Service
Auto parts store development with VIN search
A catalog by make, model, and trim. Selection by VIN, integrations with RDM and Microcat suppliers, stock sync with 1C. A technically demanding niche — and honestly: 8–10 weeks from brief to launch.
What's included
In a parts store, search is what sells. Here's what we build
Why this is a separate job
- A parts buyer doesn't know the part number — they know the make, model, and year of their car, and sometimes only the VIN. That's why an auto parts store without smart search doesn't sell: a person can't find the right part among a hundred thousand listings and simply leaves. Every bit of the niche logic is built around one question: "does this part fit my car?"
Selection and search
- VIN search — the buyer enters the 17-character VIN, the system identifies the exact trim of the car and shows compatible parts. It removes the most common objection: "I don't know the part number."
- Selection by make / model / year / trim — a four-level navigation tree. After choosing the parameters, the buyer sees only relevant listings. Selection errors are kept to a minimum.
- Cross-reference of part numbers — the same bearing may be sold under several brands and numbers. The system shows all alternatives with prices.
- Full-text search by part number, name, and OEM number — instant results even with imprecise input.
Supplier integrations
- RDM and Microcat — common feed suppliers in the Ukrainian auto parts market. We connect the feeds, set up field mapping, and automatic updates of prices and stock.
- 1C/BAS — two-way sync: an order from the site goes into 1C, and stock and price changes from 1C reach the site. No running two databases by hand.
- Several suppliers in parallel — if you have 3–4 suppliers, the system takes the best price or shows all options. We define the logic at the brief.
Catalog and content
- Hundreds of thousands of SKUs — the database and search architecture is designed for large volumes: a separate search subsystem (Elasticsearch or Meilisearch), not WooCommerce with 200 products.
- Product import from supplier feeds — the initial catalog launch is automated. We train your manager to maintain it.
- Technical specs on the product card — manufacturer, OEM number, country of origin, compatible models. Not just "name + price."
Payment and delivery
- LiqPay and Fondy — cards, Apple Pay, Google Pay, installments (Monobank, PrivatBank) for pricier parts.
- Nova Poshta API — cost calculation, tracking in the buyer's account. Heavy parts — a separate freight delivery scenario.
Case studies
Working catalogs with tens of thousands of listings
How we work
Supplier price lists become a working catalog
Brief and specification
30 minutes on Zoom: how many SKUs, which suppliers (RDM, Microcat, your own feeds), the logic for prices and stock, whether you run 1C, the logic for VIN search. The result: a spec with the catalog architecture, a list of integrations, a budget range, and timelines.
Architecture and design
We design the catalog structure for a large SKU volume. Wireframes: home page, navigation tree, product card with selection, cart, checkout. The result: approved layouts for desktop and mobile, plus a fixed price for the full scope.
Search subsystem development
The hardest stage: setting up search with selection by VIN and make/model, and indexing the large catalog. In parallel — the base site development. The result: working search on staging, tested on real queries.
Integrations and catalog population
We connect supplier feeds, 1C/BAS, LiqPay/Fondy, Nova Poshta. The first catalog import from a feed is automatic. The result: a store on staging with a real catalog and configured integrations.
Testing and launch
We test with live transactions, check VIN search on real queries, and verify real-time stock updates from 1C. The result: a live auto parts store, a manual for the manager, and 30 days of free support.
Numbers
years in business
SKUs in the catalog
weeks — to launch an auto parts store
FAQ
Answers to questions about parts online
Let's talk
Name your suppliers and volume — we'll price the store
- 30 minutesOne-on-one online
- Flexible formatVideo or phone call
- Solution-focusedPractical answers
More detail
Auto parts store e-commerce: why it's a class of its own
An auto parts store is the most technically complex segment of e-commerce, and it's not about the number of SKUs. It's that the buyer doesn't know exactly what they need. They know their car. So the entire site architecture is built not from the product catalog but from the automotive one: make → model → year → trim → compatible parts. Or a shorter path — the VIN, from which the system pulls all the car's parameters itself.
VIN search is the key competitive edge for an auto parts store. The buyer enters the 17 characters from the vehicle document and sees only what fits their exact car. This removes most selection errors and, in turn, cuts the number of returns. The implementation requires a separate database of VIN → car specification mappings and correct mapping to the product catalog. To see how it works live — in our case studies. Portfolio →
Suppliers and feeds are another feature of the niche. Many auto parts stores in Ukraine work with RDM and Microcat as their main distributors. Both supply regular feeds with prices and stock. We connect these feeds, set up auto-updates and field mapping to your catalog's structure. If there are several suppliers, the system picks the most favorable price or shows all options with the supplier named. This is a standard part of the scope of work, not a "paid extra."
Sync with 1C or BAS — for stores that keep their own inventory records. Two-way: an order from the site is recorded in 1C, and a change of stock or price in 1C updates the site at once. Without sync, you'll be selling products you don't have, or tracking two ledgers by hand. More on e-commerce integrations — on the main service page. Turnkey e-commerce stores →
The search subsystem for a large catalog is an architecture topic of its own. Standard SQL search over 300,000+ SKUs gives an unacceptably slow response. We connect Elasticsearch or Meilisearch: full-text search by part number, OEM number, and name, with suggestions and tolerance for typos. A buyer who typed "berings" instead of "bearings" still finds what they need — and buys. In large parts stores, exactly this kind of solution runs under the hood.
That's exactly why the honest timeline for an auto parts store is 8–10 weeks, not 6. Not because we work slowly, but because the VIN database, search, supplier feeds, and 1C integration are four separate technical systems, each of which needs configuration and testing. If a contractor promises an auto parts store in a month, either there won't be VIN search, or the catalog will launch without real data. Let's talk — in 30 minutes we'll tell you which architecture fits your auto parts store. Get an estimate →