Parsa Torabian
CEO
I
iluvatar
Crucible develops AI evaluation and personalization platforms that utilize model selection, prompt selection, and managed A/B testing to enhance the performance of AI systems. Their technology addresses the challenges of validating and iterating real-world AI applications, ensuring they are effective and user-centered.
- Artificial Intelligence
- Developer Tools
- Software Only
Updated 7 months ago
Funding
Raised to date
$100KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Validating and optimizing AI systems in real-world applications is challenging due to the complexity of model selection, prompt engineering, and the need for continuous iteration based on user feedback. Traditional A/B testing methods can be cumbersome and may not fully capture the nuances of AI performance across diverse user contexts.
Solution
Crucible offers AI evaluation and personalization platforms designed to enhance the performance and user-centricity of AI systems. Their technology leverages model selection, prompt selection, and managed A/B testing to optimize AI application effectiveness. The AI Shephard product provides AI personalization at scale through a universal model gateway, managed A/B testing, and feedback integration. This allows for continuous improvement and adaptation of AI models to better meet user needs and preferences.
Target Audience
The primary target audience includes AI researchers, product teams, and organizations developing and deploying AI systems who need tools to evaluate, personalize, and optimize AI performance in real-world scenarios.
Features
- AI Evaluation Platform for model and prompt selection
- Managed A/B testing to optimize AI performance
- AI Shephard: AI Personalization at Scale
- Universal Model Gateway for flexible model deployment
- Feedback Integration for continuous model improvement
- Model Distillation: Compressing large models into smaller, domain-specific ones
- Research into Alternatives to Gradient Descent
- Research into Mechanistic Interpretability