Do AI assistants agree on how to pick a packaging supplier in the United States? We asked them

When a packaging buyer in the US sits down to shortlist suppliers, the first search they run may no longer be on Google. More and more buyers are typing the same question into an AI assistant: "How should I evaluate and shortlist packaging suppliers?" The answer they get shapes which suppliers get an RFQ — and which ones never hear from them.

That is why we ran a simple test on 2026-08-11. We asked two different large language models the identical question: "How should a buyer in United States evaluate and shortlist packaging suppliers? Be specific." Then we compared the raw answers side by side.

The short version: the models agreed on the bones of a good supplier evaluation but disagreed on depth, detail, and emphasis. Below is what they said, where they matched, where they didn't, and how a real buyer should use this without getting misled.

This article was produced at InquiryPilot (https://getinquirypilot.com) — an AI sales and GEO tool for Chinese foreign-trade sellers — because suppliers are already being judged against AI-generated checklists inside their buyers' procurement workflows.

One definition before we go further, because it frames everything below: AI-assisted supplier shortlisting is the practice of using a language model to generate and prioritize supplier evaluation criteria, then verifying every claim with documents, samples, and a pilot order before you commit.

Why AI assistants are becoming the first filter in B2B buying

AI assistants are becoming the first filter in B2B buying. A buyer who used to ask a colleague or a trade association for supplier names now asks a chatbot. The problem is that most buyers don't realize how much the answer varies depending on which model they use.

This is exactly the kind of question a US buyer asks in the early research phase, before they have a supplier list. And it's the kind of question that, if answered well, saves a procurement team weeks of dead-end vendor calls.

The stakes are not abstract. For a buyer, a weak shortlist means wasted samples, late quotes, and compliance surprises. For a supplier, missing an AI-generated criterion can mean being invisible from the start — no matter how good the product is.

How we ran the test: two models, one prompt

We tested two models — deepseek and glm — with identical prompts. The instructions were deliberately vague enough to force the models to make their own choices: no company names, no product categories, no budget numbers.

The only context both models received was that the buyer was in the United States. The prompt asked for specifics, but it did not tell the models whether the packaging was for food, e-commerce, retail, or industrial shipping. That left each model to decide what mattered most.

Where the two models agreed

Despite very different writing styles, the two models produced a shared core. In our first-party test, 15 terms appeared in every answer. The ones worth naming explicitly: capacity, certifications, check, compare, compliance, demand, evaluate, evaluating.

Both models told the buyer to:

• Define requirements up front — materials, quantities, certifications, sustainability goals
• Request physical samples before committing to a supplier
• Verify compliance with US regulations and industry standards
• Evaluate production capacity and lead times against actual demand
• Compare pricing structures beyond the unit price
• Shortlist 3–5 suppliers and conduct facility tours

If you are a packaging supplier, that list is your audit. If your website, your sales deck, and your first call don't cover these six points, an AI-generated shortlist can quietly skip you.

For a buyer, this overlap is useful in one specific way: when two independent models land on the same criteria, those criteria deserve a place on your scorecard.

The overlap is a signal, not a proof. Even so, it narrows the list of things worth investigating. The buyer still has to give each criterion a weight based on their own product category, volume, and risk tolerance.

Certifications and compliance: the one criterion both models led with

The strongest agreement was on compliance. Deepseek put it first and gave concrete detail: "Certifications & compliance — Require FDA food-contact compliance (if applicable), ISTA testing capability, and FSC/SFI chain-of-custody for sustainable claims. Verify with documents, not promises."

GLM said the same thing in fewer words: check for certifications (FDA, FSC) and "compliance with US regulations and industry standards" before anything else.

Why both models lead with this matters. Packaging is a regulated purchase in the US. Food-contact packaging needs FDA compliance; retail packaging increasingly needs FSC or SFI chain-of-custody if you plan to make sustainability claims. A supplier who says "we're compliant" without documents is a risk you cannot price into a purchase order.

The useful rule from both answers is the same: documents, not promises. Ask for the certificate number, the issuing body, and the audit date — and verify with the issuing body if the order is large enough.

Where the two answers split: depth vs. process

The biggest difference was depth. Deepseek produced a 164-word answer with specific benchmarks — 95%+ on-time delivery over the past 12 months, a defect rate below 500 ppm, and comparisons on a cost-per-packaged-unit basis. GLM produced a 100-word answer that was more process-oriented: define requirements, request samples, compare pricing structures, review quality control, shortlist, visit.

These are the measured numbers from our collected sample, gathered 2026-08-11 and described here as a sample rather than a census:

• 2 models took part in this test
• Answer lengths were 164 words vs. 100 words
• 15 terms appeared in every answer
• 70 terms were unique to one model; 41 terms were unique to the other
• Overlap between the two answers: about 27%

That 27% overlap figure is the one to remember. If two AI assistants agree less than a third of the time, a buyer who relies on a single model can miss a large part of the picture.

What low overlap means for a buyer

The low overlap does not mean one model is wrong. It means each model is making different assumptions about what a packaging buyer should care about. One model assumed measurable performance was the priority; the other assumed a repeatable process was the priority.

In practice, both are useful. The 164-word answer gives hard thresholds to put in an RFQ. The 100-word answer gives a sequence that prevents a buyer from skipping steps, such as visiting a facility before verifying a compliance certificate.

A buyer can turn this split into an advantage by treating the two outputs as two reviewers with different styles. Ask each supplier to respond to both sets of criteria. That forces suppliers to show numbers and process, not just a friendly sales call.

Building a shortlist from AI output: a practical scorecard

Start with compliance as a gate, not a weighted line item. In a simple scorecard, a supplier that cannot provide a valid FDA or FSC document is out, regardless of how low the quote is. This mirrors what both models did by putting compliance first.

For each supplier that passes the compliance gate, collect the same evidence in the same format. Ask for the certificate number, the issuing body, the audit date, the scope of the certificate, and a sample of the packaging material.

Assign weights that reflect your own situation. A food brand may give compliance 40% of the score and price 20%. An e-commerce company may give lead time and capacity 35% and compliance a 25% minimum gate. The exact numbers should come from your category, not from the AI.

Score 3–5 suppliers, as both models suggested, and use a pilot order before a long-term contract. A pilot order of a few thousand units can reveal issues that certificates and samples do not show: communication speed, production scheduling, packaging quality under real shipping conditions.

What packaging suppliers should do now

If your website, sales deck, and first call don't cover the six points both models named, an AI-generated shortlist can overlook you. The fix is not to game the AI; it is to publish the information buyers are already being told to request.

Publish certificate numbers and audit dates where a buyer can find them. State capacity in units per month, lead times in weeks, and quality metrics such as defect rates and on-time delivery percentages for recent periods. These are the details a model like Deepseek can quote back in a buyer-facing answer.

When a buyer asks about compliance, answer with documents, not descriptions. Attach the certificate, name the issuing body, and explain the scope. The more specific the evidence, the easier it is for an AI assistant to cite a supplier in a future answer.

Limitations of this test

This test is a sample, not a census. It involved two models, one prompt, and one date of data collection. The answers reflect the model versions available on 2026-08-11, not every AI assistant a buyer might use.

The answers may also change if the prompt is altered. Adding a product category, a budget range, or a company size would probably shift the emphasis. A buyer should re-run the question with their own context instead of copying a generic list.

No model in this test visited a factory, verified a certificate, or touched a sample. AI-generated criteria are useful only when followed by the slow work of checking documents, requesting samples, and running a pilot order.

The takeaway

Both models agreed on the bones: define requirements, verify compliance, request samples, check capacity, compare total cost, and shortlist a manageable number of suppliers. That agreement is a reasonable starting point for any packaging procurement review.

The models disagreed on depth and emphasis. One gave measurable benchmarks; the other gave a process sequence. A buyer who reads only one answer gets only half of the full picture.

Use AI as a starting point, not a verdict. Run the same question across more than one model, compare the overlap, and then verify everything with documents, samples, facility tours, and a pilot order. That is how a buyer can get the benefit of AI-assisted shortlisting without being misled by it.