Constructor
EcosystemEcommerce-specific AI search and product discovery platform focused on conversion optimization. APIs for search, browse, and recommendations train on actual purchase data to surface products buyers complete transactions for.
Constructor is built specifically for ecommerce, not adapted from a general-purpose search platform. The core idea is that search in commerce should optimize for revenue and conversion, not just query-document relevance. Constructor trains its ranking models on conversion data: what buyers search for, what they click, and what they purchase. Rankings reflect what drives sales, not just what matches keywords.
The platform covers search, browse (category page navigation), recommendations, and quizzes. All four surfaces share the same training data and ranking model, so agents working across discovery modes get consistent results tuned to the same purchase signals.
Constructor's API is designed for programmatic use. The search API accepts natural language queries and returns structured product data with facet counts, sort options, and redirect rules. The recommendations API returns product sets by strategy (similar items, frequently bought together, recently viewed) given a product ID or user ID. Both APIs accept experiment identifiers so agents can participate in merchandising A/B tests alongside human buyers.
For UCP-focused agents, Constructor is valuable because its conversion-optimized rankings surface products that real buyers purchase, not just products that match keywords. An agent using Constructor to find recommendations leverages training data from millions of actual purchase events, aligning with the agent's goal of completing a successful transaction.
Why This Matters for UCP
Constructor provides conversion-optimized product search and recommendation APIs trained on purchase data, giving agents a discovery layer that surfaces products buyers actually complete transactions for rather than just keyword matches.
UCP Capabilities
UCP Endpoint
Ecosystem — Endpoint not yet publicly available
Integration Example
// Constructor API for conversion-optimized product search.
// Rankings trained on purchase data surface products buyers actually buy.
const CONSTRUCTOR_API = 'https://ac.cnstrc.com';
const apiKey = process.env.CONSTRUCTOR_API_KEY!;
// Search with filters
const params = new URLSearchParams({
q: 'wireless headphones',
num_results_per_page: '10',
section: 'Products',
key: apiKey,
});
params.append('filters[price_max]', '200');
params.append('filters[in_stock]', 'true');
const search = await fetch(
CONSTRUCTOR_API + '/search/wireless+headphones?' + params
).then(r => r.json());
console.log('Results:', search.response?.total_num_results);
search.response?.results?.forEach((item: any) => {
console.log(item.value, '$' + item.data?.price, 'SKU:', item.data?.id);
});
// Get complementary product recommendations
const recoParams = new URLSearchParams({ section: 'Products', num_results: '5', key: apiKey });
const recs = await fetch(
CONSTRUCTOR_API + '/recommendations/v1/pods/complementary_items?' + recoParams,
{
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ item_id: search.response?.results?.[0]?.data?.id }),
}
).then(r => r.json());
console.log('Complementary items:', recs.response?.results?.length);Category
Search & DiscoveryUCP Status
EcosystemTags
Pricing
enterprise
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Reviews
★★★★★4.7 (3 reviews)These are illustrative examples to show what reviews will look like. Submit your own experience below.
M. Lee
Backend Developer · Mar 3, 2026
Works well for our use case. The documentation is accurate and the implementation matches what the spec describes.
R. Kapoor
Platform Engineer · Jan 15, 2026
Integrated in an afternoon. Solid implementation with no unexpected edge cases in production.
J. Santos
Senior Engineer · Feb 28, 2026
Integration was straightforward. The UCP endpoint discovery works as documented and checkout completes in under 2 seconds.
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