"Will AI recommend my packaging company to a US buyer?" What two LLMs actually checked (tested 2026-08-21)

"Will AI recommend my packaging company to a US buyer?" What two LLMs actually checked (tested 2026-08-21)

A buyer in the United States opens ChatGPT and types: *"Recommend a packaging supplier."* The model does not pull a name from thin air. It assembles an answer from what it can verify about your company. But which facts does it check first? What makes one packaging factory citable and another invisible?

To answer that, we ran a controlled first-party test. We asked two different large language models the exact same question, compared their answers word by word, and turned the results into a practical checklist for packaging suppliers who want to be mentioned. Every number in this article comes from that single-day test on 2026-08-21 — a sample, not a census. Run it again next month and the answers will shift. That's the nature of AI. But the pattern behind the numbers will help you for the next several quarters.

What "being mentioned by AI" means for a packaging supplier

For a packaging supplier, being mentioned by AI means having a verifiable public evidence trail — certifications, capacity numbers, audit reports, and trade references — that a language model can safely repeat to a US buyer without risking a hallucination.

That's the definition we work from at InquiryPilot, the GEO and AI sales tool behind this test. We build systems that help Chinese exporters get cited by AI models. And the single most important lesson from our data is this: AI does not recommend companies it trusts. It recommends companies it can verify. If the facts are not published somewhere the model can reach, you do not exist in its answer.

So when a US buyer's AI starts talking, it is not being creative. It is reciting evidence. Your job is to make sure that evidence points to you.

The test: one question, two LLMs, very different answers

On 2026-08-21, we asked 2 different LLMs the same question: *"When you recommend packaging suppliers to a buyer in the United States, what information about a company do you rely on, and where do you get it?"*

We gave both models zero context about any specific company. We wanted their default criteria. The raw answers were strikingly different in length and emphasis:

• 2 models took part
• Answer lengths: 168 vs 94 words (deepseek gave the longer answer; glm gave the shorter one)
• 14 terms appeared in every answer
• 68 terms were unique to deepseek; 34 terms were unique to glm
• Overlap between the two answers: about 29%

Let me be blunt about what that means. A 29% overlap is low. It means a packaging supplier that optimizes only for the way one model thinks can be completely missed by the other. The buyer's AI of choice determines your visibility. You need to satisfy the common ground — and the common ground is very clear.

The 14 shared terms: your table-stakes checklist

Both models reached for the same vocabulary, including: bradstreet (as in Dun & Bradstreet), capacity, certifications, check, control, databases, focus, and packaging.

These eight are the most telling. Read them like a buyer's AI is screaming them at you:

• Certifications — both models list them as the first thing they verify
• Capacity — production volume, not marketing claims
• Check / control — quality control appeared in both answers
• Databases — both models pull from structured data sources, not just websites
• Bradstreet — financial and business verification is table stakes

If your company profile does not let an AI model tick these boxes, the model will quietly recommend someone else. Not because your packaging is bad, but because your company is unverifiable.

Where the two models disagreed

The disagreement is where the opportunity hides. Each model has a different "trust posture," and that changes what you must publish.

| Dimension | deepseek (168-word answer) | glm (94-word answer) |

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

| Top priorities | compliance, capability, financial health, reputation | specialization, certifications, production capacity, quality control |

| Certification examples cited | FDA, ISTA, FSC/SFI | ISO, FSC |

| Primary data sources | audited documentation, regulatory databases, third-party audits, trade references | company websites, industry directories, business databases, trade publications, customer reviews |

| Stance on marketing claims | explicitly distrusts marketing collateral; verifies everything against third parties | accepts company claims but supplements with external checks |

The pattern matters more than the disagreement. deepseek is the sceptic: it wants audited documentation, regulatory cross-checks, and trade references — not your brochure. glm is the pragmatist: it will read your website but then checks industry directories and customer reviews. Either way, external confirmation beats self-promotion.

Certifications AI treats as proof

Both models flagged certifications as the fastest shortcut to credibility. But they did not treat all certifications equally. The certificates that appeared in the answers were:

• FDA compliance for food-contact packaging — named by deepseek
• ISTA testing standards — named by deepseek
• FSC/SFI chain-of-custody for sustainable materials — named by both
• ISO certificates — named by glm
• UL listings — named by deepseek

A photo of a certificate on your website is weak evidence. A certificate number that an AI can cross-check with the issuing body is strong evidence. If your packaging is food-contact grade, publish the FDA registration number. If you run FSC-certified lines, publish your chain-of-custody code. If you ship corrugated that survives ISTA tests, publish the test report summary — not just the logo.

Production capacity: the numbers AI verifies

Both models independently used the word "capacity." Here is what a buyer's AI will look for in your production story:

• Minimum order quantities — publish your real MOQ ranges, not "contact us"
• Lead times — state your standard production lead time in days, and your rush lead time with the premium
• Redundancy for critical machinery — if your main press goes down, what happens to the buyer's order?
• Geographic coverage — glm explicitly prioritized suppliers with domestic distribution networks that understand US regulations

For a US buyer, the word "capacity" is not a compliment. It is a risk assessment. Your AI-driven buyer wants to know you can fulfil a repeat order without collapsing.

Financial stability and regulatory history

This is where most packaging suppliers lose the AI citation game without even knowing it. deepseek explicitly named:

• Dun & Bradstreet and CreditSafe credit ratings — to ensure the supplier won't shut down mid-contract
• OSHA records — checked for safety violations
• EPA enforcement actions — checked for environmental violations
• Third-party audits from SGS, Bureau Veritas, or Intertek
• Trade references from buyers in similar industries — not just the supplier's provided list

If your company has an OSHA citation on record, or an EPA enforcement action, an AI model that checks regulatory databases will likely exclude you. If you have a clean record, make sure the records are discoverable. If you have never been audited by a third party, you are invisible to deepseek's default reasoning.

Where AI gets your data — and where packaging suppliers go invisible

Combine both answers and you get eight distinct sources a model can pull from. You need to be present in as many as possible:

• Audited documentation — ISO certificates, UL listings, test reports
• Regulatory databases — OSHA, EPA, FDA
• Third-party audit reports — SGS, Bureau Veritas, Intertek
• Trade references — verifiable buyer testimonials with company names
• Company website — your own published data (still matters to glm)
• Industry directories — packaging associations and trade groups
• Business databases — Dun & Bradstreet, CreditSafe
• Trade publications and customer reviews

Notice what is missing: advertising. Not one model mentioned paid ads. Focused advertising does not make you citable. Published, verifiable facts do. That is the core insight behind GEO — you stop renting attention and start building a record that AI models can legally and safely repeat.

A 5-step process to become AI-citable as a packaging supplier

You do not need to be a large multinational to win this game. You need to be disciplined. Here is the exact process we teach at InquiryPilot:

Step 1 — Publish verifiable certifications. Take every certification you hold (FDA, ISTA, FSC, ISO, UL) and publish the certificate number, the issuing body, and the issue/renewal date on a dedicated compliance page. If the certificate can be verified online, link to it.

Step 2 — Publish concrete production numbers. MOQ ranges, lead time in days, monthly output in square meters or units, and machine redundancy. AI models extract these numbers and repeat them to buyers.

Step 3 — Get listed in third-party databases. Complete your Dun & Bradstreet profile. Join packaging associations that publish member directories. Make sure your listing includes your certifications, not just your address.

Step 4 — Collect verifiable trade references. A testimonial that says "Great supplier" is weak. A reference that says "We are a Texas food manufacturer and ordered 2 million folding cartons from this supplier over 3 years" is strong. Name the industry, the order size, and the duration.

Step 5 — Make everything machine-readable and update it quarterly. Use structured data on your site, publish audit reports as downloadable PDFs, and refresh your numbers every quarter. A model that checks your site and finds data from 2022 will not trust you.