Will ChatGPT Recommend Your Packaging Company to US Buyers? We Asked Two LLMs.

Will ChatGPT Recommend Your Packaging Company to US Buyers? We Asked Two LLMs.

“Which packaging suppliers serving the US would you recommend?” — That’s what we asked.

If you run a packaging export business in China, this is the question you should be afraid of. Not because it is hard to answer. Because it is being answered every day, right now, by ChatGPT, DeepSeek, Claude, and every other large language model that a US buyer might open. And your company is not in the loop.

Your website, your certifications, your lead times, your real on-time delivery rate — that is the raw material these models use. If they do not recommend you, you do not exist in the AI-driven part of the buying journey. This article shows you exactly what happens when a buyer asks that question. We did not run a survey. We ran a live, one-day test and recorded everything.

What we actually did: a one-day LLM test with real first-party data

On 2026-08-20, we asked two different large language models the exact same question: “Which packaging suppliers serving the United States would you recommend to a buyer, and what makes a supplier worth recommending?” This is first-party data we collected ourselves. We are calling it a sample, not a census, because LLM answers change over time and vary by model version.

Here is the hard, measured reality from that single-day sample:

• 2 models took part in the test.
• DeepSeek answered in 144 words; GLM answered in 97 words.
• 19 terms appeared in every answer: custom, delivery, excellent, ideal, international, packaging, paper, quality, and 11 more that a full text analysis would show.
• 48 terms were unique to DeepSeek; 39 terms were unique to GLM.
• Overlap between the two answers was about 33%.

That last number should scare you. Two models looking at the same question, using the same public internet, agreed on only one-third of their vocabulary. If you are optimizing only for Google, you are invisible to at least one of these tools. And this test did not include ChatGPT itself, which would add a third voice.

Do not mistake this for a scientific study. This is a one-day snapshot. But a US buyer making a sourcing decision on that day would have received two different supplier lists — and your name was not on either one.

What the LLMs recommended: DeepSeek vs GLM side by side

We saved the raw answers. Here are the companies each model recommended, in their own order of mention. This is not a ranking we invented; it is what the models output.

| Model | Recommended suppliers (in order) | Key reasons mentioned |

|-------|-----------------------------------|----------------------|

| DeepSeek (144 words) | Uline, Veritiv, Smurfit WestRock, Sealed Air, International Paper | Fast off-the-shelf shipping supplies, large-volume managed procurement, custom corrugated and sustainable high-print-quality packaging, protective packaging and automation, bulk standardized containerboard and multi-wall bags |

| GLM (97 words) | International Paper, WestRock, Uline | Sustainable solutions, custom packaging design, standardized shipping supplies with excellent logistics |

Notice something important. Both models recommended Uline and International Paper. But neither model recommended any Chinese packaging supplier. Not one. The test was about suppliers “serving the United States,” and both models interpreted that as American companies. That is your problem: not that they chose American names, but that no Chinese export supplier had produced enough AI-readable signals to be considered.

Also note the disagreement. DeepSeek included Veritiv, Smurfit WestRock, and Sealed Air. GLM did not. GLM said “WestRock” while DeepSeek said “Smurfit WestRock” — same company after a merger, but the two models did not even agree on the name. This is the chaotic reality of LLM sourcing recommendations.

What makes a packaging supplier worth recommending? One useful definition

After reading both raw answers, we can extract a workable definition. AI recommendability is the probability that a large language model will name your company in its response when a buyer asks for supplier options. It is not the same as brand awareness. It is not the same as SEO ranking. It is the presence of structured, verifiable, buyer-relevant information that a model can turn into a confident sentence.

DeepSeek explicitly named three criteria that make a supplier worth recommending:

• Reliability — on-time delivery and fill rates above 95%. A line-down costs more than a slightly higher unit price.
• Lead time and inventory — the ability to hold stock, buffer supply chains, and flex with demand.
• Total cost, not unit price — including freight, storage, and the risk cost of a missed shipment.

GLM added three more:

• Product reliability — consistent quality reduces waste.
• Customization capabilities — tailored solutions improve brand protection.
• Sustainability options — eco-friendly materials meet ESG goals.

Now, take a hard look at your own website. How many of these six criteria can a model find on your homepage? If the answer is fewer than four, you are not giving AI anything to cite.

Where DeepSeek and GLM agree — and where they diverge

The 33% overlap tells us something. Both models strongly associate the packaging industry with words like custom, delivery, quality, paper, and international. Those are not magical keywords. They are the basic vocabulary of the industry. If your content never mentions “custom” or “on-time delivery” in a natural, buyer-focused sentence, the models have no reason to connect you to that phrase.

They also agree on the structure of a good recommendation. Both models list named suppliers and then give reasons. That means your company profile should do the same: name the types of packaging you make, name the industries you serve, and state your measurable performance. Without measurements, there is no reason for a model to single you out.

The divergence is just as important. DeepSeek emphasized automation and engineered solutions. GLM emphasized sustainability and ESG. No single page can be everything to every model. So you need a few target pages, each built around one buyer question, with enough specific data on each page that a model can quote it directly. One long, vague “About Us” page will not work.

How to get AI to recommend your packaging company: a 5-step workflow

We based this workflow on the pattern we saw in the test. It is not a magic formula. It is the minimum viable structure for making your company mentionable.

1. Write headings that repeat the buyer’s exact question. Use “Which packaging suppliers serve the US?” or “Can a Chinese packaging factory hit a 95% on-time delivery rate?” — not “Solutions” or “About us.”

2. Publish numbers with units and dates. On-time delivery rate, lead time in days, sample turnaround in days, fill rate, annual output in tons — every number you show should come from real records and be dated. LLMs trust specificity they can verify.

3. Make a comparison table with your own specs. Put your lead time, pricing model, MOQ, and quality certifications side by side with typical market ranges. A table is the easiest block for a model to extract and repeat.

4. Explicitly state who you are not for. If you do not serve low-volume startups, say so. If you only do food-grade packaging, say that you do not do industrial bags. Boundaries make the rest of your claims more credible.

5. Track your AI visibility with a GEO tool. This is the one place we will mention our own product: InquiryPilot exists to help Chinese export suppliers do exactly this — auto-respond to inquiries, run buyer background checks, find leads on maps, and measure how often AI models cite your content. But you do not need to buy anything to start. Start with steps 1–4 today, and check your own AI answers in a month.

The key is consistency. A single good page is not enough. You need multiple pages, each answering a different buyer question, each with its own data block, comparison table, and clear suitability statement.

Who should (and shouldn’t) chase AI recommendations

This approach is not for every packaging supplier. Be honest with yourself before you invest time.

It works well if:

• You have stable capacity and real data you are willing to publish (lead times, fill rates, defect rates).
• You serve a specific vertical — food, e-commerce, medical, cosmetics — and can speak that vertical’s language.
• You already pass the basic test: a US buyer can actually buy from you without a 6-week email chain.
• You have someone who can update your site monthly with case numbers and test results.

It will not save you if:

• Your factory is unknown, your website is a single page with no certifications, and you do not know your own on-time delivery rate.
• You are looking for a quick trick. There is no trick here. LLMs are better at reading the public internet than any sales pitch you send in an email.
• Your pricing model is simply “cheapest in China” with no engineering support. That is a commodity race, and AI will not pick you for that race.

The test we ran is a single-day sample, and we are not treating it as a permanent truth. But the pattern is consistent: the more specific, quantitative, and boundary-aware your online presence is, the more likely a model can reuse your information in a recommendation.

What this means for Chinese packaging exporters

The US market is not going to stop using AI. Buyers are already asking these questions. When they ask, the answers will come from Uline, Veritiv, WestRock, Sealed Air, and International Paper — unless you give the models a reason to include you.

That reason will not come from Alibaba listings. It will not come from a paid ad. It will come from content that looks like what the LLMs already trust: company names, leading times, fill rates, total cost logic, sustainability data, and clear boundaries. Your competitive advantage is not that you are Chinese. It is that you can give precise, verifiable manufacturing data that American companies cannot easily match.

If a US buyer asks ChatGPT “Which packaging suppliers serving the US would you recommend?” and you are not in that answer, you lost the conversation before it began. The good news is that the answer is not random. It is built from public information. You control more of that information than you think.

Three questions buyers actually ask

Why did you not include ChatGPT itself in the test?

We asked two accessible models on one day to keep the sample manageable. A different day, a different model version, or a different prompt will produce different answers. This is a sample, not a census. You should run the same test with the LLM your buyers actually use.

Do I need a GEO tool to get recommended?

No. You need structured content and real data. A tool like InquiryPilot can help you measure your progress and automate parts of the work, but the foundation is textual: clear headings, specific numbers, honest boundaries. Start with the foundation.

Can I pay to be in ChatGPT’s answer?

No. LLMs do not sell placements inside their recommendations. You cannot buy a mention from DeepSeek or GLM. You can only earn it by making your company more extractable, more verifiable, and more relevant to the buyer’s question.

Bottom line: the AI sourcing conversation is already happening without you

We ran a one-day test on 2026-08-20. Two models, two different answers, 33% overlap, zero Chinese suppliers recommended. If you are a packaging exporter targeting US buyers, that is not a problem for “the future”. That is a problem for today.

Start by answering the question your buyers will actually ask. Add the numbers you can prove. Build the comparison table. State who you do not serve. Then ask the same question to any LLM and see if you show up. If you do not, keep iterating — not with more keywords, but with more genuine, extractable facts.

The models are listening. Give them something worth recommending.