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- New /developers/ page with API docs, SDKs, pricing, use cases - OpenAPI 3.0 spec for Orders, Missions, Photos, Analytics - Case study: Glasskiosken i Flatenbadet — complete ROI analysis - Updated /order/ with recurring missions and frequency dropdown
852 lines
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HTML
852 lines
64 KiB
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<title>Glossary — landvex</title>
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</head>
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<body>
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<nav class="nav">
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<a href="/" class="nav-logo">qui<span class="x">X</span>zoom</a>
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<div class="nav-divider"></div>
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<span class="nav-title">Glossary</span>
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</nav>
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<main>
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<header class="page-header">
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<h1>Every term.<br>Defined.</h1>
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<p>The complete landvex reference — from field observations to decision intelligence.</p>
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</header>
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<section class="letter-section" id="a">
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<div class="letter-heading">
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<h2>A</h2>
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<span class="letter-count">14 terms</span>
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</div>
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<article id="amos" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Adaptive Autonomous Multi-Agent Operating System — the AI capability platform that powers Landvex intelligence products.</p>
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<p class="term-long">AMOS is not an AI system. It is an AI Capability Platform — a suite of specialised engines that process observations into structured intelligence. AMOS ingests raw field data from quiXzoom, applies computer vision, pattern recognition, and cross-referencing, and produces outputs: indices, scores, change flags, and predictive signals. AMOS is the analytical layer between reality and decisions. It is developed and sold by Landvex Inc, Houston, TX.</p>
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<div class="term-context">
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<span class="term-context-label">In the ecosystem</span>
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<p>quiXzoom collects 12,000 observations of bridge conditions across Sweden. AMOS processes these into an Infrastructure Risk Index — a single score per bridge that maintenance planners can prioritise by.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#observation">Observation</a><a href="#index">Index</a><a href="#decision-intelligence">Decision Intelligence</a><a href="#amos-vision">AMOS Vision</a><a href="#amos-identity">AMOS Identity</a><a href="#amos-fraud">AMOS Fraud</a>
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</div>
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</article>
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<article id="amos-change-engine" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Change Engine</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Detects changes between observations over time.</p>
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<p class="term-long">The AMOS Change Engine compares current observations against historical baselines to identify additions, removals, and modifications. It powers change detection at scale, enabling proactive maintenance and early problem identification. The engine operates across multiple time scales from hours to years.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>A property manager receives a quarterly change report showing that 3 buildings have new roof damage, 2 have added unauthorized structures, and 1 has removed safety equipment — all flagged automatically by the Change Engine.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#change-detection">Change detection</a><a href="#contradiction">Contradiction</a>
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</div>
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</article>
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<article id="amos-compliance" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Compliance</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Regulatory control, documentation verification, and compliance checking engine.</p>
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<p class="term-long">AMOS Compliance verifies that assets, processes, and documentation meet regulatory requirements. It checks for required signage, accessibility features, fire safety equipment, and other compliance markers. The engine maintains up-to-date regulatory rule sets for multiple jurisdictions.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>A retail chain uses AMOS Compliance to verify that all 200 stores meet accessibility requirements. The engine has flagged 15 stores missing required wheelchair ramps and 8 stores with inadequate emergency lighting.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#amos-inspection">AMOS Inspection</a>
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</div>
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</article>
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<article id="amos-evidence-engine" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Evidence Engine</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Creates traceable evidence chains for audit, insurance, and legal proceedings.</p>
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<p class="term-long">The AMOS Evidence Engine maintains an immutable, auditable chain of custody for every observation. It records capture metadata, processing history, analysis results, and access logs in a tamper-evident format suitable for legal admissibility. Evidence packages include cryptographic hashes and timestamps.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>An insurance dispute reaches court. The AMOS Evidence Engine produces a complete evidence package showing: when the observation was captured, by which device, with what GPS accuracy, what AI analysis was performed, and who accessed the data — all cryptographically signed.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#observation">Observation</a><a href="#exif">EXIF</a><a href="#confidence-score">Confidence Score</a>
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</div>
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</article>
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<article id="amos-fraud" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Fraud</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Skimming detection, manipulated ATM/utility meter detection, and insurance fraud prevention engine.</p>
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<p class="term-long">AMOS Fraud detects fraudulent manipulation of physical devices and systems. It identifies skimming devices on ATMs, tampered utility meters, falsified insurance claims, and other physical fraud vectors. The engine combines visual analysis with pattern matching against known fraud signatures.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>A bank receives a quiXzoom observation of an ATM showing a suspicious card reader attachment. AMOS Fraud analyses the image, compares against known skimming device patterns, and flags a high-probability fraud alert within seconds.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#amos-vision">AMOS Vision</a><a href="#contradiction">Contradiction</a>
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</div>
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</article>
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<article id="amos-identity" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Identity</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Identity verification, document control, liveness detection, and fraud prevention engine.</p>
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<p class="term-long">AMOS Identity verifies that a person is who they claim to be. It processes government-issued IDs, performs liveness checks, and detects document tampering or synthetic identity fraud. The engine is used for KYC processes, access control, and high-trust verification scenarios.</p>
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<div class="term-context">
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<span class="term-context-label">In quiXzoom</span>
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<p>A Zoomer completes identity verification by submitting their passport and a selfie. AMOS Identity verifies the document's authenticity and matches the selfie to the ID photo with 99.7% accuracy.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#kyc">KYC</a><a href="#liveness-check">Liveness check</a><a href="#identity-verification">Identity verification</a>
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</div>
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</article>
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<article id="amos-infrastructure" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Infrastructure</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Road, bridge, pole, sign, and property condition monitoring engine.</p>
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<p class="term-long">AMOS Infrastructure monitors the built environment: roads, bridges, utility poles, traffic signs, buildings, and other fixed assets. It detects deterioration, damage, and changes over time, producing condition indices and maintenance prioritisation scores. The engine is the core capability behind Landvex's infrastructure intelligence products.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>A municipality receives monthly Infrastructure Condition Index updates for all 400 bridges in their county. AMOS Infrastructure has flagged 12 bridges showing progressive deterioration that requires inspection within 30 days.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#amos-vision">AMOS Vision</a><a href="#index">Index</a><a href="#change-detection">Change detection</a>
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</div>
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</article>
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<article id="amos-inspection" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Inspection</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">General quality control and industrial inspection engine.</p>
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<p class="term-long">AMOS Inspection provides general-purpose quality control and inspection capabilities for industrial applications. It detects manufacturing defects, assembly errors, and quality deviations from specification. The engine is configurable for specific industry standards and can be trained on custom defect libraries.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>A manufacturer uses AMOS Inspection to quality-check 10,000 units per day. The engine detects surface scratches, dimensional deviations, and colour mismatches with 99.2% accuracy, reducing manual inspection workload by 80%.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#amos-vision">AMOS Vision</a><a href="#quality-score">Quality score</a>
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</div>
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</article>
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<article id="amos-prediction-engine" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Prediction Engine</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Estimates likely future development based on current observations and historical patterns.</p>
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<p class="term-long">The AMOS Prediction Engine uses historical data, environmental factors, and deterioration models to forecast future asset conditions. It estimates remaining useful life, predicts failure probabilities, and identifies optimal intervention timing. Predictions include confidence intervals and are updated as new observations arrive.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>AMOS Prediction Engine forecasts that Bridge 247 will reach critical condition (Index below 40) in 18 months based on current deterioration rate and traffic load. The recommendation: schedule major maintenance within 12 months to avoid emergency closure.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#index">Index</a><a href="#amos-risk-engine">AMOS Risk Engine</a>
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</div>
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</article>
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<article id="amos-reality-engine" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Reality Engine</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Verifies that an image shows actual reality, not manipulated or synthetic content.</p>
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<p class="term-long">The AMOS Reality Engine detects image manipulation, deepfakes, and synthetic content. It analyses EXIF data, pixel-level artifacts, and lighting consistency to verify that an observation depicts real physical reality. This is critical for legal admissibility and insurance validity.</p>
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<div class="term-context">
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<span class="term-context-label">In the ecosystem</span>
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<p>An insurance claim includes photos of storm damage. The AMOS Reality Engine verifies that the images are authentic, unmanipulated, and captured at the claimed location and time — providing the foundation for claims processing.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#exif">EXIF</a><a href="#confidence-score">Confidence Score</a>
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</div>
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</article>
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<article id="amos-risk-engine" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Risk Engine</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">Converts observations into risk scores and prioritisation rankings.</p>
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<p class="term-long">The AMOS Risk Engine transforms raw observations into actionable risk assessments. It combines condition data, environmental factors, historical failure rates, and consequence analysis to produce risk scores that prioritise maintenance and intervention. Risk scores are normalised and comparable across asset types and geographies.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
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<p>A county's 400 bridges are ranked by risk score. The top 10 highest-risk bridges (scores above 85) receive immediate inspection orders, while the bottom 200 (scores below 30) are scheduled for routine maintenance.</p>
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</div>
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<div class="term-related">
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<span>Related:</span>
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<a href="#amos">AMOS</a><a href="#index">Index</a><a href="#confidence-score">Confidence Score</a><a href="#decision-intelligence">Decision Intelligence</a>
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</div>
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</article>
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<article id="amos-safety" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AMOS Safety</h3>
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<span class="term-category cat-amos">AMOS Engine</span>
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</div>
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<p class="term-short">PPE detection, workplace hazard identification, and risk assessment engine.</p>
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<p class="term-long">AMOS Safety analyses visual data to detect personal protective equipment (PPE) compliance, identify workplace hazards, and assess safety risks. It can detect missing hard hats, unsafe scaffolding, blocked emergency exits, and other safety violations from standard photographs.</p>
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<div class="term-context">
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<span class="term-context-label">In Landvex</span>
|
||
<p>A construction site manager receives daily safety observations from quiXzoom. AMOS Safety has flagged 3 instances of missing fall protection and 1 blocked fire exit across 50 observations — enabling immediate corrective action.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#amos-vision">AMOS Vision</a>
|
||
</div>
|
||
</article>
|
||
<article id="amos-vision" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">AMOS Vision</h3>
|
||
<span class="term-category cat-amos">AMOS Engine</span>
|
||
</div>
|
||
<p class="term-short">Object recognition, anomaly detection, and damage assessment engine.</p>
|
||
<p class="term-long">AMOS Vision is the computer vision engine that identifies objects, detects anomalies, and assesses damage in visual data. It powers infrastructure inspection, property condition monitoring, and safety compliance checking. The engine can detect cracks, corrosion, vegetation encroachment, and other physical defects from standard photographs.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In AMOS</span>
|
||
<p>A bridge inspection observation shows surface cracking. AMOS Vision measures crack width, length, and pattern, comparing against historical data to determine if the damage is new or progressive.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#observation">Observation</a><a href="#change-detection">Change detection</a><a href="#amos-infrastructure">AMOS Infrastructure</a>
|
||
</div>
|
||
</article>
|
||
<article id="analysis-layer" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Analysis Layer</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">The processing tier where raw observations are transformed into structured intelligence by AMOS.</p>
|
||
<p class="term-long">The Analysis Layer sits between the Observation Layer and the Decision Layer. Here, AMOS ingests verified field observations and applies analytical models: object detection, condition scoring, change analysis, pattern recognition, and anomaly detection. The outputs are normalised indices, confidence scores, and contradiction flags. The Analysis Layer is where data becomes insight — where thousands of individual observations are synthesised into coherent, actionable intelligence.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>A thousand roof observations from across a property portfolio enter the Analysis Layer. AMOS identifies that 8% show hail damage, 2% have gutter degradation, and 90% are unchanged. The Analysis Layer outputs a Property Condition Index with regional breakdowns and recommended inspection priorities.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#observation-layer">Observation Layer</a><a href="#decision-layer">Decision Layer</a><a href="#reality-layer">Reality Layer</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="c">
|
||
<div class="letter-heading">
|
||
<h2>C</h2>
|
||
<span class="letter-count">4 terms</span>
|
||
</div>
|
||
<article id="change-detection" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Change detection</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">The automated identification of differences between current observations and historical baseline data — surfacing what has changed in the physical world.</p>
|
||
<p class="term-long">Change detection is a core AMOS capability that compares new observations against prior data to identify additions, removals, or modifications to physical assets. It powers infrastructure monitoring, property condition tracking, and environmental surveillance. Change detection operates at multiple scales: from pixel-level differences in repeated photographs to structural changes identified through computer vision. Each detected change is scored by confidence and severity, and flagged as a Contradiction when it represents an unexpected deviation.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A quarterly observation of a retail park shows three new storefronts and one demolished unit compared to the baseline from six months ago. AMOS change detection automatically flags these modifications, updates the asset registry, and alerts the property manager to verify the changes.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#contradiction">Contradiction</a><a href="#amos">AMOS</a><a href="#observation">Observation</a><a href="#confidence-score">Confidence Score</a>
|
||
</div>
|
||
</article>
|
||
<article id="confidence-score" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Confidence Score</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">A statistical measure of certainty attached to an observation or analysis output, expressed as a percentage.</p>
|
||
<p class="term-long">The Confidence Score quantifies how certain the system is about a specific observation or derived insight. It is computed from multiple signals: EXIF integrity, GPS accuracy, image clarity, cross-validation against historical data, and agreement with other observations of the same asset. A high confidence score (90%+) indicates strong reliability; a low score triggers manual review or additional observation requests. Confidence scores are essential for decision-making — they tell the customer not just what was observed, but how much to trust it.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A bridge inspection observation receives a 94% confidence score based on sharp imagery, precise GPS, and consistency with the previous inspection. An insurer uses this high-confidence observation to approve a maintenance claim without sending an engineer.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#observation">Observation</a><a href="#amos">AMOS</a><a href="#index">Index</a><a href="#contradiction">Contradiction</a>
|
||
</div>
|
||
</article>
|
||
<article id="contradiction" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Contradiction</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">A mismatch between observed reality and expected or previously recorded reality — a signal that something has changed or requires attention.</p>
|
||
<p class="term-long">A Contradiction is flagged when an observation deviates significantly from the expected state: a building that was intact now shows damage, a road that was clear now has a sinkhole, or an asset that was present is now missing. Contradictions are not errors — they are valuable signals. They trigger alerts, escalate priority, and often indicate the most urgent decisions a customer must make. The Contradiction engine cross-references observations against historical data, design specifications, and regulatory baselines.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A quarterly observation of a commercial roof shows new water staining not present in the previous observation. AMOS flags this as a Contradiction, alerting the property manager to schedule repairs before the rainy season — preventing a €50,000 claim.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#observation">Observation</a><a href="#amos">AMOS</a><a href="#confidence-score">Confidence Score</a><a href="#reality-signal">Reality Signal</a>
|
||
</div>
|
||
</article>
|
||
<article id="control-intelligence" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Control Intelligence</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">Descriptive intelligence that tells you the current state of your physical assets — what is, not what to do.</p>
|
||
<p class="term-long">Control Intelligence answers the question: 'What is the current condition of my assets?' It is the foundational layer of insight derived from observations, describing reality as it exists today: which bridges are sound, which buildings have damage, which roads need repair. Control Intelligence is objective, measurable, and verifiable. It is the raw material from which Decision Intelligence is forged — you cannot decide what to do until you know what is.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A municipality receives Control Intelligence showing that 12% of its road bridges have surface cracking, 3% have structural concerns, and 85% are in good condition. This descriptive baseline is the starting point for all maintenance and investment decisions.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#decision-intelligence">Decision Intelligence</a><a href="#observation">Observation</a><a href="#amos">AMOS</a><a href="#index">Index</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="d">
|
||
<div class="letter-heading">
|
||
<h2>D</h2>
|
||
<span class="letter-count">5 terms</span>
|
||
</div>
|
||
<article id="data-controller" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Data controller</h3>
|
||
<span class="term-category cat-legal">Legal</span>
|
||
</div>
|
||
<p class="term-short">The entity that determines the purposes and means of processing personal data — under GDPR, quiXzoom acts as data controller for Zoomer and orderer data.</p>
|
||
<p class="term-long">Under GDPR, the data controller is the legal entity responsible for deciding why and how personal data is processed. quiXzoom is the data controller for data collected from Zoomers (name, contact details, bank details, submission metadata) and from orderers. As data controller, quiXzoom must maintain a Record of Processing Activities (RoPA), appoint a Data Protection Officer where required, and respond to data subject rights requests within statutory timeframes.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>When a Zoomer exercises their right to erasure, they submit a request to quiXzoom as data controller. quiXzoom is then legally responsible for erasing the Zoomer's personal data from its systems and instructing its data processors (including the KYC provider and Stripe) to do the same.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#data-processor">Data processor</a><a href="#gdpr">GDPR</a><a href="#pii">PII</a><a href="#right-to-erasure">Right to erasure</a>
|
||
</div>
|
||
</article>
|
||
<article id="data-processor" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Data processor</h3>
|
||
<span class="term-category cat-legal">Legal</span>
|
||
</div>
|
||
<p class="term-short">An entity that processes personal data on behalf of the data controller, under a Data Processing Agreement (DPA).</p>
|
||
<p class="term-long">Data processors act on the instructions of the data controller and cannot use the data for their own purposes. quiXzoom's data processors include Stripe (payment processing), the KYC provider (identity verification), and cloud infrastructure providers. Each processor relationship must be governed by a Data Processing Agreement that complies with GDPR Article 28, specifying the scope, nature, purpose, and duration of processing.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>The KYC provider that verifies Zoomer identities is a data processor for quiXzoom. They process identity documents and liveness check data strictly for identity verification purposes, under a DPA — they cannot use that data for their own marketing or analytics.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#data-controller">Data controller</a><a href="#gdpr">GDPR</a><a href="#kyc">KYC</a>
|
||
</div>
|
||
</article>
|
||
<article id="data-residency" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Data residency</h3>
|
||
<span class="term-category cat-legal">Legal</span>
|
||
</div>
|
||
<p class="term-short">The geographic location where data is stored — quiXzoom stores all data in the EU (Stockholm, eu-north-1).</p>
|
||
<p class="term-long">Data residency is increasingly important for enterprise customers in regulated industries, who may have legal or policy obligations requiring that data stays within a specific jurisdiction. quiXzoom's infrastructure runs on AWS eu-north-1 (Stockholm), keeping all data within the European Economic Area. This satisfies GDPR third-country transfer restrictions and is a key requirement for public sector and financial services customers.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>An EU public sector orderer requires that all submission data — including GPS coordinates and images — never leaves the EEA. quiXzoom's Stockholm data residency satisfies this requirement by default, without special configuration.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#gdpr">GDPR</a><a href="#s3">S3</a><a href="#pii">PII</a>
|
||
</div>
|
||
</article>
|
||
<article id="decision-intelligence" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Decision Intelligence</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">Actionable recommendations derived from observations through AMOS — the end product of the Landvex ecosystem.</p>
|
||
<p class="term-long">Decision Intelligence is not data. It is not reports. It is the answer to a specific question: Which asset should I repair first? Where should I open my next store? Which claims are fraudulent? Landvex delivers Decision Intelligence by combining quiXzoom observations, AMOS analysis, and domain expertise into structured recommendations that directly enable decisions. Every output includes a confidence score and traceable source data.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<p>A municipality receives a City Health Index of 71 for Norrmalm, down from 78 six months ago. The Decision Intelligence includes: specific deterioration patterns, contributing factors, recommended actions, and confidence intervals. The planner decides which streets to prioritise for maintenance.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#index">Index</a><a href="#confidence-score">Confidence Score</a><a href="#amos">AMOS</a><a href="#control-intelligence">Control Intelligence</a>
|
||
</div>
|
||
</article>
|
||
<article id="decision-layer" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Decision Layer</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">The top tier of the ecosystem where structured intelligence is translated into actionable decisions for customers.</p>
|
||
<p class="term-long">The Decision Layer is where Landvex delivers intelligence products to end customers. It takes the indices, scores, and contradictions from the Analysis Layer and contextualises them for specific use cases: prioritising maintenance budgets, assessing insurance risk, evaluating property investments, ensuring regulatory compliance. The Decision Layer is customer-facing — it speaks the language of business, not technology. Every output is designed to answer one question: 'What decision does this help me make?'</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<p>A property investor uses the Decision Layer to compare three potential acquisitions. The layer delivers a Neighbourhood Quality Index, Infrastructure Risk Score, and Commercial Opportunity Index for each — enabling a data-driven purchase decision in minutes rather than weeks.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#landvex">Landvex</a><a href="#analysis-layer">Analysis Layer</a><a href="#decision-intelligence">Decision Intelligence</a><a href="#index">Index</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="e">
|
||
<div class="letter-heading">
|
||
<h2>E</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="eur" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">EUR</h3>
|
||
<span class="term-category cat-payment">Payment</span>
|
||
</div>
|
||
<p class="term-short">Euro — the secondary currency used in quiXzoom platform and Landvex products, primarily for European markets.</p>
|
||
<p class="term-long">EUR is the secondary currency in the quiXzoom and Landvex ecosystem, used primarily for European markets and SEPA-zone transactions. Zoomers in Eurozone countries receive payouts in EUR via SEPA. Orderers in Europe may be invoiced in EUR. Like USD, EUR is a customer-facing currency — SEK is never shown to customers.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>A Zoomer in Germany receives their payout in EUR via SEPA. An orderer in France is invoiced in EUR for their monthly quiXzoom usage.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#usd">USD</a><a href="#sepa">SEPA</a><a href="#payout">Payout</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="f">
|
||
<div class="letter-heading">
|
||
<h2>F</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="field-observation" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Field observation</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">A structured data collection event performed at a physical location by a verified contributor, producing an observation record.</p>
|
||
<p class="term-long">A field observation is the complete act of capturing reality data on location: travelling to the mission site, following the observation protocol, capturing required imagery from specified vantage points, and submitting the observation package. It is more than the photos — it is the verified, time-stamped, geo-located record of what was observed. Field observations are the foundational input to the entire quiXzoom–AMOS–Landvex pipeline. Without field observations, there is no data, no intelligence, and no decisions.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A Zoomer travels to a bridge inspection mission, follows the protocol (6 photos from specified angles), captures geo-tagged images, and submits. The field observation is verified by AI review and enters the AMOS dataset as a trusted data point.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#observation">Observation</a><a href="#observation-protocol">Observation protocol</a><a href="#zoomer">Zoomer</a><a href="#amos">AMOS</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="g">
|
||
<div class="letter-heading">
|
||
<h2>G</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="gdpr" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">GDPR</h3>
|
||
<span class="term-category cat-legal">Legal</span>
|
||
</div>
|
||
<p class="term-short">General Data Protection Regulation — the EU framework governing how personal data is collected, stored, and processed.</p>
|
||
<p class="term-long">GDPR (Regulation (EU) 2016/679) applies to any organisation processing the personal data of EU residents. For quiXzoom, this covers Zoomer identity data, GPS submission metadata, and orderer contact details. Key obligations include lawful basis for processing, data minimisation, purpose limitation, storage limitation, and honouring data subject rights (access, erasure, portability). All quiXzoom data is stored in EU (Stockholm, eu-north-1) to avoid third-country transfer complications.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A Zoomer in Germany requests a copy of all data quiXzoom holds on them (right of access). quiXzoom must respond within 30 days with a machine-readable export of their submission history, identity data, and payment records — all governed by GDPR Article 15.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#pii">PII</a><a href="#right-to-erasure">Right to erasure</a><a href="#data-controller">Data controller</a><a href="#data-residency">Data residency</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="i">
|
||
<div class="letter-heading">
|
||
<h2>I</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="index" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Index</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">A normalised score (0–100) derived from thousands of observations, representing the condition, risk, or quality of an asset or area.</p>
|
||
<p class="term-long">An Index is the primary output of the AMOS analysis engine — a single number that summarises complex, multi-dimensional observation data into an intuitive, comparable metric. Different indices serve different purposes: the City Health Index measures overall infrastructure condition; the Infrastructure Risk Index quantifies probability of failure; the Commercial Opportunity Index identifies high-potential investment areas. Indices are computed from aggregated observations, weighted by confidence scores, and updated continuously as new observations flow in.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<p>A city's Road Condition Index drops from 72 to 58 over six months. AMOS traces the decline to increased pothole observations in the northern district. The municipality uses this Index to justify a €2M resurfacing budget — with the Index projected to return to 75 post-works.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#observation">Observation</a><a href="#confidence-score">Confidence Score</a><a href="#decision-intelligence">Decision Intelligence</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="l">
|
||
<div class="letter-heading">
|
||
<h2>L</h2>
|
||
<span class="letter-count">3 terms</span>
|
||
</div>
|
||
<article id="landvex" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Landvex</h3>
|
||
<span class="term-category cat-landvex">Landvex</span>
|
||
</div>
|
||
<p class="term-short">Global intelligence infrastructure company — builds decision intelligence from field observations.</p>
|
||
<p class="term-long">Landvex Inc (Houston, TX) develops and sells IT products powered by AMOS engines. Landvex does not sell AI — it sells intelligence based on AMOS. The company combines quiXzoom field data, AMOS analysis, and domain expertise into decision intelligence products for infrastructure owners, governments, insurers, and enterprises. Landvex has operational presence in Europe and North America.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<p>A property investor uses Landvex's Neighbourhood Quality Index to compare three potential acquisitions. The index combines infrastructure condition, safety observations, and commercial activity data into a single comparable score.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#quixzoom">quiXzoom</a><a href="#decision-intelligence">Decision Intelligence</a><a href="#index">Index</a>
|
||
</div>
|
||
</article>
|
||
<article id="landvex-ab" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">LandveX AB</h3>
|
||
<span class="term-category cat-landvex">Landvex</span>
|
||
</div>
|
||
<p class="term-short">The Swedish entity (org.nr 559141-7042) that handles development, operations, and accounting in SEK.</p>
|
||
<p class="term-long">LandveX AB is the Swedish subsidiary responsible for development, operations, and local accounting. It handles all Swedish bookkeeping, VAT, payroll, and supplier invoices in SEK using the BAS chart of accounts and SIE4 format. This is correct per Swedish accounting law. LandveX AB operates the technical infrastructure from Stockholm (AWS eu-north-1).</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>LandveX AB employs the development team, pays Swedish salaries, and files Swedish tax returns. All internal bookkeeping is in SEK, while customer-facing products use USD/EUR.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#landvex">Landvex</a><a href="#landvex-inc">Landvex Inc</a>
|
||
</div>
|
||
</article>
|
||
<article id="landvex-inc" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Landvex Inc</h3>
|
||
<span class="term-category cat-landvex">Landvex</span>
|
||
</div>
|
||
<p class="term-short">The US entity (Houston, TX) that develops and sells IT products powered by AMOS engines.</p>
|
||
<p class="term-long">Landvex Inc is the US-based company that owns and operates the AAMOS platform and quiXzoom product. It is the legal entity responsible for product development, sales, and operations. Landvex Inc is NOT 'AAMOS Group' — there is no such entity. All products are developed and sold by Landvex Inc.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>Landvex Inc holds the intellectual property for AMOS engines, operates the quiXzoom platform, and sells intelligence products to enterprise customers worldwide.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#landvex">Landvex</a><a href="#amos">AMOS</a><a href="#quixzoom">quiXzoom</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="o">
|
||
<div class="letter-heading">
|
||
<h2>O</h2>
|
||
<span class="letter-count">3 terms</span>
|
||
</div>
|
||
<article id="observation" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Observation</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">A verified field record captured through quiXzoom — the fundamental unit of reality data.</p>
|
||
<p class="term-long">An observation is not a photo. It is a structured, verified record of physical reality at a specific place and time. Every observation includes: geolocation (GPS coordinates with accuracy estimate), timestamp (when it was captured), media (images or video), and metadata (device, conditions, mission context). Observations are quality-checked by AI before entering the dataset. They are the raw material from which all Landvex intelligence is derived.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A Zoomer captures 6 images of a road segment. Each image becomes part of an observation that includes GPS, timestamp, and mission context. AI review verifies the observation before it enters the dataset.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#mission">Mission</a><a href="#zoomer">Zoomer</a><a href="#amos">AMOS</a><a href="#reality-signal">Reality Signal</a>
|
||
</div>
|
||
</article>
|
||
<article id="observation-layer" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Observation Layer</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">The field data collection tier where Zoomers capture verified observations of physical assets and infrastructure.</p>
|
||
<p class="term-long">The Observation Layer is the foundation of the ecosystem — the interface between physical reality and digital intelligence. It encompasses the quiXzoom mobile app, Zoomer network, mission management system, and AI review pipeline. Its purpose is to produce high-quality, trustworthy observations at scale. The Observation Layer guarantees data integrity through geo-tagging, timestamp verification, and automated quality checks. Without a robust Observation Layer, everything above it — analysis, indices, decisions — is built on sand.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>The Observation Layer deploys 500 Zoomers across a metropolitan area to capture quarterly building condition observations. Each observation is verified for GPS accuracy, image quality, and specification compliance before entering the Analysis Layer.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#observation">Observation</a><a href="#zoomer">Zoomer</a><a href="#reality-layer">Reality Layer</a><a href="#analysis-layer">Analysis Layer</a>
|
||
</div>
|
||
</article>
|
||
<article id="ouroboros" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Ouroboros</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">The internal platform architecture that powers quiXzoom, AAMOS, and Landvex — a self-referential system where the platform monitors and improves itself.</p>
|
||
<p class="term-long">Ouroboros is the internal name for the technical architecture that underpins the entire Landvex ecosystem. It encompasses the microservices, data pipelines, AI models, and infrastructure that enable quiXzoom field data collection, AMOS analysis, and Landvex intelligence delivery. The name reflects the system's self-monitoring nature: observations feed analysis, analysis drives decisions, decisions generate new observations. Ouroboros is not a customer-facing product — it is the technical foundation.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>quiXzoom runs as an Ouroboros instance — using the same core infrastructure as other Landvex products but configured for the quiXzoom marketplace model. Ouroboros handles user management, mission orchestration, AI review, and data pipelines.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#quixzoom">quiXzoom</a><a href="#landvex">Landvex</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="p">
|
||
<div class="letter-heading">
|
||
<h2>P</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="pii" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">PII</h3>
|
||
<span class="term-category cat-legal">Legal</span>
|
||
</div>
|
||
<p class="term-short">Personally Identifiable Information — any data that can directly or indirectly identify a specific individual.</p>
|
||
<p class="term-long">PII encompasses obvious identifiers (name, email, address, IBAN) as well as indirect identifiers that, in combination, could identify someone (GPS traces, device fingerprints, submission patterns). Under GDPR, quiXzoom treats PII with strict controls: it is collected only where necessary, retained only as long as required, and never shared with orderers in identifiable form. Zoomer submissions are delivered to orderers without attached Zoomer identity — orderers receive field data, not person data.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>When an orderer receives a completed submission, they see the images, GPS coordinates, and timestamp — but not the Zoomer's name, account details, or any identifying information. The Zoomer's identity is pseudonymised in normal operation, with full identification available only under legal process.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#gdpr">GDPR</a><a href="#data-controller">Data controller</a><a href="#right-to-erasure">Right to erasure</a><a href="#data-residency">Data residency</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="q">
|
||
<div class="letter-heading">
|
||
<h2>Q</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="quixzoom-auth-core" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">quiXzoom Auth Core</h3>
|
||
<span class="term-category cat-product">Product</span>
|
||
</div>
|
||
<p class="term-short">Standalone authentication product sold via AAMOS — includes Auth Foundation, KYZ Engine, Company Registry, and Payout Engine.</p>
|
||
<p class="term-long">quiXzoom Auth Core is a standalone authentication and identity product that can be integrated into any application. It includes: Auth Foundation (email/Google + phone + password), KYZ Engine (Know Your Zoomer — identity verification), Company Registry (sole proprietorship / registered company), and Payout Engine (Stripe Connect, Frilans Finans, bank transfers). Auth Core is sold as a product via AAMOS and is used by quiXzoom and other Landvex products.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>A third-party app developer integrates quiXzoom Auth Core to handle user authentication, identity verification, and payouts — without building these complex systems themselves.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#kyc">KYC</a><a href="#stripe-connect">Stripe Connect</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="r">
|
||
<div class="letter-heading">
|
||
<h2>R</h2>
|
||
<span class="letter-count">4 terms</span>
|
||
</div>
|
||
<article id="reality-layer" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Reality Layer</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">The physical world itself — the assets, infrastructure, and environments that the ecosystem observes, analyses, and optimises.</p>
|
||
<p class="term-long">The Reality Layer is everything physical: roads, bridges, buildings, coastlines, forests, power lines, water pipes. It is the domain that exists independently of any observation system — the ground truth that the ecosystem seeks to understand and improve. The Reality Layer is not digital; it is the actual world that customers own, manage, insure, and invest in. Every observation is a measurement of the Reality Layer; every index is a summary of its state; every decision is an intervention upon it.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>A city's Reality Layer comprises 1,200 km of roads, 400 bridges, 50 tunnels, and 2,000 public buildings. The ecosystem's purpose is to create a complete, accurate, up-to-date digital understanding of this Reality Layer — enabling better decisions about maintenance, investment, and risk.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#observation-layer">Observation Layer</a><a href="#analysis-layer">Analysis Layer</a><a href="#decision-layer">Decision Layer</a><a href="#reality-signal">Reality Signal</a>
|
||
</div>
|
||
</article>
|
||
<article id="reality-signal" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Reality Signal</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">A measurable, verifiable observation that describes a specific aspect of the physical world.</p>
|
||
<p class="term-long">A Reality Signal is any observation that carries information about the state of physical reality: a photo of a cracked bridge deck, a GPS-tagged image of flood damage, a timestamped record of a building's facade. Reality Signals are the raw inputs to the Analysis Layer. They are distinguished from synthetic or inferred data by their direct connection to physical measurement — a Zoomer was there, the camera captured light from the actual scene, the GPS recorded the actual coordinates. Reality Signals are the antidote to assumptions and desk-based estimates.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>An insurer receives a Reality Signal showing hail damage to a factory roof — captured 6 hours after the storm, with GPS confirming the location, timestamp proving immediacy, and image quality enabling damage severity assessment. This Reality Signal replaces the traditional adjuster visit.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#observation">Observation</a><a href="#reality-layer">Reality Layer</a><a href="#confidence-score">Confidence Score</a><a href="#amos">AMOS</a>
|
||
</div>
|
||
</article>
|
||
<article id="rexo" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">REXO</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">Landvex's coding pipeline for automated software development, testing, and deployment.</p>
|
||
<p class="term-long">REXO is Landvex's internal coding pipeline that automates software development tasks. It runs via /opt/amos/rexo-build/, with tasks defined in plan/PLAN.json and executed by parallel workers. REXO orchestrates code generation, testing, and deployment across the ecosystem. All coding tasks should be routed through REXO rather than manual file uploads or direct server modifications.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<p>When a new feature is needed for the quiXzoom API, the task is added to REXO's PLAN.json. REXO workers generate the code, run tests, and deploy to staging — all without manual intervention.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#ouroboros">Ouroboros</a><a href="#amos">AMOS</a>
|
||
</div>
|
||
</article>
|
||
<article id="right-to-erasure" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Right to erasure</h3>
|
||
<span class="term-category cat-legal">Legal</span>
|
||
</div>
|
||
<p class="term-short">A GDPR right allowing individuals to request deletion of their personal data from a platform's systems.</p>
|
||
<p class="term-long">Under GDPR Article 17, individuals have the right to request erasure of their personal data when it is no longer necessary for the purpose it was collected, when they withdraw consent, or when they object to processing. For quiXzoom, this means Zoomers can request deletion of their account and all associated personal data at any time via the app settings. Certain data may need to be retained for limited periods to satisfy legal obligations (e.g. financial records under tax law) even after an erasure request.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A Zoomer decides to stop using quiXzoom and requests erasure. quiXzoom deletes their profile, contact data, and GPS history. Some financial transaction records are retained for 7 years as required by EU accounting regulation — this is disclosed in quiXzoom's privacy policy and does not affect the Zoomer's ability to stop using the platform.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#gdpr">GDPR</a><a href="#pii">PII</a><a href="#data-controller">Data controller</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="u">
|
||
<div class="letter-heading">
|
||
<h2>U</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="usd" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">USD</h3>
|
||
<span class="term-category cat-payment">Payment</span>
|
||
</div>
|
||
<p class="term-short">United States Dollar — the primary currency used in quiXzoom platform and Landvex products.</p>
|
||
<p class="term-long">USD is the main currency throughout the quiXzoom platform and Landvex ecosystem. All customer-facing pricing, payouts, and product fees are denominated in USD (or EUR where specified). This is a locked policy — SEK is never used in customer-facing copy, sites, documents, or communication. The only exception is LandveX AB's internal Swedish accounting.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In the ecosystem</span>
|
||
<p>A mission pays $15 (USD). A Zoomer's earnings are displayed in USD. Landvex enterprise contracts are priced in USD. This is consistent across all customer touchpoints.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#eur">EUR</a><a href="#payout">Payout</a><a href="#platform-fee">Platform fee</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="v">
|
||
<div class="letter-heading">
|
||
<h2>V</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="vims" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">VIMS</h3>
|
||
<span class="term-category cat-product">Product</span>
|
||
</div>
|
||
<p class="term-short">Visual Infrastructure Monitoring System — a Landvex product for continuous infrastructure condition assessment.</p>
|
||
<p class="term-long">VIMS (Visual Infrastructure Monitoring System) is a Landvex product that uses quiXzoom observations and AMOS analysis to provide continuous monitoring of infrastructure assets. It delivers condition indices, change alerts, and maintenance prioritisation for roads, bridges, buildings, and utilities. VIMS replaces periodic manual inspections with ongoing, data-driven condition assessment.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<p>A municipality subscribes to VIMS for their 400 bridges. Monthly observations from quiXzoom Zoomers feed into VIMS, which updates condition indices, flags new damage, and generates maintenance priority lists — all automatically.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#quixzoom">quiXzoom</a><a href="#index">Index</a><a href="#change-detection">Change detection</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<div class="last-updated">Last updated: July 14, 2026</div>
|
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|
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