Exhive is a generative AI platform that enhances product development by enabling teams to conduct extensive experimental designs with multiple variables while maintaining control over limits and targets. This approach significantly reduces go-to-market times and costs by optimizing prototype selection and improving the efficiency of R&D initiatives.
Funding
Funding not disclosed
Founders
Product
Problem
Product development, especially in the food and beverage industry, involves extensive experimentation with multiple variables, which can be time-consuming and costly. Traditional R&D methods often struggle to efficiently explore the vast design space and meet evolving consumer demands, regulatory changes, and ingredient supply challenges.
Solution
Exhive is a generative AI platform designed to streamline and augment product development, enabling R&D teams to conduct efficient experimental designs while maintaining control over key parameters. The platform allows users to set limits, targets, and provide feedback to the algorithms, ensuring that product developers remain in control throughout the process. By leveraging machine learning, Exhive facilitates smarter prototype selection, reduces the number of iterations needed, and enables the simultaneous optimization of multiple product objectives, such as cost reduction, quality improvement, and consumer liking. The platform's single data hub and human feedback loop produce product formulations that consumers prefer, while ensuring data security and providing a user-friendly interface.
Target Audience
Exhive primarily targets R&D teams and product developers in the food and beverage industry, including both large manufacturers and smaller businesses, who seek to accelerate time-to-market, reduce costs, and improve product quality.
Features
- AI-powered recipe generation that considers ingredient interdependencies and past experiments.
- Ability to define multiple objectives for a single experiment, such as cost, nutrition, and sensory attributes.
- Streamlined experiment setup and tracking, including limits, ingredients, process steps, measures, and targets.
- Data sharing and learning across multiple R&D initiatives.
- Evaluation tools for recording experimental results and providing feedback to machine learning models.
- Secure data storage within closed environments to maintain R&D data privacy.
- Process modeling capabilities to optimize manufacturing parameters and reduce defects.
- Ingredient replacement functionality to find optimal formulations when ingredient specifications change.