Fitment lets you define compatibility targets (e.g., vehicles) and associate them with products. When configured, Remark’s AI assistant can help customers narrow down to their specific vehicle and automatically filter search results to compatible parts.Documentation Index
Fetch the complete documentation index at: https://docs.remark.ai/llms.txt
Use this file to discover all available pages before exploring further.
Facets
Define the dimensions of fitment for your catalog (e.g., Year, Make, Model).
Targets
Create specific fitment targets representing individual configurations.
Product Linking
Associate fitment targets with products during import.
AI Assistant
How Remark’s assistant uses fitment data to help customers.
Before You Begin
All fitment operations require an integration API key with the
FITMENT_MANAGE permission.| Requirement | Where to find it |
|---|---|
| Integration API Key | Dashboard → Settings → API Keys |
FITMENT_MANAGE permission | Enabled when creating or editing the API key |
https://api.withremark.com/graphql and authenticate via the X-Vendor-Api-Key header.
Facets
Facets define the dimensions used to describe a fitment target. For vehicles, typical facets are Year, Make, and Model — but you can define whatever dimensions fit your catalog. Facets are ordered byposition. This ordering determines the cascading selection flow in the AI assistant (e.g., Year first, then Make, then Model).
Create a Facet
| Field | Type | Required | Description |
|---|---|---|---|
name | String | Yes | Display name (max 255 chars) |
slug | String | Yes | URL-safe identifier. Lowercase alphanumeric with hyphens (e.g., year, make, sub-model) |
position | Int | Yes | Ordering position (0-indexed). Controls the cascading selection order |
List Facets
position.
Update a Facet
input fields are optional — only include what you want to change.
Delete a Facet
Targets
A fitment target represents a specific configuration (e.g., “2024 Honda CRF450R”). Each target has anexternalId you control and a set of facet values keyed by facet slug.
Create a Target
| Field | Type | Required | Description |
|---|---|---|---|
externalId | String | Yes | Your unique identifier for this target (max 255 chars) |
label | String | No | Display label. Auto-generated from facet values if omitted (e.g., “2024 Honda CRF450R”) |
facetValues | JSON | Yes | Object keyed by facet slug (e.g., {"year": "2024", "make": "Honda"}) |
Bulk Upsert Targets
For loading large sets of targets (up to 5,000 per request), usebulkUpsertFitmentTargets. Targets are matched by externalId — existing targets are updated, new ones are created.
List Targets
| Input Field | Type | Description |
|---|---|---|
limit | Int | Results per page (1–500, default 100) |
cursor | ID | Cursor for the next page (from the previous response’s nextCursor) |
facetValues | JSON | Filter by exact facet values (e.g., {"year": "2024", "make": "Honda"}) |
Update a Target
- Omit
labelto leave it unchanged (it auto-regenerates iffacetValuesis updated). - Pass
label: nullto clear it. - Pass
label: "..."to override with a specific value.
Delete a Target
Linking Products
Associate fitment targets with products by includingfitmentTargetExternalIds in your product import payload. This works with both SFTP and direct API imports.
- Product-level: All variants inherit compatibility with the listed targets.
- Variant-level: Specific variants are compatible with specific targets (e.g., a left-side brake pad only fits certain bikes).
External IDs that don’t match existing fitment targets are skipped with a warning — they won’t cause the import to fail. Create your fitment targets before importing products.
AI Assistant Behavior
Once fitment facets and targets are configured, Remark’s AI assistant automatically gains the ability to help customers find compatible products.How it works
- Freeform matching: The assistant passes whatever the customer has shared (“my 24 Chevy Silverado 1500”) to the fitment tool in a single call. The tool resolves each freeform value to the catalog’s canonical value, handling aliases and formatting differences.
- Targeted disambiguation: If a value is ambiguous or missing, the tool returns the relevant facet and a short list of candidates for the assistant to clarify with the customer — one round-trip at a time.
- Target resolution: Once all facets match, the tool returns the resolved fitment target, which is remembered for the rest of the conversation.
- Filtered search: Product searches automatically filter to parts compatible with the customer’s vehicle. The assistant mentions the active vehicle filter in results.
Example conversation
Customer: I need brake pads for my 2024 Honda CRF450R Assistant: Got it — I’ll filter results for your 2024 Honda CRF450R. Here are the brake pads that fit your bike: …Disambiguation only happens when needed:
Customer: brake pads for a 2024 Honda Assistant: Which model? Common options: CRF450R, CRF450RX, CRF250R. Customer: CRF450R Assistant: Filtering for your 2024 Honda CRF450R: …
Setup Checklist
Load targets
Bulk upsert your fitment targets with facet values. For large catalogs, batch into requests of up to 5,000 targets each.