OVR and Magic Square case studies: dual case-study release documents 39-point and 47-point AEO lifts

LONDON · JUNE 3, 2026

The two case studies anchor two new vertical benchmarks for Crawlux. OVR is the calibration point for geospatial Web3 (the category that includes augmented-reality Web3, virtual real estate and on-chain mapping). Magic Square is the calibration point for Web3 app marketplaces (the category that includes dApp directories, wallet app stores and Web3 SaaS marketplaces). Both verticals had no methodology-calibrated benchmark before these audits.

OVR opening state. The project operates a geospatial Web3 platform with AR experiences tied to specific real-world locations. The audit found AI search engines returning generic Web3 metaverse answers when asked about OVR specifically, with no engine connecting the AR overlay product to the geospatial data layer. Schema-wise, OVR had Article schema on blog posts but no Place schema on any of the location-bound experiences, which is the single most important signal for a geospatial product. Robots.txt allowed all crawlers cleanly. Trust signals were mid-pack at 38 out of 100.

The OVR fix sequence. Days 1 to 25: Place schema added to every geo-anchored experience page, linked through Organization schema to OVR token and OVR Land entity. Days 25 to 50: AR-specific schema (SoftwareApplication subclass for AR experiences) added with technical capability fields documenting device support, AR features and content type. Days 50 to 75: citation engineering across the 18 highest-authority external publications covering OVR added through structured sameAs and a dedicated Coverage page. AI Visibility moved 28 to 41, 41 to 56, 56 to 67 across the three phases.

What the OVR case study reveals about geospatial Web3 specifically. The Place-Organization-Token schema triangulation transferred cleanly from the World Mobile Token (DePIN) case study to OVR (geospatial). The pattern works wherever a Web3 project is anchored to physical or pseudo-physical real-world entities (DePIN nodes, AR locations, virtual real estate, tokenized real-world assets). The Place schema is the bridge that lets AI engines connect the digital token to the physical or geospatial referent. Other geospatial Web3 projects can apply the same pattern.

Magic Square opening state. Magic Square operates a Web3 app marketplace with curated dApp listings across DeFi, NFT, GameFi and infrastructure categories. The audit found the most severe schema gap of any case study Crawlux has run to date. The marketplace had 2,400+ app listing pages with no structured data, no schema, and essentially no AI citation surface. AI search engines asked about specific apps listed on Magic Square returned answers from elsewhere on the web, completely bypassing the Magic Square pages. Opening AI Visibility: 24 out of 100.

The Magic Square fix sequence. Days 1 to 30: SoftwareApplication schema templated across all 2,400+ app listings with applicationCategory mapped to Crawlux solution categories (DeFi, NFT, GameFi, Wallet). The template ship-and-deploy ran across all listings in a single push. AI Visibility moved from 24 to 52 in 9 days post-deploy. The single biggest single-week AEO lift Crawlux has ever observed. Days 30 to 60: Organization schema for app developers added wherever the developer entity was disclosed, creating an Organization-SoftwareApplication graph across the marketplace. Days 60 to 80: Magic Square's own Organization schema expanded with the full sameAs graph including the dApp categories the marketplace covers. AI Visibility: 52 to 64 to 71.

The structural insight from Magic Square. The marketplace category is fundamentally a schema problem. App marketplaces aggregate other people's products. Without structured data connecting the marketplace listing to the underlying app, AI engines cannot tell whether the listing is a primary source or a derivative aggregator page. SoftwareApplication schema solves the problem cleanly. The same pattern transfers to other Web3 marketplace verticals: wallet app stores, dApp directories, NFT marketplace aggregators, even DAO tool directories. The schema graph creates the citation surface.

Both case studies are published in full at crawlux.com/case-studies. The case-study format documents every audit finding, every fix shipped, every score delta with confidence intervals, and the dead-end experiments that did not work (one for OVR involving extensive internal-link engineering, one for Magic Square involving Person schema for app developers that produced no measurable lift). Future case studies will follow the same format: complete transparency on what worked, what did not, and what the operational sequence looks like.

Crawlux is the world's first automated SEO audit tool built for Web3, DeFi and blockchain. The platform runs 23 analyzers across 6 check groups including AI visibility testing across ChatGPT, Perplexity and Claude. Free tier available. Paid tiers from $25 per audit. More at crawlux.com.