Not Using ImportYeti Is the New Not Using AI. Here Is the Full Playbook.

The Number That Changed How I Source.

This is not a platform review. This is what happened when I actually used it.

A few years back I was paying $5.50 a unit landed from a U.S. manufacturer for a paper product. I moved it to a factory in China at $2.80 landed. Forty-nine percent reduction in cost. Returns dropped from 3 percent to 0.3 percent. The product got better and got cheaper at the same time.

That move was not AI-assisted. I researched it manually. I typed competitor names into ImportYeti, read every supplier page, cross-referenced on Alibaba, checked addresses on Google Maps satellite, sent emails. Old-school diligence with a free modern tool. Two factories replied. One quoted. One is still our manufacturer.

I signed up for ImportYeti in 2021, close to when it launched. Early paid subscriber. In 2024 and 2025 I started layering ChatGPT on top of the data: pasting supplier lists, asking it to classify factories versus trading companies, building outreach drafts. Now I do the same thing in Claude, which handles larger data sets and longer histories more cleanly. The platform did not change. What changed is that AI turned a research tool into an intelligence system.

I have used ImportYeti to research paper, ceramics, wood, puzzles, textiles. I have pulled competitor sourcing across the gift and puzzle space. I have bookmarked suppliers across all those categories and tracked how they move between customers over years. The intelligence I have built using public data and a free tool would have cost fifty thousand dollars a year on Panjiva five years ago.

That is what this article is about. If you run a gift or lifestyle brand and you are not in ImportYeti every time you evaluate a new product, a new supplier, or a competitive threat, you are working without data your competitors may already have. Not using it right now is exactly like not using AI two years ago. You will feel it eventually. This is the playbook.

The intelligence I have built using public data and a free tool would have cost fifty thousand dollars a year five years ago. Most brands in this space still do not know it exists.

What You Are Actually Looking At.

This data did not come from hackers. It has been public since 1930 and it is sitting there waiting for you.

Every time a container ship docks at a U.S. port, the cargo manifest is filed with U.S. Customs and Border Protection. Under the Tariff Act of 1930 (19 U.S.C. Section 1431) and the CBP regulation at 19 CFR Section 103.31, most of that manifest is a matter of public record. The disclosure regime was codified in 1981. It is not a data leak. It is the law.

ImportYeti founder David Applegate built a system during COVID downtime that indexed roughly 70 million of those records, cleaned and structured them, and made them searchable for free. Bootstrapped, unfunded, around three employees. Launched in 2020, went viral on Reddit. It is now the standard free tool for any operator who sources internationally.

What is in a bill of lading. The shipper name and address (your competitor's supplier), the consignee name and address (the brand or its 3PL), the notify party, vessel and voyage, port of loading, port of discharge, container number, piece count, weight, and a product description. ImportYeti layers on an HS code classification from the description text and builds a chart from the history.

What you see on a company page. Every supplier used since 2015, volume by supplier, country breakdown, port mix, shipment frequency, shipment descriptions, and a time-series chart of activity. You can see whether a brand is growing or declining from their import volume. You can see when they switched factories. You can see which factories serve multiple competitors in your category. You can map the entire supply chain of a product you are about to launch, in about forty-five minutes.

Free vs paid. The free tier covers unlimited company and supplier searches, the full dataset back to 2015, and all charts and visualizations. You can see everything. The paid tier (around fifty dollars a month for the sourcing plan) unlocks CSV export and Power Query. CSV export is the door that lets you take the data somewhere interesting, specifically into an AI.

Platform snapshot

How Experienced Operators Read It.

The data is public. What you do with it is not obvious. This is the operator read.

Search the LLC, not the brand. Brands almost never import under their consumer-facing name. Step zero is finding the legal entity. Run the brand name through the USPTO trademark database and read the owner field. Or check the brand's terms of service or privacy policy, which usually discloses the legal company name. ImportYeti will then find every shipment filed under that entity, including name variants and alternate spellings that show up at the bottom of the company page.

The neighbor brand technique. Once you find a supplier, click into their ImportYeti page and read their full U.S. customer list. A factory that ships to multiple premium brands in your category has been independently vetted by each of them. That is a tier-one signal. A factory you find in ImportYeti that serves only one brand you have never heard of is a different conversation. The neighbors are the quality signal, not the factory's own claims.

Read the volume over time. Shipment frequency tells you how a brand manages inventory and whether their business is healthy. A regular monthly rhythm means a stable factory relationship and a working reorder cycle. A long gap followed by a volume burst often means a supplier swap, a quality dispute, or a product pivot. I have spotted competitor pivots from import data months before any public announcement.

FCL vs LCL. Full container load versus less-than-container-load shipments are now visible on ImportYeti. LCL shipments are small orders. A factory that regularly ships LCL to brands your size will almost certainly accept small initial orders from you. FCL-only factories have real MOQ floors. This single data point can save you weeks of back-and-forth with factories that were never going to take your volume.

Country mix over time. The year-over-year country breakdown shows whether a brand is China-only, actively diversifying into Vietnam or India, or already running a multi-country supply chain. One important note, and Applegate has said this publicly: many Vietnam and Cambodia shipments are Chinese goods finished or relabeled offshore to avoid Section 301 tariffs. Do not read a Vietnam supplier as confirmed China decoupling without verifying the finished-goods production location.

Watch for the swap. A supplier that represents more than 15 percent of a brand's volume and then disappears within 60 days, with a new supplier of similar product appearing in roughly the same timeframe, almost always means a quality dispute, an IP issue, or a deliberate sourcing upgrade. That pattern shows up clearly in the timeline view.

The neighbors are the quality signal. A factory serving three premium gift brands has been vetted three times. Read the customer list before you read anything else.

The Factory vs Trader Problem. And How AI Solves It.

Roughly 30 to 50 percent of Alibaba "manufacturers" are trading companies. The same problem exists in import records. This is where AI earns its place.

The most valuable thing ImportYeti shows you is not the brand. It is the factory behind the brand. But there is a persistent problem. A significant portion of the shipper names in import records are not factories. They are traders, agents, or sourcing middlemen presenting themselves as manufacturers. The commission they collect is invisible in the data, but it is real and it is coming out of your eventual cost structure.

Name alone does not always tell you the difference. This is where AI becomes the actual tool.

I use Claude, ChatGPT and Manus in combination. I do not pick one and stay there. I run the same supplier data through all three. Claude is better for large-context full-history analysis. ChatGPT Data Analyst is faster for charting and quick tables. Manus is useful when the research needs to reach across the open web and combine with public business registry data or Alibaba listings. I make them check each other. The disagreements between models are often where the real insight sits.

The workflow: export the supplier CSV from ImportYeti (paid plan) or copy-paste the supplier table from the free-tier company page. Feed it into the prompt in the next section. Ask for factory versus trader classification, a confidence score, and the top three signals behind each verdict. Take the likely-factory list, cross-reference each on Alibaba, pull the address on Google Maps satellite to confirm it is a real industrial building, and find a named sales contact on LinkedIn before you send anything.

That process used to take two weeks and a sourcing trip. With AI it takes an afternoon.

Factory vs trading company signals

Yiwu address is almost always a trader. The city is China's largest small-commodity wholesale hub and very few actual manufacturers are based there. Shenzhen CBD addresses are high-probability agents or sourcing reps. Dongguan, Chaozhou, Ningbo, Foshan, Cixi: real production clusters. Karur, Tamil Nadu: real home textile production. Jaipur, Rajasthan: real block-print production. Sivakasi, Tamil Nadu: real safety matches. If the address is in the right city for the product and the building is in an industrial park, you are probably looking at a factory.

Prompts to Steal.

These are the actual prompts I run. Copy them, replace the placeholders, drop in your ImportYeti data, and run across Claude, ChatGPT or both.

The way I work: paste or export the supplier list from ImportYeti, run the same prompt through Claude and ChatGPT, compare the outputs, and use the disagreements as starting points for deeper research. Manus comes in when I need the AI to independently verify something on the open web, like checking whether a Ningbo address actually shows as a factory on Baidu Maps or whether an Alibaba profile matches what the BOL says.

Six prompts that do the most work.

Prompt 01. Classify factory vs trading company

Classify each supplier below as FACTORY, TRADER, AGENT, or UNKNOWN. For each give: the classification, confidence (high / medium / low), and the single strongest signal that drove it. Signals to use: name ending with Manufacturing, Industrial, Factory, or Works leans factory. International, Trading, Import Export, or a brandable English name leans trader. Yiwu address is almost always a trader. Shenzhen CBD or Hong Kong office leans trader. Dongguan, Ningbo, Chaozhou, Foshan, Karur, Sivakasi in the address leans factory cluster. Narrow product range across shipments leans factory. Broad unrelated products across shipments leans trader. Output: one table with Supplier / Classification / Confidence / Strongest signal. Below the table, list your top three factory picks and one sentence on why each is worth pursuing first. [paste supplier data here]

Prompt 02. Competitor sourcing brief

Below is shipment data for [COMPETITOR] from ImportYeti. Each row or entry is one shipment. Answer five questions. One short paragraph each. Use only what is in the data. Name names, cite dates, quote product descriptions where useful. 1. Who are their top three suppliers by shipment count and what does the concentration tell me about how they source? 2. Is there a single-supplier risk, meaning one factory above 40 percent of their shipments? 3. Based on the product descriptions, what are they actually importing? 4. Does the cadence from their top supplier look stable and ongoing, or has something shifted recently? 5. Has any supplier appeared or disappeared in the last six months? If the data is not sufficient to answer a specific question, say so. Do not fill gaps with assumptions. [paste shipment data here]

Prompt 03. Head-to-head comparison

Below are supplier lists from ImportYeti for two companies over the same time period. Compare them on four things and write one paragraph per point. 1. Which brand imports more and by roughly how much, based on shipment count or weight? 2. Do they share any suppliers? Name any overlap and note what volume each sends there. 3. What does each brand's top supplier suggest about their production tier? 4. Who looks more exposed to a single country or a single factory? No scorecard. No bullet summary. Just the four paragraphs. If the data is thin on any point, say so. [COMPANY A data] [COMPANY B data]

Prompt 04. Supplier due diligence

I am evaluating a potential supplier. Below is everything visible on their ImportYeti page: their name and address, their U.S. customer list with shipment counts, their shipment history, and their HS codes. Four things I need: 1. Factory or trader? Give three specific signals from the data that led to your verdict. Not general rules. Signals from this specific record. 2. What does their customer list tell me about the types of brands they work with and what production tier they serve? 3. Is their business stable, growing, or declining based on the shipment timeline? 4. What are the three things I should verify before contacting them, and where exactly should I go to verify each one? Do not infer anything that is not supported by the data. Flag anything that is unclear. [paste ImportYeti supplier page data here]

Prompt 05. Supplier outreach shortlist

Below is a supplier list from ImportYeti for [CATEGORY]. I need to know which ones to contact first. My context: [one sentence on your brand, approximate order volume, what you are sourcing]. Rank them using these factors in order of importance: active shipments within the last 90 days is the most important. Then total shipment volume as a proxy for capacity and experience. Then whether their visible customers include recognizable mid-market brands rather than mass retail only. Then whether their address is in a known production cluster rather than a trading city like Yiwu or Hong Kong. For your top five picks, give me one specific data point from their record that I can reference naturally in a first email. Not generic. Something real from what is in the data: a customer they ship to, a product description, a shipment pattern. [paste supplier list here]

Prompt 06. Spot new product launches

Below are shipment descriptions from [COMPETITOR]'s ImportYeti record in chronological order with dates. Three things I want to know: 1. What product categories are they importing? Group and label them. 2. Has anything new appeared in the last 90 to 120 days that was not in the earlier record? 3. Has anything that was shipping regularly gone quiet in the last six months? The strongest launch signal is a new product description combined with a new supplier name both appearing for the first time in the same recent window. If you see that combination, flag it first. For every new item flagged, one sentence: what it appears to be, when it first showed up, and whether the shipment count suggests a test or an actual launch. [paste shipment description history here]

The Category Cheat Sheet.

What to search, which cluster to expect, and what the data actually means for each category we work in.

Each product category in the gift and lifestyle space has its own manufacturing geography. Knowing which city to expect when you look up a supplier is one of the fastest ways to validate whether a name in ImportYeti is a real factory or a trading intermediary.

What It Cannot Show You.

This section is as important as the rest. ImportYeti is powerful. It is not omniscient. Know the limits before you act on the data.

Air freight is invisible. The public-disclosure statute covers ocean vessel manifests only. Air freight, rail and truck are not included. A brand that ships by air leaves no trace in ImportYeti. If a competitor has no ocean import history but clearly has product in stores, they are either manufacturing domestically, using air freight, or have filed a confidentiality request.

DDP shipments erase the supplier. When a brand buys on Delivered Duty Paid terms, the supplier becomes the importer of record and their name goes on the BOL as the consignee, not the brand. A significant portion of small DTC brands use DDP, especially on early production runs. This can make a brand look invisible in ImportYeti even when they are actively sourcing.

Domestic manufacturing leaves no trace. U.S.-made product does not generate an ocean BOL. Brands that manufacture in the U.S., Canada, Mexico by truck, or EU domestically are outside the dataset unless they also import components.

Confidentiality requests remove brands entirely. Free to file, twenty-four hours, two years of coverage. If your competitors figure this out and you do not, your sourcing is visible to them while theirs is hidden from you. Worth knowing.

What the data cannot tell you even when it finds the right factory. Quality is not in the BOL. MOQ for new accounts is not in the BOL. Whether they will respond to your email is not in the BOL. Lead times, defect rates, communication speed, certifications, ethics and compliance are all invisible. ImportYeti finds the factory. What happens next is still your job.

Transshipping distorts country signals. Many shipments that appear as Vietnam or Cambodia origin are Chinese goods finished or relabeled offshore to avoid Section 301 tariffs. A Vietnam supplier in your search results is not automatic proof that a brand has meaningfully diversified its China exposure. Verify the finished-goods production location separately before drawing supply-chain conclusions.

Naming inconsistency is real. The same supplier may appear under four different name spellings across different BOL records, especially when different freight forwarders file the document. ImportYeti does some de-duplication but it is not perfect. When building a complete supplier picture, check the "other names" section on every supplier page and run partial-name searches.

The argument

Two years ago, the brands that ignored AI lost ground to the ones that did not. Not because AI was magic. Because information compounds and operators who got efficient earlier built faster. The same thing is happening right now with sourcing intelligence.

ImportYeti is free. The data is public by law. The AI tools that turn it into actionable intelligence cost twenty dollars a month. The sourcing firms that used to charge five thousand dollars for a category analysis are doing the same thing with the same data. They just got there first.

In our own brands, we moved a paper product from $5.50 landed to $2.80 landed. Returns dropped from 3 percent to 0.3 percent. We found that factory in an afternoon using ImportYeti and Claude. That margin improvement compounded forward into every unit we have shipped since. That is the case for treating this as infrastructure, not a project.

Open ImportYeti. Start with your three closest competitors. The data has been there since 2015.

Sources

ImportYeti / About Page David Applegate founder origin story, COVID-era build, "I am the Yeti" framing, mission statement on cost and accessibility. / importyeti.com/about

ImportYeti / FAQ LLC vs DBA search guidance, how to find the legal company name, confidentiality explanation, data sourcing explanation. / importyeti.com/faqs

ImportYeti / Product Sourcing Pricing Current plan structure: free forever tier, $50/mo sourcing plan, enterprise $1,000+/mo, CSV and Power Query gating. / importyeti.com/pricing/product-sourcing

19 CFR § 103.31: Information on vessel manifests and summary statistical reports The operative regulation governing public disclosure of cargo manifest data, codified via T.D. 81-168 (1981). Subsection (a)(3): "All the information appearing on the cargo declaration of the inward vessel manifest may be copied and published." / law.cornell.edu/cfr/text/19/103.31

19 U.S.C. § 1431: Tariff Act of 1930, Section 431 Statutory basis for mandatory manifest filing and public disclosure. / uscode.house.gov

CBP / Confidential Treatment of Vessel Manifest Data CBP guidance on how importers file confidentiality requests: free, 24-hour processing, two-year validity, no justification required. / help.cbp.gov/s/article/Article-1108

MyWifeQuitHerJob / Episode 453, David Applegate of ImportYeti Founder interview. Applegate on how competitors use import data to negotiate pricing, the "Real Value LLC" / Simple Modern example, and how brands can hide their supply chain. / mywifequitherjob.com/episode453

Tool or Die / David Applegate Profile"Basically building the Bloomberg terminal for international shipping." Origin story, product direction, market positioning. / toolordie.com

NPR Planet Money / February 2023 ImportYeti featured as a notable data-transparency tool. Applegate's "I am the Yeti" quote. / npr.org/transcripts/1155050327

Federal Reserve / FEDS 2021-066, Bill of Lading Data in International Trade Research Academic analysis of CBP AMS bill-of-lading data fields. Confirms which fields are most and least reliably populated across the dataset. / federalreserve.gov

QualityInspection.org / How to Hide Your Supplier Name from Import Genius and Panjiva Three documented methods for obscuring supply chains: "to order of" BOL, HK consignee, CBP confidentiality filing. / qualityinspection.org

ImportGenius / ImportGenius vs ImportYeti Comparison Feature and coverage comparison between the two platforms. ImportGenius: from $229/mo, daily data updates, 25+ countries, AI Genius Profiler. / importgenius.com/comparison/importyeti

Leo Paper Group / Hong Kong Book Fair Pavilion Profile Heshan, Guangdong facility details, ~10,000 employees, Vietnam production expansion (LPV), product capability: books, puzzles, board games, gift packaging. / bookfairhkpavilion.com

Cre8 Direct (Ningbo) Co., Ltd. / Official Website Ningbo Beilun and Anhui facilities, product lines: journals, notebooks, planners, puzzles. / cre8direct.net

Heros Fashion Private Limited / IndiaMART Profile Jaipur, Rajasthan. Block-print home textiles (Sanganeri and Bagru techniques). Bed sheets, scarves, stoles. Confirms Jaipur cluster, not Karur. / indiamart.com/herosfashion

Bellingcat / ImportYeti in the Online Investigation Toolkit OSINT community endorsement. Field notes on how investigators use ImportYeti for supply-chain tracing and entity research. / bellingcat.gitbook.io

Production Credit

Written by a serial operator who was probably in the first few hundred ImportYeti users. Early paid subscriber. We now run multiple brands through this system regularly and have done so across puzzles, paper, ceramics, wood and textiles. The cost and returns numbers in this piece are real and they are ours.

Published by the QVINTA Library.

Sources

• ImportYeti / About Page
• ImportYeti / FAQ
• ImportYeti / Product Sourcing Pricing
• 19 CFR § 103.31: Information on vessel manifests and summary statistical reports
• 19 U.S.C. § 1431: Tariff Act of 1930, Section 431
• CBP / Confidential Treatment of Vessel Manifest Data
• MyWifeQuitHerJob / Episode 453, David Applegate of ImportYeti
• Tool or Die / David Applegate Profile
• NPR Planet Money / February 2023
• Federal Reserve / FEDS 2021-066, Bill of Lading Data in International Trade Research
• QualityInspection.org / How to Hide Your Supplier Name from Import Genius and Panjiva
• ImportGenius / ImportGenius vs ImportYeti Comparison
• Leo Paper Group / Hong Kong Book Fair Pavilion Profile
• Cre8 Direct (Ningbo) Co., Ltd. / Official Website
• Heros Fashion Private Limited / IndiaMART Profile
• Bellingcat / ImportYeti in the Online Investigation Toolkit