Material & resin suppliers
For material suppliers: win the material-class decision, then the grade.
When an engineer asks which foam, elastomer or engineering polymer to use, AI engines usually answer at the material-class level first (PEBA vs. eTPU vs. supercritical EVA) and only then name grades and suppliers. Reference Grade gets your grades into both decisions.
Questions engineers ask AI
From our pilot question set
- “What foam should I use for a high-rebound running shoe midsole: PEBA, expanded TPU or supercritical EVA?”
- “PEEK vs. PEI vs. PPSU: which is best for a high-temperature, chemically resistant structural part?”
- “Which flame-retardant PC or PC/ABS grades meet UL 94 V-0 at thin walls?”
- “Which TPU grades stay flexible below −10 °C for a clear outsole?”
- “Which nylon for an under-hood part in coolant at 130 °C: PA66 GF30, PPA or PA46?”
- “Who are the leading suppliers of TPU for footwear, and what are their flagship grades?”
What AI cites today
What our pilot found
52 engineering questions put to ChatGPT and Claude on October 14, 2026, with every cited source traced.
Class answers often name nobody
Asked about midsole foams, Claude compared PEBA, eTPU and supercritical EVA in detail and named no supplier or brand at all. The first decision engineers get help with is the material class.
Category leaders can go unnamed
Across 24 footwear answers, one supplier's TPU brands appeared in 10. The brand most associated with PEBA appeared in none, even though both engines recommended PEBA for maximum rebound.
Grade-level pages win compliance questions
For UL 94 V-0 questions, both engines cited suppliers with crawlable, one-URL-per-grade product pages. Other suppliers were represented by distributors' copies of their datasheets.
Brands drift free of their owners
Trade names are used as shorthand for a whole class (“TPV, e.g. Santoprene”), often without the owner's name. One owner of several such brands was never named in 156 answers.
Who gets cited on these questions
Share of cited URLs by source type, ChatGPT and Claude pooled
Table view
| Supplier pages | Manufacturers & service providers | Market research & press wires | Patents | Academic papers | Trade press, databases & reference | Other | Total | |
|---|---|---|---|---|---|---|---|---|
| Engineering polymers | 49% (45) | 12% (11) | 0% (0) | 4% (4) | 3% (3) | 8% (7) | 24% (22) | 92 |
| Footwear materials | 31% (35) | 3% (3) | 2% (2) | 17% (19) | 28% (31) | 8% (9) | 12% (13) | 112 |
| Elastomers & composites | 23% (21) | 13% (12) | 0% (0) | 10% (9) | 16% (15) | 13% (12) | 24% (22) | 91 |
What we do
How we change the answer
- A question panel by application and grade family, benchmarked against your competitors.
- HTML datasheets for priority grades, plus selection and comparison guides that win the class decision.
- Spec Watch on your top grades and certifications.
- Access fixes: product-finder crawlability, PDF/HTML parity, duplicate regional sites, robots and firewall rules.
- Launch support: new grades seeded into retrieved answers and measured after launch.
What you get
- Share of Answer and Citation Share by application and competitor
- A Source Map of the pages behind every answer
- Spec Watch flags with the source of each error
- A prioritized content roadmap, re-measured quarterly
Start with evidence
See what AI tells engineers about your materials.
We'll run 25 of your customers' questions across three engines and show you who gets named, which sources get cited, and what's wrong. Free, in five business days.