By industry
Nine industries, nine different fixes
Find the one closest to yours. If it isn't listed, that's fine. The method is the same; only the critical data fields change by industry. If you're unsure, start with the audit and we'll show you, with test results, exactly where you're stuck.
Medical devices / lab consumables
Makers of consumables, device components and lab supplies
Why AI skips you: Buyers in this field screen for compliance before they talk price. Your CE, FDA and ISO 13485 information sits in PDFs and certificate images. AI can't confirm your credentials, so it drops you from the shortlist.
- Structured compliance data: certification type, number, expiry date and product scope, every field modeled
- Spec modeling: materials, sterilization method and applicable standards, each published as HTML
- Application coverage: content organized by clinical department and lab workflow
Recommended plan: Growth
Specialty coatings / additives
Makers of coating resins, matting agents, rheology modifiers and dispersants
Why AI skips you: Buyers search for a specific model, such as an "OK 412 alternative." AI only recommends the original maker, and you never make the shortlist.
- Competitor-model mapping library: each best-selling big-brand model mapped to your grade
- System compatibility table: waterborne / solventborne / UV-cure × recommended grade
- Performance content: matting efficiency, clarity, feel, scratch resistance
AI test in this industry: for a defoamer/dispersant question, all six suppliers named were US or European
Recommended plan: Growth
Rubber additives / chemicals
Fine chemical makers of accelerators, antioxidants and vulcanizing agents
Why AI skips you: The category is highly commoditized. AI can't tell suppliers apart, so it ranks by brand recognition, and a challenger inevitably lands near the bottom.
- Technical differentiators: purity, ash content, initial melting point and other verifiable metrics
- Formulation content: dosage and results across rubber types and processes
- Third-party cross-verification: placement in industry technical media and professional communities
Recommended plan: Growth
Feed additives / bio-based products
Producers of enzymes, carriers and probiotics
Why AI skips you: Overseas buyers ask in precise English technical terms (heat-resistant carriers, coating processes). If your technical documentation is in another language or uses different vocabulary, AI can't use it.
- Multilingual terminology mapping, aligned with the language buyers actually use
- Structured tolerance and stability data (heat, acid, shelf life)
- Regulatory and certification content: market-access information for each target market
AI test in this industry: for a heat-stable phytase question, AI named only three US and European brands
Recommended plan: Growth
Precision machined parts
Precision machining, tooling and special-purpose part makers
Why AI skips you: Custom, non-standard work is hard to search for, so AI can't match your machining capabilities to a buyer's specific need.
- Capability matrix: material × tolerance × process × capacity
- Structured application cases, so AI can recommend you by use case
- Certifications and quality systems turned into citable source content
AI test in this industry: for a precision fastener question, AI named Bossard and Bufab
Recommended plan: Foundation
Machinery / industrial equipment
Makers of complete machines, production lines and special-purpose equipment
Why AI skips you: Equipment is a high-ticket sale with a long decision cycle. Buyers check specs and suitability with AI again and again, but AI can't read your technical documentation.
- Structured equipment specs and operating-condition parameters
- Application industry × capacity × configuration, modeled as a comparison
- After-sales and spare-parts capability as source content, to answer long-term concerns
Recommended plan: Growth
Electronic components / modules
Makers of connectors, modules, PCBA and passive components
Why AI skips you: Buyers select by datasheet and search by part number. Your part numbers simply don't exist in the source content AI reads.
- Part numbers and specs modeled field by field
- Cross-reference relationships to major international manufacturers' part numbers
- Structured certification and compliance data (RoHS / REACH / UL)
AI test in this industry: for a connector question, all nine brands AI named were US or European
Recommended plan: Growth
Consumer goods / home / textiles
Brands entering new markets, and ODM/OEM suppliers
Why AI skips you: Buyers ask "which brand is good?" or "what do you recommend?" AI answers subjective questions based on how much is written about a brand and how consistent the reviews are. Without a presence, you have no place in the answer.
- Brand entity and product-line modeling to build recognition
- Content mapped to use cases and audiences
- Multi-source coverage: reviews, reputation and third-party mentions
Recommended plan: Growth
Niche specialists
Niche categories where you lead in global share but few people know your name
Why AI skips you: Source content for the category is thin, and almost nobody discusses it with AI. That is the bad news. It is also the biggest opportunity.
- Claim the empty space: a small amount of high-quality structured content builds weight fast
- Define the category: publish your technical standards as the industry reference
- Multilingual coverage across every market, so AI treats you as the reference supplier worldwide
Recommended plan: Leader
