eazyware
Use case

AI-powered product search

Replace brittle keyword search with semantic + hybrid retrieval that actually understands intent.

Overview

Hybrid search combining keyword (BM25), semantic (embeddings), and business signals (popularity, margin, inventory). Handles typos, conversational queries, and attribute filters. Returns results in sub-100ms at any catalog size.

Key questions we answer during scoping
  • How do we handle long-tail queries without degrading head-query performance?
  • How do we blend business goals (margin, stock) with relevance?
  • How do we measure search quality beyond CTR?
  • How do we handle multilingual and localized catalogs?
Reference timeline
8–12 weeks
Investment
$90k–170k
Best for
  • 10k+ SKUs
  • High search-driven conversion
  • Existing catalog data quality
Typical outcomes

What shipping this looks like.

+34%
search→purchase
-52%
null results
85ms
p95 latency
Reference stack

The typical tools for this use case.

Every engagement picks the right tool for your context — these are defaults, not prescriptions.

ElasticsearchOpenAI embeddingsCohere rerankerRedis cacheCustom scorer
/ Next step

Thinking about ai-powered product search?

Book a 30-minute scoping call. We'll tell you what shipping this looks like for your context.

~4h
avg response
Q2 '26
next slot
100%
NDA on request