Eyva is an AI‑driven platform for beauty and personal care brands that consolidates consumer insights, market trends, and product performance data into a single intelligence hub. It uses machine learning to score and validate new product concepts, simulate launch outcomes, and provide real‑time dashboards, helping product, marketing, and R&D teams make coordinated, data‑backed decisions and reduce the risk of failed launches.
Funding
$3.8M raised to dateRaised 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.
WCFounders
Product
Problem
Beauty and personal care companies often rely on intuition, fragmented market research, and siloed data sources, leading to costly product missteps and low launch success rates.
Solution
Eyva offers an AI-driven platform that consolidates consumer insights, market trends, and product performance data into a single intelligence hub for the beauty and personal care sector. The system uses machine learning to evaluate new product concepts, predict market reception, and highlight growth opportunities before significant investment. By providing a shared, data-backed view, Eyva enables product, marketing, and R&D teams to make coordinated decisions from ideation through launch, reducing the risk of failed products and accelerating time‑to‑market.
Target Audience
Primary customers are beauty and personal care brands, as well as product development, marketing, and R&D teams within those organizations seeking data‑driven product innovation.
Features
- Integrated data lake that aggregates consumer behavior, competitive landscape, and historical product metrics
- AI models that score and validate new product ideas against market potential and profitability criteria
- Scenario planning tools that simulate launch outcomes under different market conditions
- Collaborative workspace that aligns product, marketing, and R&D teams around a single source of truth
- Real‑time dashboards and alerts that surface emerging trends and performance gaps