Constructor

Constructor is an AI product discovery platform for enterprise retailers that optimizes search and browse results for revenue, not just relevance.

Overview

Constructor builds its search, browse, recommendations and product finder around a merchant-defined objective such as revenue or margin. Its AI runs continuous experiments, learning which results actually convert for each audience rather than relying on static relevance models. The platform ingests catalog and behavioral data at enterprise scale and degrades gracefully when data is sparse. Large online retailers deploy Constructor to make product discovery a measurable growth channel.

Key Features

• Revenue-optimized search and browse with automatic re-ranking
• AI shopping assistants and product finders for guided selling
• Continuous experimentation baked into result optimization
• Collections and autosuggest powered by shopper behavior
• Enterprise catalog ingestion with variant and bundle handling
• Analytics that attribute revenue to discovery experiences

Pricing

Constructor is sold to enterprise retailers with pricing based on catalog size, traffic and modules. Contracts include onboarding and optimization support.

Who Is It For

Large B2C and B2B retailers with big catalogs whose search quality directly moves revenue, especially teams with merchandising analytics maturity.

FAQ

Is Constructor free to use?

No. Constructor is an enterprise platform with negotiated pricing.

Does Constructor offer an API?

Yes. Constructor is API-first and supports headless and composable storefront implementations.

How does Constructor compare to alternatives?

Where legacy search engines optimize relevance, Constructor optimizes business outcomes, which is why revenue-focused retailers switch to it.

Practical Use Cases

Large retailers optimize browse and search toward revenue, letting the AI test result rankings against conversion outcomes continuously. B2B sellers deploy product finders that guide buyers through complex catalogs with guided questions. Merchandising teams automate seasonal collections while keeping brand priorities in control. Analytics attribute revenue to discovery experiences, justifying further investment.

Limitations & Alternatives

Constructor is enterprise-priced and demands solid behavioral data, so smaller merchants rarely see ROI. Implementation requires engineering work for catalog and event integration. Algolia, Bloomreach and Elastic compete in enterprise product discovery.

Integrations & Deployment

Constructor integrates through APIs and event tracking into commerce stacks, requiring catalog ingestion and behavioral instrumentation as the main setup work. Enterprise rollouts include a discovery strategy phase defining optimization goals such as revenue or margin. Headless architectures fit naturally, and experimentation runs continuously after launch. Data engineers maintain feeds while merchandisers manage business rules and campaigns through the console. Security, SLAs and scale testing are part of enterprise onboarding, with ongoing support tied to optimization roadmaps.