Durex Technique, a Liege-based SME specializing in industrial surface treatment and anti-corrosion coating services for the metals and mechanical engineering sector, had operated for 19 years primarily through long-standing relationships with Belgian and German industrial clients. The commercial director, taking over from the founder in 2025, identified a structural growth constraint: the company's client base was concentrated in four large accounts representing 61% of revenue, and new client acquisition was entirely relationship-dependent.
The new commercial director recognized that international industrial buyers, particularly those in the automotive supply chain and aerospace maintenance sectors, were increasingly using AI tools for supplier discovery and preliminary qualification. When she tested this hypothesis by asking ChatGPT and Perplexity for anti-corrosion coating specialists in Belgium, Durex Technique did not appear in any response. Three German and Dutch competitors with comparable capabilities appeared consistently. The gap was structural and could not be addressed through relationship development alone.
AISOS was engaged for an 80-day implementation focused on building Durex Technique's AI visibility for B2B procurement queries from international industrial buyers. The engagement required building AI signals around technical capability documentation, industrial certification credentials, and sector-specific references. Our guide on AI visibility for B2B industrial companies, our AI visibility overview, and our Liege AI visibility page informed the approach. For engagement scoping, see our contact page.
The Challenge
Industrial B2B AI visibility faces a specific challenge that differs from both consumer and professional services contexts: prospective buyers are procurement professionals with specific technical qualification criteria. When a purchasing manager at an automotive tier-1 supplier asks an AI assistant for anti-corrosion coating specialists in Belgium or the Benelux region, the AI response needs to include specific capability data: treatment processes offered, substrate types handled, certifications held, capacity and lead time norms, and quality management standards. Generic descriptions of industrial services are not sufficient for AI recommendation in technical procurement contexts.
The AI visibility audit at the start of the engagement tested 42 B2B procurement queries relevant to Durex Technique's services, including queries in English, French, German, and Dutch, reflecting the multilingual nature of the Benelux industrial buyer market. Durex Technique appeared in 4 of the 42 queries (9.5%), with appearances concentrated in French-language queries and entirely absent from English and German-language procurement queries that represented the highest-value prospective client segments.
Durex Technique held ISO 9001 and ISO 14001 certifications, NADCAP aerospace approval (an industry-specific credential of significant weight with aerospace procurement), and several major customer qualification certificates. None of these certifications were documented in machine-readable format on the company website or in any publicly accessible structured data format. For international AI-enabled procurement searches, the company was essentially anonymous despite holding credentials that would have immediately qualified it as a serious supplier candidate. Understanding AEO for industrial B2B was the conceptual starting point for the implementation design.
The AISOS Strategy
The strategy was designed around the three categories of information that industrial procurement AI queries require: technical capability specifications, quality and compliance credentials, and sector-specific reference evidence. Each category required different content types and different schema implementations. The implementation operated in four languages: French, English, German, and Dutch, to cover the full geographic scope of the target buyer market.
Technical capability documentation: AISOS worked with the Durex Technique engineering team to document the company's process capabilities in structured format: surface treatment processes by substrate type (steel, aluminum, titanium, specialty alloys), treatment specifications (thickness ranges, temperature tolerances, adhesion performance parameters), facility specifications (chamber dimensions, batch capacity, monthly throughput capacity), and geographic delivery scope. This information was published as structured Service schema pages with TechnicalSpecification sub-schema. The documentation was simultaneously published in French, English, and German to cover the full buyer language profile.
Certification and qualification documentation: A dedicated credentials page was created documenting ISO 9001, ISO 14001, and NADCAP aerospace approval with specific scope statements, audit date references, and links to certification body registries where verifiable. Customer qualification certificates were referenced with appropriate anonymization. A Wikidata entity was created for the company. The company was added to three international industrial supplier directories with known AI sampling rates, in all four languages. An llms.txt file was deployed in French and English with structured positioning information for AI crawlers. Internal links connected to industry pages and to relevant resources. Reference evidence pages: Three anonymized case study pages were developed documenting specific anti-corrosion projects for automotive and aerospace clients, each with process specifications, substrate type, challenge characteristics, and measurable performance outcomes. These pages linked to the glossary for relevant technical terms.
The Results
By day 80, Durex Technique appeared in 24 of the 42 original audit queries (57%), up from 4 (9.5%). The improvement was most pronounced in English-language procurement queries, where the company went from 0 to 10 appearances in 14 relevant queries. German-language queries improved from 0 to 7 in 10. French-language queries improved from 4 to 10 in 12. Dutch-language queries improved from 0 to 5 in 8. NADCAP aerospace approval was cited in 8 AI responses as the primary qualification credential, confirming that the certification documentation strategy was the primary driver of AI recommendation in aerospace procurement queries.
The commercial director received 22 qualified supplier qualification requests from new industrial buyers over the 80-day post-implementation period, compared to a baseline of 2-3 per quarter from non-relationship sources. Of the 22, 18 were from buyers outside Belgium: 9 from Germany, 4 from France, 3 from the Netherlands, and 2 from the United Kingdom. Six progressed to formal supplier qualification visits. Three completed qualification and placed initial trial orders within the measurement window, with a combined initial order value of 87,000 euros and estimated annual potential of 340,000 euros across the three new accounts.
The NADCAP aerospace credential, once documented in structured machine-readable format, generated an unexpected benefit beyond AI visibility: the credential pages were discovered by two aerospace maintenance organizations through organic search, generating two additional qualification inquiries that were not part of the original AI query test set. Technical credential documentation frequently generates demand from query types that were not specifically targeted, because the content specificity that serves AI visibility also serves traditional search performance for niche technical queries.
Key Success Factors
The multilingual implementation was the highest-impact structural decision in the engagement. A French-only implementation would have improved performance for French-speaking Belgian and French buyers while leaving the German, Dutch, and English procurement markets untouched. Industrial B2B buyers in the Benelux and DACH regions use their native language for supplier AI queries. A company that wants to appear in procurement queries from German automotive tier-1 suppliers must have German-language technical capability documentation in machine-readable format. The translation investment for industrial B2B AI visibility consistently generates its highest ROI in the language markets where the target buyer concentration is highest.
The NADCAP aerospace approval documentation was the single most powerful individual credential in the engagement. NADCAP is an industry-specific accreditation for critical aerospace processes that most non-specialist industrial suppliers do not hold. Once this credential was documented in structured format, AI systems generating responses to aerospace procurement queries had a specific, verifiable, high-relevance signal that immediately differentiated Durex Technique from generic industrial coating competitors. For SMEs with industry-specific accreditations that are rare and valued by their target buyers, those accreditations are the first and highest-priority AI visibility asset to structure and publish.
The reference case study pages addressed a specific challenge in industrial B2B AI recommendation: AI systems are appropriately cautious about recommending industrial suppliers for critical processes without reference evidence. Generic technical capability descriptions provide capability information but not evidence of capability execution. The anonymized case studies provided the execution evidence that elevated Durex Technique from a capability claim to a demonstrated capability, significantly increasing AI recommendation confidence for high-stakes procurement queries. In industrial and professional services contexts, the distinction between claimed capability and demonstrated capability is a decisive AI recommendation factor.
Lessons Learned
The most important lesson from the Durex Technique engagement is that industrial SMEs with genuine technical differentiators are systematically underrepresented in AI procurement recommendations because their differentiators are undocumented in machine-readable format. The technical capability documentation that industrial buyers need to make procurement decisions is exactly the information that AI systems need to make recommendations. The same document serves both audiences. Building a technical capability library that is both human-readable and machine-structured is the highest-ROI single investment for industrial SMEs seeking AI visibility in B2B procurement contexts.
The geographic expansion effect was more rapid than anticipated. Within 60 days, the majority of new inquiries were coming from outside Belgium. This reflects the fundamentally international nature of AI-enabled industrial procurement. When a German purchasing manager asks an AI assistant for anti-corrosion coating specialists in the Benelux region, geographic boundaries become less relevant than technical capability and credential verification. Industrial SMEs that have historically been constrained by geographic proximity in their sales process now have access to AI-enabled international buyer discovery that was previously available only to companies with active international sales teams.
Finally, the engagement demonstrated that AI visibility is not a marketing initiative for industrial SMEs. It is a commercial development initiative that generates qualified procurement inquiries from buyers who are actively evaluating suppliers. The return on investment calculation is not based on brand awareness metrics. It is based on the financial value of qualified supplier qualification requests converted to new account relationships. Industrial SMEs should evaluate AI visibility investment through a commercial development budget lens, not a marketing budget lens, and measure success through procurement inquiry generation and new account conversion metrics. Contact AISOS to design an industrial B2B AI visibility program calibrated to your technical capabilities and target buyer geography.