Case study, long form: running the full AI Commerce OS at 51,000-SKU scale with Tanlink

Of the seven named engagements on our case-study roster, Tanlink is the one that exercises every layer of the AI Commerce OS at once — which makes it the right candidate for our first long-form write-up. One disclosure before the detail: as on the case-study cards, quantified results here are derived from engagement modeling combined with public-source AI-surface monitoring, and are labeled approximate (≈) to indicate estimation.

Tanlink is a globally recognized cross-border automotive-parts retailer specializing in OEM-equivalent and aftermarket parts — brake pads and rotors, suspension components, ignition coils, oxygen sensors, control arms — for European and Japanese vehicles, selling primarily into the US and EU. The catalog exceeds 51,000 SKUs across more than 1,200 vehicle make / model / year / engine combinations. That long-tail scale is the defining constraint: hand-written GEO content per vehicle is economically infeasible at any reasonable cost.

The starting position was the one most long-tail retailers are in today. For queries like "best brake pads for BMW E90 335i", "Honda CR-V 2018 oxygen sensor replacement", or "aftermarket vs OEM strut comparison for Audi A4 B8", AI engines defaulted to RockAuto, generic Amazon listings without brand attribution, or FCP Euro. Tanlink rarely surfaced even for direct-fit queries that perfectly matched its catalog — the existing technical content was simply too generic for AI-engine semantic matching.

The engagement deployed the full AI Commerce OS Solution Matrix as a 4-layer integrated program: AI Visibility, Performance Marketing, E-commerce Closed-Loop, and Private-Domain LTV running in parallel. The structural core was catalog-scale schema generation: our Agentic Commerce Growth platform processed the entire catalog into 51,000+ Schema.org Product entities with structured fitment data, Vehicle compatibility entities, OEM-number cross-references, and per-SKU FAQ schema covering common DIY scenarios.

On top of the structured layer sat the content program. A YouTube how-to series with car-mechanic creators produced 200+ videos with chapter-stamped transcripts, each wired to the specific Vehicle and Product entities involved via VideoObject schema. Reddit Power-User content seeded DIY repair scenarios in r/MechanicAdvice, r/CarTalk, and r/cars with cross-links into the fitment graph. Quora carried the deep technical answers. Amazon Rufus optimization targeted fitment queries directly, and Email + SMS private-domain flows added vehicle-aware recommendations for repeat customers.

Results, averaged across 50 monitored vehicles and labeled approximate throughout: Perplexity citation for "best brake pads for [specific vehicle]" went from near-zero to ≈25–40%. Google AI Overviews inclusion for "OEM vs aftermarket [part] for [vehicle]" went from absent to ≈45%. Amazon Rufus moved from unstructured presence to schema-driven top-5 inclusion on fitment queries. Repeat-customer email LTV rose ≈45% on a 24-month cohort. The headline number: revenue attributable to AI-search traffic went from ≈0% to ≈18% of total.

The takeaway we keep coming back to: long-tail catalogs above 50K SKUs are the perfect Solution Matrix use case. Only an agentic platform can generate per-SKU AI-readable schema at catalog scale, and only the 4-layer framework ensures citation gains compound with paid acquisition and private-domain LTV instead of stopping at visibility. If your catalog is too big to write content for by hand, that is not the obstacle — it is the case for the agentic approach.

The condensed version of this engagement, alongside the other six named cases, lives on our case-studies section; the methodology behind the attribution chain is described on the Evolution page. If you want the same diagnostic run against your catalog, the engagement starts with a free AI Search Readiness Audit — contact@leapunion.com.