Bernt
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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
1068 lines
83 KiB
HTML
1068 lines
83 KiB
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<title>Glossary — aamos</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 aamos 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">16 terms</span>
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</div>
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<article id="ai-review" class="term-card">
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<div class="term-header">
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<h3 class="term-name">AI review</h3>
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<span class="term-category cat-technical">Technical</span>
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</div>
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<p class="term-short">Automated quality and specification check run on every submission before payment is triggered.</p>
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<p class="term-long">AI review is the first gate a submitted set of photos passes through. The system checks geo-accuracy (is the submission within the mission geofence?), image quality (sharpness, exposure, occlusion), specification compliance (correct angles, required elements present), and EXIF integrity (timestamp within time window, GPS data present and consistent). The result is either an approval — triggering payout — or a rejection with a reason code that the Zoomer can act on.</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 submits 6 photos of a road segment. AI review verifies GPS coordinates match the mission location, timestamp falls within the claimed time window, and all required vantage points are covered — typically completing within 90 seconds of upload.</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="#approval">Approval</a><a href="#rejection">Rejection</a><a href="#submission">Submission</a><a href="#approval-rate">Approval rate</a>
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</div>
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</article>
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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>
|
||
<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">
|
||
<h3 class="term-name">AMOS Inspection</h3>
|
||
<span class="term-category cat-amos">AMOS Engine</span>
|
||
</div>
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||
<p class="term-short">General quality control and industrial inspection engine.</p>
|
||
<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>
|
||
<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>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#amos-vision">AMOS Vision</a><a href="#quality-score">Quality score</a>
|
||
</div>
|
||
</article>
|
||
<article id="amos-prediction-engine" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">AMOS Prediction Engine</h3>
|
||
<span class="term-category cat-amos">AMOS Engine</span>
|
||
</div>
|
||
<p class="term-short">Estimates likely future development based on current observations and historical patterns.</p>
|
||
<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>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<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>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#index">Index</a><a href="#amos-risk-engine">AMOS Risk Engine</a>
|
||
</div>
|
||
</article>
|
||
<article id="amos-reality-engine" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">AMOS Reality Engine</h3>
|
||
<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>
|
||
<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>
|
||
</div>
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||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#exif">EXIF</a><a href="#confidence-score">Confidence Score</a>
|
||
</div>
|
||
</article>
|
||
<article id="amos-risk-engine" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">AMOS Risk Engine</h3>
|
||
<span class="term-category cat-amos">AMOS Engine</span>
|
||
</div>
|
||
<p class="term-short">Converts observations into risk scores and prioritisation rankings.</p>
|
||
<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>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In Landvex</span>
|
||
<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>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#amos">AMOS</a><a href="#index">Index</a><a href="#confidence-score">Confidence Score</a><a href="#decision-intelligence">Decision Intelligence</a>
|
||
</div>
|
||
</article>
|
||
<article id="amos-safety" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">AMOS Safety</h3>
|
||
<span class="term-category cat-amos">AMOS Engine</span>
|
||
</div>
|
||
<p class="term-short">PPE detection, workplace hazard identification, and risk assessment engine.</p>
|
||
<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>
|
||
<div class="term-context">
|
||
<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>
|
||
<article id="api" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">API</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">Application Programming Interface — the programmatic interface through which orderers access mission data, submission results, and platform services.</p>
|
||
<p class="term-long">The quiXzoom API is a REST-style HTTP API that allows orderers to submit mission briefs, monitor mission status, retrieve completed submission packages (images, metadata, GeoJSON), and receive real-time updates via webhooks. Responses are in JSON format. The API uses OAuth 2.0 for authentication and HMAC-SHA256 for webhook signature verification.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>An insurance company integrates the quiXzoom API into their claims management system. When a claim is filed for a specific address, an API call automatically creates a mission, and approved submission data flows directly back into the claims system without manual intervention.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#webhook">Webhook</a><a href="#oauth">OAuth</a><a href="#json">JSON</a><a href="#hmac-sha256">HMAC-SHA256</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="c">
|
||
<div class="letter-heading">
|
||
<h2>C</h2>
|
||
<span class="letter-count">5 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>
|
||
<article id="csv" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">CSV</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">Comma-Separated Values — a plain-text tabular data format available as a bulk export option for orderers.</p>
|
||
<p class="term-long">CSV exports from quiXzoom contain one row per submission, with columns for mission ID, submission timestamp, GPS coordinates, approval status, Zoomer tier (anonymised), and other metadata fields. CSV is suitable for bulk import into spreadsheet tools, BI platforms, or legacy claims systems that do not support JSON or API integration. CSV exports are available on demand via the orderer dashboard or API.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A municipality uses CSV exports to import quiXzoom road survey data into their GIS system on a weekly basis, plotting submission coordinates alongside existing infrastructure records.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#json">JSON</a><a href="#geojson">GeoJSON</a><a href="#api">API</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="d">
|
||
<div class="letter-heading">
|
||
<h2>D</h2>
|
||
<span class="letter-count">4 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>
|
||
</section>
|
||
<section class="letter-section" id="e">
|
||
<div class="letter-heading">
|
||
<h2>E</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="exif" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">EXIF</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">Exchangeable Image File Format — metadata embedded in image files, including GPS coordinates, capture timestamp, device model, and camera settings.</p>
|
||
<p class="term-long">EXIF metadata is written into image files at capture time by the device camera. For quiXzoom, EXIF is the primary evidence layer: it carries the GPS coordinates where the photo was taken, the precise timestamp, and device identifiers. AI review cross-validates EXIF GPS coordinates against the mission geofence, and EXIF timestamps against the mission time window. EXIF data is preserved in delivery packages to orderers as part of the chain-of-custody record.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A Zoomer photographs a flood-damaged road. The image's EXIF data records 59.3312°N, 18.0685°E at 09:47:23 UTC. AI review checks this GPS fix is within the mission's 50-metre geofence and the timestamp falls within the claimed time window — both pass, contributing to approval.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#geo-tagged">Geo-tagged</a><a href="#gps">GPS</a><a href="#timestamp">Timestamp</a><a href="#ai-review">AI review</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">6 terms</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>
|
||
<article id="geo-accuracy" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Geo-accuracy</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">The closeness of a recorded GPS coordinate to the actual physical position — typically expressed in metres of error radius.</p>
|
||
<p class="term-long">Geo-accuracy is determined by GPS signal quality, which is affected by urban canyon effects, tree canopy, atmospheric conditions, and device hardware. Consumer smartphone GPS typically achieves 3–10 metre accuracy in open conditions. quiXzoom's mission geofences are sized to account for realistic geo-accuracy limits — a mission for a specific building typically uses a 30–50 metre compliance radius. AI review evaluates whether the recorded GPS position is within the mission's accepted tolerance.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A Zoomer photographs a bridge in a narrow urban gorge where GPS signals reflect off buildings. Their device records a position 18 metres from the mission pin — within the 30-metre geofence tolerance — so the submission passes the geo-accuracy check.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#gps">GPS</a><a href="#geo-tagged">Geo-tagged</a><a href="#exif">EXIF</a><a href="#ai-review">AI review</a>
|
||
</div>
|
||
</article>
|
||
<article id="geo-tagged" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Geo-tagged</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">An image embedded with GPS coordinates at capture time, proving where the photo was physically taken.</p>
|
||
<p class="term-long">Geo-tagging writes latitude, longitude, and altitude data into the image's EXIF metadata at the moment of capture. All quiXzoom submissions must be geo-tagged — this is a technical requirement enforced by the app. Submissions from devices with disabled location services are rejected before upload. Geo-tagging is the foundation of quiXzoom's location verification: without it, there is no way to confirm the photo was taken at the correct mission location.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>The quiXzoom app requires location permissions to function. When a Zoomer captures a photo in-app, GPS coordinates are written to EXIF automatically. A Zoomer cannot submit photos taken in a regular camera app that disabled geo-tagging — the submission would fail AI review immediately.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#gps">GPS</a><a href="#exif">EXIF</a><a href="#geo-accuracy">Geo-accuracy</a><a href="#geojson">GeoJSON</a>
|
||
</div>
|
||
</article>
|
||
<article id="geofence" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Geofence</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">A virtual geographic boundary defining where a mission must be completed — submissions outside the geofence are automatically rejected.</p>
|
||
<p class="term-long">A geofence is a digitally defined perimeter around a mission location, typically represented as a circle with a radius of 30–100 metres or as a GeoJSON Polygon for complex shapes. When a Zoomer submits photos, AI review checks the EXIF GPS coordinates against the mission geofence. If the submission was captured outside the geofence, it is rejected with a location mismatch reason. Geofences ensure data integrity by guaranteeing that observations were physically captured at the correct location, preventing fraudulent or mistaken submissions.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A bridge inspection mission has a 50-metre radius geofence centred on the bridge coordinates. A Zoomer submits photos from 200 metres away — the GPS coordinates in EXIF fall outside the geofence, and AI review rejects the submission with 'Location mismatch — outside geofence.'</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#gps">GPS</a><a href="#geo-accuracy">Geo-accuracy</a><a href="#exif">EXIF</a><a href="#ai-review">AI review</a>
|
||
</div>
|
||
</article>
|
||
<article id="geojson" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">GeoJSON</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">An open standard format (RFC 7946) for encoding geographic data structures using JSON.</p>
|
||
<p class="term-long">GeoJSON is used by quiXzoom to define mission boundaries (geofences), to encode submission GPS paths, and to deliver location data to orderers in a format compatible with GIS platforms, mapping tools, and geographic analysis software. A mission boundary is represented as a GeoJSON Polygon; individual submission GPS points as GeoJSON Point Features with properties including timestamp, approval status, and submission ID.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A municipality ordering a road condition survey receives their submission data as a GeoJSON FeatureCollection, which they import directly into QGIS to visualise submission density alongside their road network layer.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#geo-tagged">Geo-tagged</a><a href="#json">JSON</a><a href="#api">API</a><a href="#mission-brief">Mission brief</a>
|
||
</div>
|
||
</article>
|
||
<article id="gps" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">GPS</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">Global Positioning System — the satellite-based navigation system used to determine precise geographic location of submissions.</p>
|
||
<p class="term-long">GPS is the primary location technology in smartphones, providing latitude, longitude, and altitude data by triangulating signals from multiple satellites. All quiXzoom submissions depend on GPS for location verification. The system is augmented by network-based location (Wi-Fi and cell triangulation) in environments where satellite signals are weak — combined, these signals provide the geo-accuracy required for mission compliance checks.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>Every time a Zoomer captures a photo for a mission, the device GPS is sampled. This fix — along with the capture timestamp — is embedded in EXIF and used by AI review to verify the submission was made at the correct location within the correct time window.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#geo-tagged">Geo-tagged</a><a href="#geo-accuracy">Geo-accuracy</a><a href="#exif">EXIF</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="h">
|
||
<div class="letter-heading">
|
||
<h2>H</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="hmac-sha256" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">HMAC-SHA256</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">A cryptographic signature method used to verify that webhook payloads were genuinely sent by quiXzoom and were not tampered with in transit.</p>
|
||
<p class="term-long">HMAC (Hash-based Message Authentication Code) with SHA-256 works by combining the webhook payload with a shared secret key to produce a unique signature. quiXzoom attaches this signature to every webhook request in the X-QuiXzoom-Signature header. The receiving system recomputes the signature using the same secret and compares — if they match, the payload is authentic and unmodified. This is industry-standard practice used by Stripe, GitHub, and other major webhook providers.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>An insurance company's claims system receives a webhook when a submission is approved. Before processing the data, their server verifies the HMAC-SHA256 signature against their shared webhook secret — confirming the event genuinely originated from quiXzoom and was not injected by a third party.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#webhook">Webhook</a><a href="#api">API</a><a href="#oauth">OAuth</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="j">
|
||
<div class="letter-heading">
|
||
<h2>J</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="json" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">JSON</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">JavaScript Object Notation — the lightweight data format used for all quiXzoom API responses and webhook payloads.</p>
|
||
<p class="term-long">JSON is a human-readable, language-agnostic data serialisation format that has become the de facto standard for web APIs. quiXzoom's REST API delivers all responses as JSON, including mission details, submission metadata, location data, and approval status. Webhook event payloads are also JSON. The format is natively supported in virtually all programming languages and integration platforms, making it straightforward to process quiXzoom data in any technical environment.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>When an orderer queries the API for a completed submission, the response is a JSON object containing the submission ID, approval status, GPS coordinates, EXIF metadata summary, and signed URLs to retrieve the image files from S3.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#api">API</a><a href="#webhook">Webhook</a><a href="#geojson">GeoJSON</a><a href="#csv">CSV</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="l">
|
||
<div class="letter-heading">
|
||
<h2>L</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<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">5 terms</span>
|
||
</div>
|
||
<article id="oauth" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">OAuth</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">Open Authorization — the industry-standard protocol used by quiXzoom's API for secure, delegated authentication.</p>
|
||
<p class="term-long">OAuth 2.0 allows orderers to authenticate API requests without sharing credentials. Orderers obtain access tokens through the OAuth flow, which are then included in API request headers. Tokens can be scoped (read-only, write, full access) and have configurable expiry periods. OAuth 2.0 is also used for third-party integrations — for example, allowing an orderer's claims management platform to connect to quiXzoom on behalf of their organisation.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>An insurance company's IT team sets up an OAuth 2.0 client in the quiXzoom orderer portal, receives client credentials, and uses these to generate short-lived access tokens for their claims integration server. Tokens expire after 1 hour and are automatically refreshed.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#api">API</a><a href="#webhook">Webhook</a><a href="#hmac-sha256">HMAC-SHA256</a>
|
||
</div>
|
||
</article>
|
||
<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="observation-protocol" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Observation protocol</h3>
|
||
<span class="term-category cat-core">Core</span>
|
||
</div>
|
||
<p class="term-short">The standardised set of instructions defining what to capture, from which angles, and under what conditions for a given mission type.</p>
|
||
<p class="term-long">The observation protocol is the rulebook for field data collection. It specifies the number of photos required, the vantage points (distance, angle, height), the elements that must be in frame, lighting conditions, and any safety or access constraints. Protocols are mission-type specific: a bridge inspection protocol differs from a property documentation protocol. Following the protocol is essential for approval — AI review checks compliance against the protocol, and deviations result in rejection. Protocols are developed by domain experts and refined through feedback from AI review outcomes.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A roof condition mission protocol requires: 1 overview shot from street level, 1 ridge-line photo, 2 slope photos (one per side), and 1 detail of any visible damage — all captured during daylight with the sun behind the photographer. A Zoomer who submits only 3 photos or captures at night will fail protocol compliance.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#observation">Observation</a><a href="#field-observation">Field observation</a><a href="#mission-brief">Mission brief</a><a href="#ai-review">AI review</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">2 terms</span>
|
||
</div>
|
||
<article id="quality-score" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Quality score</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">A composite rating of an observation's technical fitness — sharpness, exposure, framing, and protocol compliance — distinct from the confidence score.</p>
|
||
<p class="term-long">The Quality Score evaluates the technical and aesthetic properties of submitted media independent of location or metadata verification. It measures image sharpness (absence of motion blur or focus issues), exposure (neither under- nor over-exposed), framing (subject completeness and composition), and protocol compliance (correct angles, required elements visible). Quality Score is a component of the overall Confidence Score but is computed separately to give orderers visibility into the technical standard of their data. Low quality scores trigger coaching for Zoomers and may result in rejection if minimum thresholds are not met.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>A Zoomer submits photos that are correctly geo-located and within the time window, but two images are slightly blurry due to camera shake. AI review assigns a Quality Score of 62 — below the 75-point threshold — and rejects the submission with 'Image quality: motion blur detected.' The Zoomer receives automated coaching on stabilisation techniques.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#confidence-score">Confidence Score</a><a href="#ai-review">AI review</a><a href="#observation">Observation</a><a href="#approval">Approval</a>
|
||
</div>
|
||
</article>
|
||
<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="s">
|
||
<div class="letter-heading">
|
||
<h2>S</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="s3" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">S3</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">Amazon Simple Storage Service — the object storage platform used to store all quiXzoom submission images, securely and at scale.</p>
|
||
<p class="term-long">S3 (AWS eu-north-1) is quiXzoom's primary file store for submission images. Uploaded images are stored with server-side encryption, access is controlled via IAM policies, and orderers receive time-limited signed URLs to retrieve submission packages — images are never publicly accessible by default. S3's durability (99.999999999%) and scalability make it appropriate for a platform expecting high submission volumes during surge events.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>When a Zoomer uploads 6 photos for a mission, they are stored in S3 eu-north-1. When the orderer queries the API for the completed submission, they receive signed URLs valid for 24 hours to download the images — after which the URLs expire and a new API call is required.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#data-residency">Data residency</a><a href="#api">API</a><a href="#submission">Submission</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<section class="letter-section" id="t">
|
||
<div class="letter-heading">
|
||
<h2>T</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="timestamp" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Timestamp</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">The precise date and time recorded in a photo's EXIF metadata, proving when the image was captured.</p>
|
||
<p class="term-long">Timestamps are a critical element of quiXzoom's chain-of-custody system. The EXIF timestamp records the device's date and time at the moment of capture, and is cross-validated by AI review against the mission's claimed time window. For legal and insurance use cases, the timestamp (combined with GPS data) provides tamper-evident proof that the documentation was made at the right place at the right time. quiXzoom also records server-side ingestion timestamps for each submission to supplement device-side EXIF data.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>An insurer requires flood documentation captured within 6 hours of a storm event for their claims validity window. The EXIF timestamp on each submitted photo, cross-referenced with server ingestion time, provides the evidence that the documentation falls within the required window — suitable for claims processing and, if necessary, legal proceedings.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#exif">EXIF</a><a href="#time-window">Time window</a><a href="#geo-tagged">Geo-tagged</a><a href="#ai-review">AI review</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="w">
|
||
<div class="letter-heading">
|
||
<h2>W</h2>
|
||
<span class="letter-count">1 term</span>
|
||
</div>
|
||
<article id="webhook" class="term-card">
|
||
<div class="term-header">
|
||
<h3 class="term-name">Webhook</h3>
|
||
<span class="term-category cat-technical">Technical</span>
|
||
</div>
|
||
<p class="term-short">An automatic HTTP POST notification sent by quiXzoom to an orderer's system when a defined event occurs — typically submission approval.</p>
|
||
<p class="term-long">Webhooks enable real-time integration between quiXzoom and orderer systems without polling. When a submission is approved (or rejected, or a mission is created), quiXzoom sends a JSON payload to the orderer's configured webhook URL. The payload includes the event type, mission ID, submission ID, GPS coordinates, approval status, and image download URLs. Orderers must verify webhook authenticity using HMAC-SHA256 signature verification. Undelivered webhooks are retried with exponential backoff over 24 hours.</p>
|
||
<div class="term-context">
|
||
<span class="term-context-label">In quiXzoom</span>
|
||
<p>An insurance company's claims system has a quiXzoom webhook endpoint. When a flood damage submission is approved, quiXzoom sends a POST request within seconds. The claims system receives the event, automatically attaches the images to the open claim, and updates the claim status to 'documented' — with no manual intervention required.</p>
|
||
</div>
|
||
<div class="term-related">
|
||
<span>Related:</span>
|
||
<a href="#api">API</a><a href="#hmac-sha256">HMAC-SHA256</a><a href="#json">JSON</a><a href="#approval">Approval</a>
|
||
</div>
|
||
</article>
|
||
</section>
|
||
<div class="last-updated">Last updated: July 14, 2026</div>
|
||
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|
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|
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|
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|
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