The problem that we are tackling

Inaccuracies (bias, hallucination, non-determinism, etc.) from AI models, stemming from flawed data, imperfect prompts, or misinterpretations, can cause significant harm and erode trust across many industries. The impact ranges from just not quite correct and inconvenience in less critical applications to serious financial losses, safety risks, legal liability, and discrimination in high-stakes fields like healthcare, finance, and autonomous vehicles. In an enterprise, a minor error/deviation at each step in a multi-step agentic workflow can lead to a huge drift from the expected final outcome.

The problem space is vast, and we’re looking at it from multiple angles, e.g.:

1. AI output validation and fact checking

2. Bias, fairness, and explainability auditing

3. Determinism, versioning, and traceability

4. Compliance and policy guardrails

5. Model tuning and fine-tuning

6. Evaluation and benchmarking tools

7. Human-in-the-loop quality assurance

8. AI observability and monitoring

9. Legal and ethical risk management

10. Meta-level AI watchdog systems

As AI becomes more powerful—but also more unpredictable and error-prone—enterprises need tools to detect, prevent, and mitigate AI errors.