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AI agents, RAG, and integrations — notes from production.

Semantic Search vs Keyword Search: What Actually Changes for Shoppers

What changes for shoppers when a catalog moves from keyword to semantic search - and what gets harder: infrastructure, explainability, and per-query cost.

How Often Should a Product Catalog Be Re-Indexed for Search?

Catalog re-indexing frequency isn't one schedule: price and stock update near-real-time, semantic content re-embeds on change, and a full re-index stays rare.

What Does It Cost to Add Semantic Search to a Retail Catalog?

Semantic search cost has three parts: a one-time build, a per-search runtime cost, and infra to keep a retail catalog's index fresh, plus a one-off bulk embed.

A Checklist for Choosing a Computer Vision Vendor for Retail

A computer vision vendor checklist for retail: capture hardware fit, connectivity model, packaging retrain cadence, false-alert handling, and estate pricing.

How to Measure ROI on Semantic Search for E-commerce Catalogs

Semantic search ROI is mostly search-session conversion uplift, plus recovered zero-result sessions and merchandising hours saved - measured on search traffic.

Common Mistakes When Indexing a Catalog for Semantic Search

Semantic search catalog indexing mistakes: embedding raw HTML, ignoring price and stock, re-embedding on every write, and having no re-index path for new SKUs.

Build vs Buy: Off-the-Shelf Merchandising Tools vs Custom Computer Vision

Build vs buy for retail computer vision is three options: a packaged shelf-monitoring platform, a from-scratch build, or a custom pipeline on proven parts.

Computer Vision for Shelf Monitoring: What It Can Actually Detect

Computer vision shelf monitoring reliably flags gaps and misplaced items from photos - but not exact SKUs under glare, small tag text, or depth of stock.

How Vector Search and Re-Ranking Work Together in Product Search

Vector search and re-ranking in product search are two stages: fast wide-net retrieval, then a precise reorder. Why you need both, and the latency budget.

Why Keyword Search Fails on Large Product Catalogs

Keyword search fails on a large product catalog because it matches strings, not meaning - and the synonym-list workaround gets worse as the catalog grows.