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
HTML
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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/road-inspection/">Road Inspection</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">Road Inspection — France</div>
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<h1>Road Inspection in France</h1>
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<p class="lv-hero-subtitle">France's route nationale and departementale networks face growing maintenance pressure, with departmental road authorities managing extensive inventories on constrained budgets. Landvex provides CEREMA, DIRs, and conseils departementaux with field-verified road condition intelligence to support evidence-based prioritisation.</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">Road Inspection 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>IQRN/IQROA Condition Pre-Screening</h3>
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<p>Field observations structured to support IQRN and IQROA assessment workflows. Identify priority sections before formal survey deployment, enabling targeted use of CEREMA and DIR survey resources.</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>Route Departementale Network Surveys</h3>
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<p>Systematic road condition assessment across departmental road networks. Support conseils departementaux in prioritising limited DOB maintenance budgets with field-verified evidence.</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>Road Marking and Safety Audit Support</h3>
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<p>Assessment of route marking, glissicometrie indicators, signage, and roadside hazard conditions. Evidence-based input for DIR safety audit programmes and departmental road safety plans.</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 road inspection 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>IQRN/IQROA supplement</td>
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<td>Survey cycle only</td>
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<td>On-demand between surveys</td>
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</tr>
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<tr>
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<td>Deployment lead time</td>
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<td>Months — survey planning</td>
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<td>24–72 hours</td>
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</tr>
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<tr>
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<td>Network coverage</td>
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<td>RN and selected RD only</td>
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<td>Any French road</td>
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</tr>
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<tr>
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<td>Deliverable format</td>
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<td>Survey table, PDF</td>
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<td>Structured data, GIS-ready</td>
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</tr>
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<tr>
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<td>Contractor verification</td>
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<td>Manual</td>
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<td>AI-assisted remote</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 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 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>DIR maintenance prioritisation</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>Conseil departemental DOB budget allocation</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>Safety audit programme pre-screening</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>Contractor works acceptance</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>PDMI input and evidence</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 — RD 906 — Hautes-Alpes</div>
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<div class="conflict-desc">IQROA score C suggests standard departmental maintenance interval. Field observation identifies deterioration consistent with D-score conditions on two of the four monitored sections.</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 IQROA survey: 20 months ago, score C</li>
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<li>𘚣 Departmental maintenance programme: within schedule</li>
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<li>𘚣 No emergency repair orders active</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 Longitudinal cracking on 4.2km inconsistent with C-score parameters</li>
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<li>𘚧 Pothole density on 1.8km exceeds D-score threshold</li>
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<li>𘚧 Drainage failures at 11 culverts outside maintenance programme</li>
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<li>𘚧 Road marking fading below minimum retroreflectivity on 28km</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">25%</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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