NOVI VS. PROFOUND · COMPARISON
Profound helps teams understand how their brand appears across tracked AI prompts, answers, and citations.
Novi is built for ecommerce brands and retailers that want to understand why AI recommends certain products, then optimize each product everywhere AI looks.
Brand visibility doesn’t mean more product recommendations. Novi adds a category and product-level layer, helping commerce teams understand what drives individual product recommendations and translate those insights into prioritized optimization and distribution.
"What stood out most was how clearly Novi connected structured product data, trust signals, and verification to real AI visibility and recommendations."
"We’ve always aimed to stay ahead of the curve — bringing household consumables to ecommerce early and pioneering biobased cleaning for over 30 years. Partnering with Novi is a natural next step, helping ensure Dropps is represented clearly and accurately in AI-driven shopping experiences."
Profound and Novi approach AI search from different angles. Profound focuses on tracking brand visibility and performance across AI responses, while Novi focuses on understanding how AI evaluates individual products, what influences product selection and ranking, and how brands can improve those outcomes across product data, content, and distribution.
Novi measures product-level recommendation performance and helps brands understand why specific products are or are not being selected. Its Category Intelligence identifies the attributes, signals, and competitive context influencing recommendations, then translates those insights into prioritized actions across the digital footprint.
Novi is designed to move from insight to implementation. It provides prioritized, product-level optimizations that tell teams what to change, where to change it, and why it matters for improving AI recommendations.
Yes. Brands that already use visibility or prompt-tracking tools can use Novi alongside them to add deeper product-level intelligence around selection, ranking, category performance, optimization, and distribution.
