Bernt
6989a98d75
- Arkitektur: docs/auth/passwordless-architecture.md - Backend: iom/quixzoom-auth-service/ (FastAPI + Redis) - Webb: quixzoom-market-pages/se/login/ (QR-kod + polling) - App: iom/quixzoom-app/src/features/auth/ (push + deep links) Flöde: QR-kod → app-godkännande → webb-inloggad
350 lines
21 KiB
HTML
350 lines
21 KiB
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<a class="nav-logo" href="https://landvex.com/">← Landvex</a>
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<a class="lv-btn lv-btn--outline" href="/contact/">Contact →</a>
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<div class="breadcrumb">
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<a href="https://landvex.com/">Landvex</a>
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<span class="sep">/</span>
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<a href="/use-cases/">Use Cases</a>
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<span class="sep">/</span>
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<a href="/use-cases/retail-intelligence/">Retail Intelligence</a>
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<span class="sep">/</span>
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<span>France</span>
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</div>
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<!-- HERO -->
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<section class="lv-hero">
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<div class="lv-hero-eyebrow">Retail Intelligence — France</div>
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<h1>Retail Intelligence in France</h1>
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<p class="lv-hero-subtitle">French retail markets are undergoing structural transformation, with centre-ville pedestrianisation programmes coexisting alongside continued retail park expansion. Landvex provides property investors, retailers, and Agences de developpement with field-verified intelligence to distinguish performing locations from distressed ones before capital is committed.</p>
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<div class="hero-ctas">
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<a class="lv-btn lv-btn--lg" href="/pilot/">Request a pilot →</a>
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<a class="btn btn-outline btn-lg" href="/contact/">Contact Landvex</a>
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</div>
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</section>
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<!-- USE CASE CARDS -->
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<section>
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<div class="lv-container">
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<div class="section-label">Capabilities</div>
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<h2 class="section-title">Retail Intelligence in France — what Landvex delivers</h2>
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<p class="section-sub">Every engagement is anchored to a specific decision. Select a capability to discuss how Landvex applies it to your context.</p>
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<div class="use-case-grid">
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<a class="use-case-card" href="/contact/">
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<span class="uc-icon">󫦺</span>
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<h3>Centre-Ville Retail Assessment</h3>
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<p>Field-verified assessment of French town centre retail performance: occupier mix, vacancy indicators, pedestrian environment quality, and competition from periurban formats. Evidence for Action Coeur de Ville investment decisions.</p>
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<div>Learn more →</div>
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</a>
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<a class="use-case-card" href="/contact/">
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<span class="uc-icon">■</span>
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<h3>Zone Commerciale Monitoring</h3>
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<p>Monitor condition, occupancy, and competitive dynamics across zones commerciales and centres commerciaux throughout French markets. Detect structural distress before official data captures it.</p>
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<div>Learn more →</div>
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</a>
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<a class="use-case-card" href="/contact/">
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<span class="uc-icon">◆</span>
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<h3>SCPI and OPCI Portfolio Intelligence</h3>
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<p>Systematic field-verified monitoring of retail asset portfolios for French SCPI, OPCI, and institutional investors. Physical condition, occupier quality, and competitive positioning quantified at portfolio scale.</p>
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<div>Learn more →</div>
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</a></div>
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</div>
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</section>
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<!-- COMPARISON TABLE -->
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<section>
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<div class="lv-container">
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<div class="section-label">Comparison</div>
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<h2 class="section-title">Landvex vs traditional retail intelligence methods</h2>
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<p class="section-sub">How field-verified continuous intelligence compares to conventional survey and inspection approaches.</p>
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<table class="comparison-table">
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<thead>
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<tr>
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<th>Dimension</th>
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<th>Traditional approach</th>
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<th>Landvex</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>Data source</td>
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<td>PROCOS/CNCC surveys, modelled</td>
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<td>Continuous field observation</td>
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</tr>
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<tr>
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<td>Vacancy lag</td>
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<td>6–12 months</td>
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<td>Days</td>
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</tr>
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<tr>
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<td>Centre-ville vs zone commerciale comparison</td>
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<td>Separate surveys</td>
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<td>Integrated field assessment</td>
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</tr>
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<tr>
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<td>Physical environment quality</td>
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<td>Not captured</td>
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<td>Field-verified street-level</td>
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</tr>
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<tr>
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<td>Geographic coverage</td>
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<td>Selected centres</td>
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<td>Any French retail location</td>
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</tr>
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<tr>
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<td>Contradiction analysis</td>
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<td>Not standard</td>
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<td>Automated AI</td>
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</tr>
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</tbody>
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</table>
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</div>
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</section>
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<!-- KEY DECISIONS -->
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<section class="decisions-bg">
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<div class="lv-container">
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<div class="section-label">Decision support</div>
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<h2 class="section-title">Decisions Landvex informs in France</h2>
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<p class="section-sub">Every Landvex engagement is anchored to one or more of these decisions. We start with your question before designing the intelligence mission.</p>
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<div class="decisions-grid">
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<div class="decision-chip">
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<span class="chip-dot"></span>
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<span>Retail property acquisition and lease negotiation</span>
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</div>
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<div class="decision-chip">
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<span class="chip-dot"></span>
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<span>Action Coeur de Ville investment evidence</span>
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</div>
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<div class="decision-chip">
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<span class="chip-dot"></span>
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<span>SCPI and OPCI portfolio monitoring</span>
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</div>
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<div class="decision-chip">
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<span class="chip-dot"></span>
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<span>Retailer site selection intelligence</span>
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</div>
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<div class="decision-chip">
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<span class="chip-dot"></span>
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<span>Urban retail planning evidence for collectivités</span>
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</div></div>
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</div>
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</section>
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<!-- CONTRADICTION ENGINE -->
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<section class="contradiction-bg">
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<div class="lv-container">
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<div>
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<div class="section-label">Contradiction Engine</div>
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<h2 class="section-title">Most systems confirm what you already believe.<br>Landvex finds what doesn’t add up.</h2>
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<p class="section-sub">The most actionable intelligence is contradiction. Landvex actively searches for conflicts between official narratives and observed field reality — and quantifies the confidence gap.</p>
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</div>
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<div class="contradiction-box">
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<div class="conflict-header">
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<span class="conflict-flag">⚠</span>
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<div>
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<div class="conflict-title">Narrative Conflict Detected — Rue de la Republique — Lyon</div>
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<div class="conflict-desc">PROCOS vacancy data indicates moderate performance. Field observation reveals effective vacancy nearly three times the reported rate across the monitored stretch.</div>
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</div>
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</div>
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<div class="conflict-grid">
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<div class="conflict-side conflict-official">
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<div class="conflict-side-label">Official narrative</div>
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<ul><li>𘚣 Vacancy rate: 8.4% (PROCOS Q2)</li>
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<li>𘚣 Pedestrianisation programme: improving footfall</li>
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<li>𘚣 No exceptional distress signals in planning register</li></ul>
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</div>
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<div class="conflict-side conflict-observed">
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<div class="conflict-side-label">Observed reality</div>
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<ul><li>𘚧 Active vacancy on 17 of 71 units — effective rate 23.9%</li>
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<li>𘚧 Pop-up and temporary units masking structural vacancy on 5 units</li>
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<li>𘚧 Two anchor occupiers with reduced opening hours and degraded fit-out</li>
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<li>𘚧 Street environment maintenance below prime standard on 40% of frontage</li></ul>
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</div>
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</div>
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<div class="conflict-footer">
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<span class="conflict-footer-label">Confidence in official narrative</span>
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<span class="conflict-confidence">20%</span>
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</div>
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</div>
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<div>
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<div>
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<div>🔍</div>
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<h4>Show me what I am missing</h4>
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<p>Blind spots absent from official data and internal reports</p>
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</div>
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<div>
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<div>⚖</div>
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<h4>AI Red Team</h4>
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<p>Every conclusion is challenged by a second model before delivery</p>
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</div>
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<div>
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<div>📐</div>
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<h4>Which assumptions are wrong?</h4>
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<p>Identify where your thesis conflicts with observed reality</p>
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</div>
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</div>
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</div>
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</section>
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