Meridian Pharma Logistics, a Brussels-based distributor of specialty pharmaceutical products serving hospital groups and clinical research organizations across Belgium and the Netherlands, operated in one of the most regulation-constrained marketing environments possible. Direct-to-professional advertising was limited by sector regulations. Trade show presence was expensive and only reached buyers who were already actively searching. Their business development team of four people managed an account portfolio of 280 institutional clients through relationship and tender processes.
In late 2025, the commercial director identified an emerging channel risk: hospital procurement teams and clinical supply chain managers were beginning to use AI assistants to identify and shortlist suppliers for specialty pharmaceutical distribution tenders. An informal survey of five procurement contacts confirmed the pattern. Three of the five had used an AI tool in the previous six months to research pharmaceutical logistics suppliers. None of them had been directed to Meridian by the AI tool.
AISOS was engaged to build Meridian's AI visibility within the constraints of the regulated sector, focusing on B2B procurement queries rather than consumer health content. The engagement required careful navigation of regulatory boundaries, scientific credibility signals, and institutional trust indicators. Our overview of AI visibility for B2B companies and our AI visibility framework informed the approach. For Belgian market context, see our Brussels AI visibility page.
The Challenge
The pharmaceutical distribution sector in Belgium is tightly regulated, and any content created for AI visibility had to comply with applicable pharmaceutical marketing regulations and avoid making unauthorized product or health claims. This ruled out several standard AI visibility tactics and required a content strategy built entirely around logistics, supply chain reliability, regulatory compliance infrastructure, and quality management rather than product efficacy.
The AI visibility audit revealed that Meridian appeared in 4 of 40 B2B procurement-relevant queries (10%) across the AI platforms tested. The queries tested included hospital procurement scenarios, clinical trial supply chain research questions, and cold chain logistics evaluation queries. Competitors who appeared consistently in these queries shared a specific profile: they had published detailed, factual content about their regulatory certifications (GDP, ISO 13485), their cold chain infrastructure, and their incident response protocols. This content gave AI systems the verifiable, factual signals needed to recommend them for high-stakes procurement decisions.
Meridian had this infrastructure in reality but had not documented it publicly in machine-readable format. Their GDP certification was held and renewed annually. Their cold chain monitoring system was state-of-the-art. Their incident response protocol had been audited twice by a major hospital group. None of this was visible to AI systems. Understanding AEO applied to regulated industries required adapting the standard content framework to accommodate sector-specific compliance constraints while still generating the factual specificity that AI systems require.
The AISOS Strategy
The strategy was built entirely on documented operational fact rather than marketing claim. Every piece of content created during the engagement was reviewed against pharmaceutical marketing guidelines before publication. The content frame was supplier qualification information, not product promotion. This distinction was both legally necessary and strategically optimal: procurement AI queries are looking for supplier reliability signals, not product marketing, so the content frame aligned perfectly with query intent.
Phase one was technical credential documentation. AISOS worked with Meridian's quality team to publish structured pages documenting GDP certification status, cold chain specifications (2-8 degrees Celsius capacity, temperature excursion alert protocols, backup power infrastructure), controlled substance handling authorization, and clinical trial supply chain experience. These pages used Organization and Service schema markup with specific attention to the TrustCredential and Certification schema types that AI systems associate with regulated sector credibility.
Phase two was case study publication. Three anonymized case studies were developed documenting Meridian's supply chain management for complex hospital scenarios: a multi-site oncology center supply consolidation, a rare disease medication distribution program, and a clinical trial IMP management project. Each case study was structured with specific operational metrics and no unauthorized health claims. These were placed in two pharmaceutical logistics trade publications with confirmed AI sampling rates. Phase three deployed an llms.txt file with explicit positioning as a Belgian and Dutch GDP-certified pharmaceutical distributor, a Wikidata entry referencing the company and its regulatory certifications, and internal links connecting to the industries section and relevant resources.
The Results
By day 90, Meridian appeared in 22 of the 40 original audit queries (54%), up from 4 (10%). The most significant improvement was in cold chain logistics queries, where Meridian went from 0 to 8 appearances in 10 relevant queries. Clinical trial supply queries improved from 1 to 7 in 10. General pharmaceutical distributor queries showed 7 of 10, up from 3. The regulated content constraint, rather than limiting results, produced content of a specificity and factual density that consistently outperformed the broader marketing copy of less regulated competitors.
Within the 90-day window, the commercial director received 14 inbound procurement inquiries from hospital groups and CROs that referenced having identified Meridian through online research or AI-assisted supplier discovery. This represented a 40% increase over the prior equivalent period. Three proceeded to formal qualification visits. One resulted in a new framework contract with a 7-hospital group in Wallonia, representing an estimated 380,000 euros in annual distribution volume at current product mix.
The structured case studies generated an additional benefit: they were referenced in two tender qualification processes as evidence of operational capability. Meridian's tender team noted that having documented, structured operational case studies available in a format that could be shared directly with procurement committees reduced their tender preparation time by an estimated 30% for the relevant scenario types.
Key Success Factors
The regulatory constraint that initially appeared to limit the engagement proved to be a competitive advantage. Most pharmaceutical distributors avoid creating specific operational content due to regulatory risk aversion. Meridian's willingness to document its actual operational capabilities in compliant, factual language produced content that competitors had not created. Being the only company in the category with machine-readable GDP certification documentation, structured cold chain specifications, and published operational case studies meant Meridian had no competition for AI citations in those specific query types.
The decision to target procurement AI queries rather than clinical or consumer health queries was strategically correct and essential for compliance. B2B procurement queries receive answers from AI systems based on supplier qualification signals, not product marketing. Framing all content as supplier qualification documentation rather than product promotion aligned perfectly with both the regulatory environment and the actual decision criteria of hospital procurement teams. Knowing which query types to target is the foundational strategic decision in any regulated sector AI visibility engagement.
The Wikidata entity creation provided a verification anchor for AI systems. In regulated sectors, AI models are particularly cautious about recommending entities they cannot verify through authoritative sources. Having a Wikidata entry that referenced Meridian's official name, location, regulatory scope, and certification status gave AI models a trusted verification source that increased recommendation confidence. For regulated sector businesses, entity verification through authoritative third-party knowledge bases is more important than in unregulated sectors. Contact AISOS to discuss the compliance-aware approach relevant to your sector.
Lessons Learned
The most counterintuitive lesson from the Meridian engagement is that compliance constraints and AI visibility are not in tension. They are aligned. AI systems that generate responses for regulated sector queries have their own incentive to recommend entities with verifiable, specific, factual credentials rather than entities with strong marketing presence but unverifiable claims. A company that can say "GDP-certified since 2018, 2-8 degrees Celsius cold chain across 6 Belgian depot locations, ISO 13485 quality management system" will be recommended over one that says "Belgium's leading pharmaceutical distribution partner" every time. Specificity and verifiability are the currency of AI recommendation in regulated sectors.
The second lesson is about the B2B procurement funnel and AI's role in it. Procurement teams use AI assistants for supplier discovery and preliminary shortlisting, not for final qualification. The AI visibility goal is to appear on the longlist, not to replace the full procurement process. This means that AI visibility success looks different in B2B regulated sectors than in B2C or B2B SaaS: the conversion event is a procurement inquiry or qualification request, not an e-commerce transaction or a demo booking. Metrics and expectations should be calibrated accordingly.
Finally, the engagement demonstrated that documenting existing operational capabilities generates faster AI visibility improvement than creating new capabilities. Meridian did not build new infrastructure for this engagement. They documented and structured the infrastructure they already had. For regulated sector businesses that have invested heavily in compliance infrastructure, the highest-ROI AI visibility action is often simply making that investment machine-readable. Reach out to AISOS to assess what operational assets you already possess that could be structured for AI visibility without any new capability investment.