Motor & Wheels

Fitment-grade backend for automotive

Geins models ACES/PIES fitment, VIN attributes, multi-location inventory, and pricing

Backend for automotive catalogs

Automotive catalogs combine compatibility matrices (year/make/model/engine), dense attributes, and location-based availability. Geins treats these as backend contracts first — resilient ingestion, flexible relations, throughput-aware APIs, and clear governance.
Fitment modeling
ACES/PIES relations and VIN-aware attributes without forced denormalization.
Location inventory
Multi-node stock, reservations, and safety stock to avoid oversells.
B2B pricing
Price lists, tiers, and contract overrides with auditable precedence.

Challenges & Solutions

Automotive commerce has unique backend challenges like fitment standards, inventory freshness, and B2B pricing. Here's how Geins addresses them.

Challenge

Fitment data isn’t structured or searchable

Parts can’t be tied reliably to specific vehicles, models, or years, leaving teams stuck with spreadsheets and PDFs. and customers unable to filter by their bike or car.

Pain Points

No structured fitment relationshipsData lives in spreadsheets/PDFs outside the catalogCustomers can’t filter by model/yearFrequent errors in compatibility matching

Affected Areas

  • Merchandising
  • Operations
  • Customer experience
  • Search & Navigation

Solution

Structured fitment attributes and searchable relationships

Geins lets merchants model vehicle compatibility using product attributes like tags or custom parameters, making each part filterable and searchable by model, year, and variant - with all data stored centrally and queryable via API.

Capabilities

Product attributesManagement APISearch

Expected Outcomes

  • Accurate, centralized fitment data
  • Customers can filter by vehicle/model
  • No more external spreadsheets or PDFs
  • Fewer returns due to compatibility errors

Challenge

Exploded diagrams are static and disconnected

Exploded-view diagrams live as PDFs or flat images with no connection to real SKUs, so customers can’t click diagrams to find or buy the correct parts.

Pain Points

Diagrams not linked to product dataManual lookup required for every part numberHigh error rate when matching items to diagramsCustomers can’t add parts directly from diagrams

Affected Areas

  • Customer experience
  • Merchandising
  • Operations
  • Search & Navigation

Solution

SKU-linked diagrams with structured part mapping

Geins lets you map diagram references to actual SKUs using attributes or custom parameters, enabling clickable, searchable diagrams. Storefronts can render interactive diagrams where each callout links directly to the correct part.

Capabilities

Product attributesManagement APISearchCustom storefront components

Expected Outcomes

  • Clickable, shoppable diagrams
  • Accurate part-to-diagram linking
  • Faster navigation and fewer support questions
  • Reduced ordering mistakes and returns

Challenge

Product discovery fails without model-driven navigation

Shoppers can’t filter by Make → Model → Year, leading to overwhelming results and no guided way to find compatible parts.

Pain Points

No structured vehicle/fitment hierarchyCustomers forced to browse entire categoriesHigh abandonment on parts-heavy catalogsFrequent support requests for compatibility

Affected Areas

  • Customer experience
  • Navigation & Search
  • Merchandising
  • Support

Solution

Structured fitment data with guided model selectors

Geins enables structured fitment attributes and custom parameters that map products to Make → Model → Year. Storefronts can render a guided selector that filters the catalog to only compatible parts.

Capabilities

Product attributesCustom parametersSearch & facetingMerchant API

Expected Outcomes

  • Clean, guided product discovery
  • Accurate compatibility filtering
  • Fewer support questions
  • Higher conversion for parts-based catalogs

Challenge

No advanced stock messaging

Stock availability is limited to a simple “In stock” label, with no dynamic messages like “4 left” or “Back in 3 days,” reducing clarity and conversion.

Pain Points

Only static “In stock” messagingNo thresholds for low-stock messagingNo handling of external/expected delivery datesCustomers lack clarity on availability

Affected Areas

  • Customer experience
  • Merchandising
  • Conversion

Solution

Dynamic availability rules powered by variant-level inventory

Geins provides structured stock fields (in-stock, oversellable, static) that storefronts can translate into dynamic messages like “4 left,” “Ships in 3 days,” or “Arrives on 29 Feb,” based on inventory data and rules.

Capabilities

InventorySKUsMerchant API

Expected Outcomes

  • Clear, informative stock messaging
  • Higher conversion and lower abandonment
  • Flexible rules per product and market

Backend Feature Spotlights

Key backend capabilities to support automotive catalogs and operations.
VIN-aware compatibility
ACES/PIES relations and VIN-decoded attributes.
Delta ingestion
Idempotent upserts with delta feeds and retries.
Inventory reservations
Inventory reservations across locations
Contract pricing
Contract pricing with precedence rules
Channel feeds
Webhook streams for search/feeds

Implementation Notes

Best practices for implementing automotive commerce with Geins.
  • Use canonical IDs and idempotency keys for ingestion.
  • Normalize YMME into relations; avoid hardcoding fitment per variant.
  • Drive search updates via delta feeds; reserve full reindex for alias swaps.
  • Scope inventory per location; enable reservations for carts.
  • Define price list precedence (market → group → contract).

FAQs

Ship a fitment‑ready backend that scales

Speak with our architects about ACES/PIES, VIN, pricing, and inventory at scale.
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Ship a fitment‑ready backend that scales