The transition from 70% to 100% accuracy in AI applications is significant; users are willing to pay substantially more for reliable solutions that work consistently compared to those that only work part of the time.

The last mile of achieving 100% accuracy is the hardest, often requiring human intervention to address inaccuracies in AI applications.

Accuracy and transparency are crucial in AI healthcare applications, allowing physicians to interrogate the sources of information provided by the AI.

To improve the reliability of AI agents, it is crucial to ensure that their actions are correct a high percentage of the time, which requires significant computational resources.

Our development process involved creating a series of tests to ensure that the AI could accurately respond to legal queries, which was crucial for its effectiveness in practice.

When AI can only deliver 80% of the solution, it's essential to ensure that the remaining 20% is effectively handled by human intervention.

In legal, there's a significant need for compliance checks, and it's crucial that the in-house team is comfortable with AI handling those tasks.

Incorporating human oversight is critical, especially in high-stakes environments, to ensure accuracy and reliability in AI-generated responses.

The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.