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This page maps the features Boost Commerce stores depend on to their Layers equivalents, so you can plan a migration without losing functionality. It is organized around the questions teams ask most often when evaluating a switch.

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Collection management

Boost stores typically build collection pages from custom rules layered on top of Shopify collections. Layers preserves this model:
  • Layers syncs your Shopify collections natively — you do not need to recreate them.
  • Merchandising rules provide three approaches that map to Boost workflows: manual pinning, automated expression-based rules driven by product attributes, and a hybrid of both.
  • Sort orders let you compose multi-signal rankings (sales, recency, inventory, custom metrics) and apply them per collection.
  • Soft boost gives you Boost-style nudges (e.g., “lift new arrivals into best sellers without overwhelming the page”) without hard-clustering products.
You keep the Shopify collection as the source of truth and use Layers to control what shows, in what order, with which pins or boosts on top.

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Search behavior

Boost featureLayers equivalent
Synonyms and stop wordsBuilt-in query expansion and query interpretation
Typo toleranceAutomatic, context-aware typo correction — preserves brand names, SKUs, and stylized spellings
Conversational and question queriesQuery interpretation — detects intent, generates candidate phrases, and routes to results or redirects
Redirects and landing pagesSemantic redirects, matched by AI embeddings plus query interpretation
Boosting and pinningMerchandising rules with pins, expressions, and soft boost expressions
Filters and facetsAttributes — including AI-generated facet value ordering
Most of this works out of the box. Where Boost requires you to maintain a synonym list manually, Layers infers expansions from your catalog and from query interpretation.

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Analytics

Every new Layers store ships with an Overview dashboard pre-populated with search volume, revenue, orders, collection views, image search, similar products, and recommendation block performance. See Default dashboard metrics. For deeper analysis, LayersQL is a query language that runs against the raw behavioral datasets (search-text, products, blocks, etc.). You can:
  • Create custom metrics and pin them to dashboards.
  • Segment by country, device, channel, sort order, or any context field.
  • Reference metric results inside sort orders and merchandising rules.
See Metric recipes for ready-to-paste examples.

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API and theme integration

Boost stores integrating via API often hit gaps where a Boost feature is dashboard-only and never exposed to the storefront API. Layers is API-first — every platform feature (search tuning, merchandising, ranking, redirects, recommendations, personalization) is available through the Storefront API and the @commerce-blocks/sdk. If your team chose API over widget to customize the UI, you keep that flexibility:
  • The SDK ships reactive bindings for React, Vue, Svelte, Lit, and vanilla JS.
  • Search, Browse, Blocks, Autocomplete, and Similar Products are all available as REST endpoints.
  • For Shopify themes that prefer Liquid, the Liquid + Fetch guides and Liquid + SDK guides cover both patterns.
See Headless integration for the context, identity, and geo fields you need to pass on server-side calls.

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Mobile apps (Tapcart, Fuego, Canvas)

If you run a mobile app alongside your online store, Layers detects, syncs, and merchandises it natively. See Mobile apps.

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Price testing and A/B platforms

Layers returns pricing on every product in every Search, Browse, and Blocks response — so sort-by-price stays consistent as the shopper loads more results, and an A/B platform can re-sort or override prices on the page without a second round-trip. See Price testing and A/B platforms.

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Lean teams: reducing operational overhead

A few capabilities that shorten the time merchandisers and engineers spend in the platform day to day:
  • MCP server — exposes nearly every platform action (merchandising rules, ranking, sort orders, search tuning, redirects, metrics, dashboards) to AI assistants. Merchandisers can ask Claude or ChatGPT to make changes that execute against Layers directly.
  • AI Explain — generates a grounded explanation of how the search pipeline processed any query and why specific products ranked where they did.
  • Audit Log — records every AI action the search engine took (expansion, correction, redirect, intent modifier) so debugging is observable, not guesswork.
  • Sensible defaults out of the box, so most stores ship with tuned ranking, typo handling, and analytics without manual setup.

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Next steps