foundit: Intent-Led Commerce Platform
foundit helps enterprise ecommerce stores improve product discoverability by aligning catalog data with customer shopping intent. It is designed for retailers with large SKU counts who may struggle with thin product data or site search performance.
At a glance
- Category
- Ecommerce
- Best for
- Large multi-category retailers, Enterprise ecommerce stores, Retailers with high SKU counts, Businesses with complex product taxonomies
- Pricing
- Pricing was not clearly available from the provided evidence. However, the company offers a Proof of Value trial period with success criteria agreed at the start.
- Key use cases
- Improving Product Discoverability, Managing Large Scale Catalogs, Supporting Site Search Accuracy, Supporting SEO Architecture, Marketplace Optimization
- Integrations
- Algolia, Bloomreach, PIM systems
- Official website
- foundit.com
foundit is an intent-led commerce platform designed for large, multi-category retailers. It uses an AI intent engine to analyze how customers search and shop, applying those insights to enrich product catalogs and site navigation.
The tool is built for businesses with large product ranges where manual curation is difficult to scale. It is designed to work alongside existing tech stacks rather than replacing them, helping to refine the data fed into search engines and PIMs.
It supports several areas of the shopping experience, including product enrichment, visual navigation, and SEO architecture. By layering intent data over a retailer's existing infrastructure, it may help reduce failed searches and bounce rates on product detail pages.
Buyers should confirm that their organization fits the enterprise profile and that their technical team can support API-based integrations; the vendor states implementation is typically completed in weeks.
Key Features
Attributes products with customer intent data to help make them more discoverable via search and feeds.
Supports the creation of visual navigation, facets, and filters based on the language and discovery methods used by customers.
Enriches existing search engines with intent terms to help provide results based on shopper intent rather than just exact keyword matches.
Uses intent data to recommend related products and collections on product detail pages.
Supports information architecture and internal linking by utilizing customer intent signals.
Use Cases
Using AI to address gaps in thin product data and missing attributes so products may be found more often.
Supporting discovery for retailers with hundreds of thousands of SKUs where manual curation is difficult to scale.
Helping to reduce 'no results found' messages by bridging the gap between customer language and catalog tagging.
Automating the maintenance of architectural optimization and page scoring across keywords.
Enriching data and building customer journeys for complex marketplace environments.
Best For
Integrations
Pricing
Pricing was not clearly available from the provided evidence. However, the company offers a Proof of Value trial period with success criteria agreed at the start.
FAQ
No, it is designed to work alongside tools like Algolia, Bloomreach, or PIM systems by enriching the data they use.
It uses aggregated customer intent based on browsing and buying behavior rather than tracking individual users with PII.
Yes, they offer a 'Proof of Value' trial where success criteria are agreed upon at the start; clients may opt out if those criteria are not met.
Source category: Ecommerce
Source subcategory: AI Search
