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>United Kingdom</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 — United Kingdom</div>
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<h1>Retail Intelligence in the United Kingdom</h1>
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<p class="lv-hero-subtitle">UK retail real estate continues to undergo structural adjustment, with high street vacancy remaining above long-term averages and out-of-town retail formats facing divergent performance trajectories. Landvex provides field-verified intelligence to support acquisition, asset management, and place-making decisions based on observed reality rather than survey interpolation.</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 the United Kingdom — 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>High Street Vacancy Monitoring</h3>
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<p>Field-verified vacancy monitoring across UK town centres and high streets. Identify structural vacancy, temporary uses masking distress, and deterioration in occupier quality — updated continuously, not on annual survey cycles.</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>Retail Park and Shopping Centre Assessment</h3>
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<p>Systematic field assessment of UK retail park and shopping centre assets: occupier mix, physical condition, presentation standards, and competitive positioning. Evidence for institutional acquisition and disposal 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>Local Authority Place Intelligence</h3>
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<p>Support UK local authorities in monitoring high street health for BID management, Levelling Up Fund interventions, and UKSPF programme targeting. Field-verified evidence to underpin investment business cases.</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>LDC/Springboard annual surveys</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 detection latency</td>
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<td>6–12 month survey lag</td>
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<td>Days</td>
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</tr>
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<tr>
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<td>Physical condition capture</td>
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<td>Not in standard footfall data</td>
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<td>Systematic street-level assessment</td>
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</tr>
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<tr>
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<td>Pop-up and temporary use detection</td>
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<td>Counted as occupied</td>
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<td>Flagged and classified separately</td>
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</tr>
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<tr>
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<td>Coverage</td>
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<td>Monitored centres only</td>
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<td>Any UK retail location</td>
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</tr>
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<tr>
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<td>Contradiction detection</td>
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<td>Not standard</td>
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<td>Automated AI cross-reference</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 the United Kingdom</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>UK retail property acquisition due diligence</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>REIT and fund 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>Local authority high street intervention 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>BID renewal and investment business case</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 UK site selection intelligence</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 — Market Street — Manchester City Centre</div>
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<div class="conflict-desc">LDC data and BID assessment indicate moderate recovery. Field observation reveals effective structural vacancy more than twice the reported rate.</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 LDC vacancy rate: 11.3%</li>
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<li>𘚣 Footfall index: improving trend</li>
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<li>𘚣 BID health assessment: stable</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 structural vacancy on 19 of 74 units — effective rate 25.7%</li>
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<li>𘚧 7 pop-up, temporary, and charity units offsetting structural vacancy</li>
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<li>𘚧 Three former anchor units subdivided or at reduced rental value</li>
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<li>𘚧 Public realm maintenance below investment-grade standard on main 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">14%</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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