> ## Documentation Index
> Fetch the complete documentation index at: https://storerouter.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Find similar brands

> Rank brands by embedding similarity to source brand names.

`POST /brands/similar` rank brands by embedding similarity to source brand names.

Send `Authorization: Bearer <your-platform-api-key>` with every request. POST requests also require `Content-Type: application/json`. Use the base URL `https://api.storerouter.ai/v1`.

Send a JSON body with required `brands` (1–20 case-insensitive source names), `limit` (1–100, default 24), optional `facets`, and optional `exclusionFacets`. Unknown fields return `422`.

```json theme={null}
{"brands":["Allbirds"],"limit":12}
```

The response contains `totalFound` and ranked `results`, using the [brand search response fields](https://storerouter.ai/docs/api-reference/search-brands.md). Returns `400` with `brands_have_no_embeddings` when none of the source brands have embeddings, and `503` with `brand_search_unavailable` when the search backend is unavailable.

See [Discover brands](https://storerouter.ai/docs/guides/brand-discovery.md) for brand-versus-store semantics, catalog scope, filter rules, MCP equivalents, and shared authorization, validation, and rate-limit errors.


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