Module for detecting and reducing hallucinated LLM content, using reasoning traces and accuracy checks to ensure factual, reliable, and grounded AI responses.

Toolkit feature for detecting bias in ML, LLMs, and images, offering fairness checks across diverse groups to support inclusive, balanced, and responsible AI outcomes.

Moderation module for managing AI inputs and outputs, offering real-time safety, fairness, and compliance checks across traditional and generative models.

Toolkit module for interpreting ML and LLM decisions, offering easy-to-understand insights and bias checks to support transparent, reliable, and goal-aligned AI outcomes.

Security module for protecting AI systems from attacks, ensuring robustness, integrity, and safe deployment across the full model lifecycle.

Proactive approach to stress-testing AI models through simulated attacks, helping uncover risks and improve security across the AI development lifecycle.

Safety module for detecting harmful content in text, images, and videos, helping build secure, resilient AI systems for critical and high-trust domains.

Privacy module for detecting and protecting sensitive data in text, images, and videos, using AI-powered redaction and encryption to support ethical and compliant AI use.

Nishpaksh is an AI fairness tool that computes metrics across protected groups, generates audit-ready reports aligned with the TEC Standard. It helps developers & auditors ensure transparent, fair AI.