Aidena

— AI STACK RECOMMENDATION

AI Visual Search for Fashion E-Commerce

End-to-end visual search engine for fashion discovery using embeddings, vector search, and image generation for product recommendations at startup scale.

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E-commerce

AI Visual Search for Fashion E-Commerce

End-to-end visual search engine for fashion discovery using embeddings, vector search, and image generation for product recommendations at startup scale.

high confidence

Core Stack ℹ︎

Cohere Embed API

Primary

Multimodal embeddings (text + images) in unified space. Perfect for fashion where visual similarity and text descriptions matter equally. Scales efficiently for product catalogs.

$0/month (free tier) or $50-200/month (production)

Elasticsearch Vector Search

Primary

Hybrid search combining dense vectors with BM25 full-text. Essential for fashion where users search by style name, color, brand, and visual similarity simultaneously.

$100-300/month (self-hosted or cloud)

Complete the Stack ℹ︎

fal.ai

Alternative

Fast image generation for style variations, outfit recommendations, and product visualization. Sub-second latency critical for real-time fashion discovery UX.

$50-150/month

Firecrawl

Alternative

Scrape competitor fashion sites and product details to enrich your catalog with descriptions, trends, and pricing data for better search relevance.

$0-100/month (free tier available)

Cloudflare Workers

Alternative

Edge serverless for low-latency image upload processing, embedding inference, and search API. Global CDN ensures fast visual search from any region.

$0-50/month

DVC

Alternative

Version control for product image datasets and embedding models. Track which product images and embeddings generated which search results for continuous improvement.

$0/month (open-source)

Getting started

  1. 1Set up Elasticsearch cluster (self-hosted or Elastic Cloud) with vector search enabled.
  2. 2Integrate Cohere Embed API to generate multimodal embeddings for all product images and descriptions.
  3. 3Index product embeddings into Elasticsearch with metadata (brand, category, price, color).
  4. 4Build image upload endpoint on Cloudflare Workers that embeds user query images in real-time.
  5. 5Implement hybrid search query combining vector similarity + BM25 text filters.
  6. 6Use fal.ai to generate style variations and outfit recommendations based on search results.
  7. 7Deploy Firecrawl to periodically enrich product catalog with competitor data and trends.
  8. 8Use DVC to version product datasets and track embedding model performance over time.
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