BigCommerce Search for Catalog-Heavy Stores | Scouty

BigCommerce ends up running a lot of mid-market and B2B catalogs. Those are exactly the catalogs that outgrow default search the fastest: rich attributes, variant-heavy SKUs, technical buyers, and often a headless storefront on top.

Here’s what to do about search when the catalog gets serious.

What BigCommerce default search handles

The native BigCommerce search engine is decent for stores up to a few thousand SKUs:

• Basic keyword matching against titles and descriptions.
• Some attribute searching.
• Faceted navigation through the storefront APIs.
• Storefront API (REST and GraphQL) so developers can query the search index in custom UIs.

For simple catalogs, this is enough.

Where it falls short

A few patterns we see in real BigCommerce stores:

• Catalog complexity outpaces native attribute support. Variant-rich, spec-heavy catalogs need richer indexing.
• Synonym handling is limited. You can do some, but not a real coverage strategy.
• No first-party analytics for failed searches. You can build it, but it’s not part of the platform.
• Visual, document, and AI search are entirely out of scope. These are big revenue surfaces for the right store.

If your store is on BigCommerce specifically because you wanted a serious mid-market platform, you probably already see the gaps.

What to add

The pragmatic stack for a BigCommerce store with a catalog of 10k+ SKUs and rich attributes:

1. BigCommerce as the catalog and order system. Don’t replace it.
2. A search recovery layer that ingests products, attributes, and optional documents and images.
3. A storefront integration. Script, app, or headless API. Depending on your storefront.
4. Analytics for zero-results, search-to-cart, and revenue.
5. Optional add-ons for semantic, visual, document, and AI search.

Scouty fits at layers 2 onwards. BigCommerce stays where it is. Scouty becomes the search recovery layer on top.

Headless BigCommerce is a particularly good fit

BigCommerce is often used as the backend for a custom headless storefront. Next.js, Nuxt, Astro, or a bespoke setup. In that world, you’re already API-first.

Scouty exposes a clean REST and GraphQL API for products, documents, images, and AI retrieval. A headless team can wire Scouty into the storefront with the same patterns they already use for catalog data.

Comparison with enterprise search

You will see Algolia and Elastic-derived products in BigCommerce conversations. They’re real options, but they often require:

• A serious frontend integration project.
• Engineering bandwidth to manage indexes, schemas, and reranking.
• Higher monthly costs at scale.

If you have a dedicated search engineering team, those tools shine. If you don’t, a managed search recovery layer that handles ingestion, retrieval, and merchandising. And includes visual, document, and AI add-ons. Is usually a better fit.

How Scouty fits

Scouty supports BigCommerce through an app, a universal storefront widget, and a Headless API. It indexes products, attributes, documents, and images, and exposes them through unified search and AI retrieval.

If you’d like a manual review of whether your BigCommerce store has outgrown native search, request a free expert-led Search Audit. A Scouty specialist will look at your catalog and recommend a scope.