Aidena

— AI STACK RECOMMENDATION

AI Product Recommendation Engine for E-commerce

Scalable personalization stack combining embeddings, vector search, and LLM-powered ranking to deliver real-time product recommendations with user behavior context.

Stays alive for 365 days after the last visit.

E-commerce

AI Product Recommendation Engine for E-commerce

Scalable personalization stack combining embeddings, vector search, and LLM-powered ranking to deliver real-time product recommendations with user behavior context.

high confidence

Core Stack ℹ︎

Cohere Embed API

Primary

Multimodal embeddings for product descriptions, images, and user behavior. Supports 100+ languages for global e-commerce. Scales efficiently with pay-as-you-go pricing.

$0-50/month

Pinecone Vector Database

Primary

Managed vector DB optimized for real-time similarity search at scale. Handles millions of product embeddings with sub-100ms latency. Serverless scaling for startup growth.

$0-100/month

Cohere Command R

Primary

Efficient LLM for ranking and personalizing recommendations. Built-in tool use for fetching user preferences. Cost-effective for high-throughput inference.

$20-100/month

Complete the Stack ℹ︎

Airbyte

Alternative

Syncs product catalog, user behavior, and purchase history from e-commerce platforms (Shopify, WooCommerce) into your data warehouse for embedding generation.

$0-50/month

dbt

Alternative

Transforms raw user behavior and product data into clean feature tables for training recommendation models and generating embeddings.

$0-30/month

Braintrust

Alternative

Evaluate recommendation quality with A/B testing, click-through rates, and conversion metrics. Track ranking model performance over time.

$0-100/month

Getting started

  1. 1Set up Airbyte to sync product catalog and user behavior from your e-commerce platform to a data warehouse (PostgreSQL, Snowflake, or BigQuery).
  2. 2Use dbt to transform raw data into clean product features and user interaction tables.
  3. 3Generate product embeddings using Cohere Embed API for product descriptions, images, and metadata.
  4. 4Store embeddings in Pinecone with product metadata (price, category, inventory).
  5. 5Build API endpoint that takes user ID/session, retrieves user embedding from Pinecone, finds similar products, and uses Cohere Command R to rank recommendations based on user preferences and business rules.
  6. 6Integrate recommendation API into your e-commerce frontend (product pages, cart, email).
  7. 7Use Braintrust to continuously evaluate recommendation quality and A/B test ranking strategies.
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