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Which auto parts suppliers in the US should I contact first?

Published 2026-08-10 · InquiryPilot research

Which auto parts suppliers in the US should I contact first?

When a US buyer types "Which auto parts suppliers in United States would you recommend, and why?" into an AI assistant, they get a confident, well-formatted list of company names. But what is actually inside that answer? Which suppliers keep appearing, which ones get left out, and how much do two different AI models even agree with each other?

On 2026-08-10, we asked two leading LLMs that exact question. The results were measured, recorded, and published as first-party data. This article walks through what they said, where they agreed, where they disagreed, and how a real buyer should use — or ignore — an AI-generated supplier shortlist. We also cover what this means for Chinese auto parts exporters who want to show up in future AI answers.

One important caveat up front: this is a single-day sample, not a census of AI behavior. AI answers change over time, by model, and even by the wording of the prompt. But the pattern is useful.

DeepSeek gave me 162 words — is that enough to pick a supplier?

DeepSeek's response ran 162 words and organized everything by purchasing scenario. It did not hand the buyer one "best" supplier. Instead it split the answer into four segments, each tied to a different kind of buyer.

The logic is practical. A budget buyer does not need the same supplier as a repair shop that needs parts on a truck tomorrow. DeepSeek also added a genuinely useful warning: "Ensure you verify part quality tiers (Economy vs Professional) before purchasing." That is the kind of caveat most human supplier directories never include.

But is 162 words enough to pick a supplier? No. It is enough to build a first hypothesis. The answer names real companies, but it does not check live inventory, current pricing, or whether any of them stocks the specific part number a buyer needs.

GLM gave me a different list in 96 words — which one do I trust?

GLM's response was shorter — 96 words — and organized by supplier strength rather than buyer type. It named four suppliers:

GLM also closed with a line about considering "volume requirements, quality standards, and technical specifications" — a reminder that supplier selection depends on the buyer's own constraints.

Now compare the two answers directly. DeepSeek named six suppliers: AutoZone, Advance Auto Parts, NAPA, RockAuto, Summit Racing, and JEGS. GLM named four: NAPA, LKQ, BorgWarner, and AutoZone. Only two names overlap: NAPA and AutoZone.

So which one do you trust? Neither of them alone. You trust the verification you do after reading both.

Why did the two AI answers only overlap about 22%?

The agreement between the two models is real but narrow. Both independently surfaced NAPA and AutoZone, which suggests those names are strongly anchored in the training data for US auto parts supply. Any buyer asking "who are the big US auto parts suppliers" will likely see those names again, regardless of which model they use.

The disagreement is more interesting. RockAuto, Summit Racing, JEGS, and Advance Auto Parts appeared only in DeepSeek. LKQ and BorgWarner appeared only in GLM. Neither model mentioned regional distributors, manufacturer-direct programs, or Chinese exporters selling into the US — which is exactly the gap that matters for international buyers.

The two models also used different logic. DeepSeek matched suppliers to buyer types (budget, workshop, performance). GLM matched supplier names to product categories. That is why the shortlists diverge: the models were effectively answering two different versions of the same question.

Term-level overlap was measured too. Of the terms that appeared in both answers, only 11 were shared — aftermarket, autozone, commercial, extensive, inventory, parts, quality, recommend, and a few others. DeepSeek used 61 terms unique to its answer; GLM used 38 terms unique to its answer. The measured overlap was about 22%.

What did the numbers show? The first-party data from this test

Here is the measured first-party data. Treat it as a sample, not a definitive measurement — AI models change quickly, and this was collected on a single day.

| Metric | DeepSeek | GLM | Test-level observation |

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

| Answer length | 162 words | 96 words | 2 models took part |

| Unique terms in the answer | 61 | 38 | 61 / 38 terms unique to each |

| Shared terms across both answers | — | — | 11 terms appeared in every answer (aftermarket, autozone, commercial, extensive, inventory, parts, quality, recommend) |

| Term overlap between answers | — | — | overlap about 22% |

| Collection date | 2026-08-10 | 2026-08-10 | Collected 2026-08-10 |

Put simply: two models answering the same question on the same day shared about a fifth of their vocabulary and only two supplier names. Everything else was unique to each model. If a buyer asked only one AI assistant, they would miss roughly half of the supplier landscape.

That matters for a practical reason. A buyer who relies on a single AI answer gets a false sense of completeness. The answer sounds confident, but it is one model's reconstruction from training data, not a market census.

Can I trust an AI shortlist, or is it just a starting point?

Let's define what an AI supplier recommendation actually is. An AI supplier recommendation is a generated shortlist produced by a large language model in response to a sourcing query; it reflects the model's training data and the wording of the prompt, not a verified vendor audit.

That definition matters, because it sets the correct expectation. An AI shortlist is useful as a starting point for three reasons, and not one of them involves blind trust:

None of this means AI recommendations are useless. It means they are a hypothesis. A buyer's job is to verify the shortlist through real procurement checks — calls, documentation, samples, and a small trial order.

How do I verify an AI-recommended supplier? 5 steps

The verification process is the same whether the name came from an AI assistant, a trade show, or a directory listing. Here is a five-step process that works in practice:

1. Ask for certification and traceability documentation — ISO 9001, IATF 16949, and country-of-origin paperwork for the specific part numbers you need.

2. Call the supplier during US business hours and ask a technical question about the part you need. Poor product knowledge is an immediate red flag.

3. Request commercial evidence — a proforma invoice, an export record, or a reference from another buyer in the same vertical.

4. Order a small sample batch first. Verify the part against your OEM specification before committing to container volume.

5. Check the supplier's digital footprint — website age, content depth, and whether Google or AI assistants even index them.

Five steps, and none of them require an AI model. The AI shortlist is useful only if it fast-tracks this verification, not if it replaces it.

Who should use this list — and who should ignore it

Transparency makes this article useful. The shortlist from this test is not for everyone.

It is useful for:

It is not useful for:

Where these numbers come from
Actual answers from 2 LLMs on 2026-08-10
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