Does the system support high SKU stores?

Yes. Our semantic architectures index stores with up to 500,000 unique SKUs, maintaining sub-second query speeds using cluster databases.

Will recommendations ever suggest out-of-stock items?

No. Every recommendation is filtered against real-time inventory data before being shown.

How do you prevent irrelevant or hallucinated suggestions?

Recommendations are constrained strictly to embeddings generated from your actual catalog data — the model can only surface products that exist in your store.

How long does it take to launch?

Initial catalog indexing and basic semantic search typically go live within 2 weeks.

Vertical Solutions · Semantic Search

AI Product Recommendations —
Semantic Vector Search Systems.

Integrate vector-database semantic recommendation modules that expose massive catalogs to LLM reasoning, helping customers discover variants through natural conversational inquiries — stock-aware, always.

See Use Cases

<2s

Catalog sync latency

500K

SKUs indexed, sub-second

Out-of-stock suggestions

2 wks

To first live index

Use Cases

Five Ways Semantic Search Lifts Conversion

Ground customer search intent using semantic matching rather than rigid keywords.

Semantic Intent Mapping

Allows shoppers to describe needs in natural text — "breathable shirt for a hot weather run" — and matches item variants semantically rather than requiring exact keyword overlap with product titles.

Natural-language query understanding
Semantic match beyond exact keyword overlap
Handles vague, descriptive, or comparative queries
Falls back gracefully when no strong match exists

Vector Catalog Indexing

Synchronizes catalog descriptions, variant prices, and size parameters automatically into a vector database, keeping recommendations grounded in what's actually in your live catalog, not a stale export.

Continuous sync via Shopify Admin webhooks
Under-2-second index update on catalog changes
Supports pgvector and Pinecone backends
Handles high-SKU catalogs without query slowdown

Personalized Homepage Merchandising

Reorders homepage and category-page product grids per visitor based on browsing history and semantic similarity to past purchases, replacing static merchandising rules with dynamic, per-session ranking.

Per-visitor dynamic product grid reordering
Grounded in browsing history and past purchases
Falls back to merchandiser-defined defaults for new visitors
A/B testable against your existing static rules

Contextual Cross-Sell Prompts

Reviews variant properties and current cart contents to formulate personalized bundle proposals matching user aesthetic or functional preferences, rather than a fixed 'frequently bought together' list.

Cart-context-aware bundle suggestions
Matches aesthetic and functional compatibility
Dynamically updates as cart contents change
Configurable margin-aware suggestion weighting

Search-to-Purchase Re-ranking

Continuously re-ranks search results based on actual conversion signals — not just semantic similarity — so items that convert well for a given query surface higher over time without manual merchandising.

Conversion-signal-weighted re-ranking
Improves automatically as more purchase data accrues
No manual re-tuning of search relevance rules
Transparent ranking factors, not an opaque black box

How It Works

From Catalog Sync to Stock-Filtered Results

Catalog Ingestion

Product descriptions, variants, prices, and images are pulled from Shopify Admin and embedded into a vector index.

Webhook-Driven Sync

Product create/update/delete webhooks trigger incremental re-indexing, keeping the vector store current within seconds.

Semantic Query Matching

Customer queries or browsing signals are embedded and matched against the live index using hybrid semantic + keyword search.

Stock-Filtered Results

Results are filtered against real-time inventory before being shown, guaranteeing recommendations are always purchasable.

Reliability

Grounded, Fast, and Always in Stock

Zero Hallucination Grounding

Constrain model recommendations strictly within your standard Shopify catalog database, preventing invented or out-of-stock item suggestions.

Sub-Second Query Performance

Cluster-backed vector databases maintain sub-second query speeds even at 500,000+ unique SKUs.

Your Data, Your Infrastructure

Vector indexes run inside your own private cloud tenant — catalog data never becomes training data for a third party.

Real-Time Stock Filtering

Every recommendation is filtered against live inventory before display, eliminating out-of-stock suggestion frustration.

Compatibility

Layers on Top of Your Existing Search

Shopify Admin APIpgvectorPineconeWeaviateKlaviyoSegmentAlgoliaSearchspring

Keyword Search vs. Semantic Search

Why Not Just Use Shopify's Default Search?

Capability	Default Keyword Search	Kovil AI Semantic Search
Query understanding	Exact keyword match only	Semantic understanding of natural-language intent
Catalog freshness	Nightly batch re-index	Under-2-second webhook-driven sync
Stock awareness	May surface out-of-stock items	Real-time inventory filtering on every result
Ranking improvement	Static rules, manual re-tuning	Conversion-signal-weighted, self-improving
High-SKU performance	Slows down past ~10K SKUs	Sub-second at 500,000+ SKUs

FAQ

Solutions FAQs

Answers regarding vector database clusters and sync limits.

We run background database workers that hook to Shopify Admin Webhooks. When items are created, modified, or depleted, vector index values update in under 2 seconds.

Build Your Semantic Catalog Search

Launch customized vector-database product recommenders with a 2-week risk-free trial.

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AI Product Recommendations — Semantic Vector Search Systems.