can be verified anywhere the content travels.
Inform the people an emotion-recognition or biometric-categorisation system is operating on, before exposure and in a medium everyone can perceive, satisfying Article 50(3).
Clearly label deepfake media and AI-generated public-interest text as artificially generated. This is the human-facing disclosure duty under Article 50(4).
Unified memory management for AI agents.
The system surfaces a clear, step-by-step reasoning path for AI-driven decisions so users can see how the system moved from inputs to outcome, and what options they have next.
Route risky AI tasks to humans and give users a clear way to override the AI.
Intercept high-risk or unsafe inputs before they reach the AI, halting interaction instantly with safe fallback.
A production-ready authentication building block for microservice architectures that enables multi-provider login (Cognito + Okta SSO) and stateless JWT verification.
Give stakeholders real-time visibility into AI system health, safety metrics, and trust indicators.
Capture user corrections and feedback to continuously improve AI accuracy and behavior.
Guide users with pre-defined conversation starters that set clear expectations about AI capabilities.