🚢 ImportYeti Scraper — US Customs Importer & Supplier Data

🤖 Structured supply-chain intelligence in seconds. Pull every public datapoint from ImportYeti — importer addresses, country-of-origin breakdowns, monthly shipment history, top HS codes, trademarks and tags — straight into a JSON / CSV / Excel dataset.

✨ Why this scraper

• ⚡️ Fast — concurrent extraction, 5 records / second under default settings
• 📦 Complete — every visible field from each company/supplier page in one flat record
• 🏷️ Both sides of the trade — US importer "companies" and their foreign "supplier" counterparts
• 🌍 Country-of-origin rollups — pre-aggregated per country & continent
• 📈 Monthly history — shipments, weight (kg) and TEU broken down per month
• 🔁 Incremental mode — since: parameter skips records older than your last run
• 🧾 3 export formats — JSON, CSV, Excel

🚀 Quick start

1. Click Try for free
2. Type a search term (a company name, brand, product, or keyword) — or paste a list of /company/<slug> / /supplier/<slug> URLs
3. Hit Start and grab a coffee
4. Download the result as JSON, CSV, or Excel from the Storage tab

🛠️ Input

Field	Type	Description
query	string	Free-text search term (brand, importer, HS-code keyword)
entityType	enum	company (US importer), supplier (foreign manufacturer), or both
startUrls	string[]	Optional explicit list of /company/<slug> or /supplier/<slug> URLs
maxItems	integer	Hard cap on records; 0 = unlimited (default 50)
since	datetime	Skip records whose most recent shipment is older than this ISO timestamp
concurrency	integer	Parallel HTTP requests (default 5, max 25)

📤 Sample output

{
  "url": "https://www.importyeti.com/company/apple",
  "id": "company/apple",
  "scraped_at": "2026-05-10T20:00:10Z",
  "type": "company",
  "title": "Apple",
  "address": "568 Aldi Blvd, Mount Juliet, Tn 37122, Us",
  "country_code": "US",
  "phone_number": "XXX-XXX-X000",
  "website": "apple.com",
  "other_names_count": 2,
  "other_addresses_count": 89,
  "most_recent_shipment": "01/21/2026",
  "total_sea_shipments": 2449,
  "total_shipping_cost": 101746.81,
  "shipping_cost_coverage": 2.69,
  "avg_teu_per_month": 0.39,
  "avg_teu_per_shipment": 0.77,
  "database_updated": "05/06/2026",
  "multi_address": true,
  "dataset": "us",
  "uflpa": false,
  "location": {
    "state": "Tennessee",
    "county": "Wilson County",
    "city": "Mount Juliet",
    "district": "Mount Juliet"
  },
  "tags": [
    { "tag": "puter",    "shipments": 617 },
    { "tag": "computer", "shipments": 617 },
    { "tag": "lithium",  "shipments": 388 }
  ],
  "trademarks": [{ "name": "Apple", "trademarks_count": 1620 }],
  "imports_per_country": [
    { "country": "China",       "continent": "Asia",   "shipments": 2292 },
    { "country": "Hong Kong",   "continent": "Asia",   "shipments": 62 },
    { "country": "Vietnam",     "continent": "Asia",   "shipments": 37 },
    { "country": "Germany",     "continent": "Europe", "shipments": 11 }
  ],
  "top_hs_codes": ["8504.40","8517.62","8471.30","8544.42","8473.30"],
  "shipments_time_series": [
    { "period": "01/01/2024", "shipments": 32, "weight": 89421, "teu": 84 },
    { "period": "01/02/2024", "shipments": 41, "weight": 117850, "teu": 102 }
  ]
}

💼 Use cases

Who	What for
Procurement teams	Find alternative suppliers for components shipped from specific countries
B2B sales	Build prospect lists by HS code, trade volume, or country of origin
Market research	Quantify competitor sourcing strategies — who buys what, from where, how often
Investment analysts	Track import volume as a leading indicator for retail / consumer-goods companies
Logistics & freight	Identify high-volume lanes and consolidation opportunities
Trade compliance	Screen suppliers against UFLPA and other forced-labour flags

💡 Tips & tricks

• Free-text queries match titles, addresses and aliases — "apple" returns 27 hits across companies and suppliers, not just Apple Inc.
• For company-only datasets, set entityType: "company". Suppliers are foreign manufacturers and have a different shape (no trademarks, often no website).
• The since filter uses the most recent shipment date on each record — perfect for nightly incremental runs.
• Want comparable competitors? Run one query per company name, then merge — each result carries a tags array you can use for affinity clustering.
• Large runs (10k+ records) benefit from concurrency: 10 and unlimited maxItems: 0.

❓ FAQ

Q: What data is in each record? A: Every field shown in the sample above — importer name, primary and alternate addresses, phone, website, total sea-shipment count, country-of-origin breakdown, top HS codes, monthly time series, trademarks and keyword tags.

Q: How fresh is the data? A: ImportYeti's database update timestamp travels with each record in the database_updated field, so you always know how recent the underlying customs data is.

Q: Can I get individual shipment-level records? A: This actor returns the public aggregated view (count + breakdowns + monthly history). Individual bill-of-lading records aren't part of the public page surface.

Q: Will this work for non-US companies? A: Yes — the dataset itself is US sea imports, so US-side companies are importers and foreign-side companies are suppliers. Both show up.

Q: How are records de-duplicated? A: By the canonical id field (company/<slug> or supplier/<slug>) — the same company appearing on multiple search pages or via multiple startUrls is pushed once.

Q: What happens if a search slug doesn't have a real page? A: ImportYeti's search occasionally surfaces orphan slugs. The actor detects and silently skips them — they don't count against your run.

📊 Output

Three checkmarks live in the Storage tab — the dataset is exportable as JSON, CSV, and Excel out of the box. Use the Open in Apify Console link for an interactive table view with filtering and sort.

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🛟 Found a bug or want a field that's missing? Open an issue from the actor page — we ship fixes fast.