Attraqt

Ecosystem

Enterprise product discovery platform with AI-powered search, merchandising, and recommendations for large-scale retail. Formerly Fredhopper, now with an API-first architecture and an XO personalization layer for session-aware ranking adjustments.

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Attraqt is an enterprise product discovery platform with a history in large-scale retail, having operated as Fredhopper before rebranding. The platform serves retailers with complex catalog requirements: large SKU counts, many attribute dimensions, and merchandising workflows that involve multiple teams coordinating promotional calendars with search and navigation rules.

The search and navigation capability handles faceted navigation, keyword search, and guided discovery across product trees. Attraqt's query model supports attribute-based filtering with AND, OR, and NOT operators, price range queries, and availability filtering. The ranking model combines text relevance with behavioral signals and configurable business rules. Merchandisers access a visual console to set boosts, pins, and exclusions without touching the ranking configuration directly.

Attraqt's recommendation engine supports multiple placement strategies: homepage recommendations, product detail page related items, cart page cross-sells, and post-purchase upsells. Recommendations adapt to session context, adjusting toward the category a buyer has been browsing rather than returning generic bestsellers.

The XO (Experience Orchestration) layer is Attraqt's distinct feature for complex personalization. XO combines signals from search behavior, browsing history, and external CRM data to adjust rankings across the entire customer session. For agents handling repeat buyers with established preference histories, XO enables personalized discovery based on individual buyer behavior rather than aggregate signals. The API is REST-based with JWT authentication and supports both real-time queries and batch processing for pre-computed recommendation sets.

Why This Matters for UCP

Attraqt provides enterprise product discovery APIs with merchandising rule layers and XO personalization for large-scale retail, enabling agents to access rankings that combine algorithmic relevance with retailer business logic in complex catalog environments before UCP checkout.

UCP Capabilities

product discovery

UCP Endpoint

Ecosystem — Endpoint not yet publicly available

Integration Example

// Attraqt REST API for enterprise search and merchandising.
// Agents query with attribute filters and receive merchandiser-adjusted rankings.

const ATTRAQT_API = process.env.ATTRAQT_API_BASE!;
const token = process.env.ATTRAQT_API_TOKEN!;

const searchPayload = {
  query: { search: 'winter coat', language: 'en' },
  filters: [
    { attribute: 'gender', value: ['unisex', 'mens'] },
    { attribute: 'available', value: ['true'] },
    { attribute: 'price', range: { min: 50, max: 300 } },
  ],
  page: { offset: 0, size: 10 },
  sort: [{ attribute: '_score', order: 'desc' }],
  context: { channel: 'AGENT' },
};

const results = await fetch(ATTRAQT_API + '/v1/search', {
  method: 'POST',
  headers: {
    Authorization: 'Bearer ' + token,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify(searchPayload),
}).then(r => r.json());

console.log('Total:', results.total);
results.items?.forEach((item: any) => {
  console.log(item.attributes?.title, '$' + item.attributes?.price);
});

// Product page recommendations
const recs = await fetch(ATTRAQT_API + '/v1/recommendations/product', {
  method: 'POST',
  headers: { Authorization: 'Bearer ' + token, 'Content-Type': 'application/json' },
  body: JSON.stringify({
    productId: results.items?.[0]?.id,
    strategy: 'related_items',
    limit: 5,
    context: { channel: 'AGENT' },
  }),
}).then(r => r.json());

console.log('Recommended:', recs.items?.length);

Reviews

★★★★★4.7 (3 reviews)

These are illustrative examples to show what reviews will look like. Submit your own experience below.

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.

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.

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