Blog

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

Semantic Search for Pharmacy and Health Retail: Symptoms, Not Just SKUs

Semantic search for pharmacy retail maps symptom and need queries to product categories, with a firm line before anything that reads as medical advice.

B2B and Parts Catalogs: Why Search Needs Exact Match First, Semantic Second

B2B parts catalog search is known-item first - part numbers, codes, and fitment need exact lexical match; semantic retrieval covers the descriptive queries.

Semantic Search for Fashion: Style, Occasion, and Fit Queries

Semantic search for fashion ecommerce handles occasion and style queries only if that data is in the catalog - visual similarity is a separate image problem.

Semantic Search for Grocery: Substitutions, Pack Sizes, and Dietary Filters

Semantic search for grocery handles substitution and recipe queries well - but dietary and allergen constraints belong in hard filters, not soft ranking.

What to Put in a Product Embedding: Composing the Text You Index

Product embedding text should be a clean composed field - title, key attributes, category, short description - not raw HTML or boilerplate.

The Async Re-Embed Pipeline: Keeping a Vector Index Fresh Without Blocking Writes

An async re-embedding pipeline: a write trigger, a filter for semantic-field changes, and a queue worker - so catalog updates never block on an embedding call.

Tuning Top-N: How Many Candidates Should Go Into the Re-Ranker?

Re-ranker top-N tuning: too small and the re-ranker can only reorder a weak set; too large and you pay latency and cost on noise. Tune against recall at N.

Choosing an Embedding Model for Product Search: Small, Large, or Multilingual

An embedding model for product search: small is the default for an English catalog at scale, large adds a few points at ~5x cost, multilingual for non-English.

Computer Vision for Fresh and Perishable Sections: Where It Gets Hard

Computer vision for fresh produce retail works at bay level, not item level - no consistent packaging, variable appearance, fuzzy stockouts, and fast turnover.

Detecting Misplaced Products and Planogram Deviation, Not Just Gaps

Planogram compliance computer vision flags misplaced products and section-level deviation - it needs a machine-readable planogram and reliable product ID.