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< title > Data Quality Standards — Landvex< / title >
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< a href = "/" style = "color: #007AFF; text-decoration: none;" > Home< / a > / < span > data-quality< / span >
< / nav >
< nav >
< a class = "nav-logo" href = "https://landvex.com/" > ← Back to Landvex< / a >
< a class = "btn btn-outline" href = "#contact" > Get in touch → < / a >
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<!-- HERO -->
< section class = "hero" >
< div class = "hero-eyebrow" > Landvex — Data Quality< / div >
< h1 > How we ensure data quality.< / h1 >
< p class = "hero-sub" > Every observation in Landvex passes through a multi-stage verification chain before it influences a score. Confidence is earned, not assumed.< / p >
< div class = "hero-ctas" >
< a class = "btn btn-lg" href = "#verification" > See the process → < / a >
< a class = "btn btn-outline btn-lg" href = "#enterprise" > Enterprise API< / a >
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< / section >
<!-- VERIFICATION CHAIN -->
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< div class = "section-label" > Verification chain< / div >
< h2 class = "section-title" > Five stages from observation to score< / h2 >
< p class = "section-sub" > Every data point undergoes sequential verification. A failure at any stage removes the observation from the scoring pool.< / p >
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< div class = "chain-num" > 1< / div >
< div class = "chain-body" >
< h3 > Contributor identity verification< / h3 >
< p > Each contributor is identity-verified before any observations are accepted. Device binding, location history, and activity patterns are monitored continuously for anomalies.< / p >
< / div >
< / div >
< div class = "chain-step" >
< div class = "chain-num" > 2< / div >
< div class = "chain-body" >
< h3 > GPS validation< / h3 >
< p > GPS coordinates are validated against declared location, movement patterns, and historical data. Spoofed, interpolated, or implausible locations are automatically rejected.< / p >
< / div >
< / div >
< div class = "chain-step" >
< div class = "chain-num" > 3< / div >
< div class = "chain-body" >
< h3 > AI image review< / h3 >
< p > Computer vision models review submitted images for specification compliance, content authenticity, and data integrity. Manipulated or non-compliant images are flagged and excluded.< / p >
< / div >
< / div >
< div class = "chain-step" >
< div class = "chain-num" > 4< / div >
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< h3 > Specification compliance< / h3 >
< p > Observations are checked against task specifications: correct asset type, required angles, mandatory data fields, and temporal constraints. Incomplete submissions are rejected, not approximated.< / p >
< / div >
< / div >
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< div class = "chain-num" > 5< / div >
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< h3 > Human spot-check (borderline cases)< / h3 >
< p > Edge cases, borderline confidence scores, and flagged observations are reviewed by trained human analysts before entering the scoring pipeline. Automated decisions are not final.< / p >
< / div >
< / div >
< / div >
< / div >
< / section >
<!-- QUALITY METRICS -->
< section id = "metrics" style = "background: var(--bg-dark)" >
< div class = "container" >
< div class = "section-label" > Quality metrics< / div >
< h2 class = "section-title" > Targets we hold ourselves to< / h2 >
< p class = "section-sub" > Published targets create accountability. These are the standards Landvex commits to across every data collection mission.< / p >
< div class = "metrics-grid" >
< div class = "metric-card" style = "cursor:pointer;" onclick = "location.href='#confidence'" role = "button" tabindex = "0" >
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< div class = "metric-value" > > 85%< / div >
< div class = "metric-target" > Approval rate target< / div >
< div class = "metric-label" > Observations that pass all verification stages. Below-threshold batches trigger quality review.< / div >
< / div >
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< div class = "metric-value" > < 10m< / div >
< div class = "metric-target" > Geo-accuracy< / div >
< div class = "metric-label" > GPS accuracy tolerance. Observations outside this threshold are flagged for manual review.< / div >
< / div >
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< div class = "metric-value" > < 90 days< / div >
< div class = "metric-target" > Temporal freshness< / div >
< div class = "metric-label" > Maximum observation age per data point before requiring refresh. Older data is down-weighted in scoring.< / div >
< / div >
< / div >
< / div >
< / section >
<!-- CONFIDENCE SCORING -->
< section id = "confidence" style = "background:var(--surface)" >
< div class = "container" >
< div class = "section-label" > Confidence scoring< / div >
< h2 class = "section-title" > Every data point has a confidence score< / h2 >
< p class = "section-sub" > Landvex attaches a 0– 100 confidence score to every data point. Scores are transparent, composable, and auditable.< / p >
< div class = "confidence-wrap" >
< div class = "confidence-header" >
< div class = "confidence-score-display" >
< span class = "score-num" > 87< / span >
< span class = "score-label" > confidence< / span >
< / div >
< div class = "confidence-header-text" >
< h3 > Confidence score: 87/100< / h3 >
< p > Example: Norrmalm commercial vacancy observation, June 2026. Scored across four independent factors.< / p >
< / div >
< / div >
< div class = "confidence-factors" >
< div class = "factor-row" >
< span class = "factor-name" > Recency of observation< / span >
< span class = "factor-weight" > 32 pts< / span >
< / div >
< div class = "factor-row" >
< span class = "factor-name" > Source count< / span >
< span class = "factor-weight" > 28 pts< / span >
< / div >
< div class = "factor-row" >
< span class = "factor-name" > Cross-validation against other sources< / span >
< span class = "factor-weight" > 15 pts< / span >
< / div >
< div class = "factor-row" >
< span class = "factor-name" > Reviewer consensus< / span >
< span class = "factor-weight" > 12 pts< / span >
< / div >
< / div >
< / div >
< / div >
< / section >
<!-- CONTRADICTION DETECTION -->
< section id = "contradiction" style = "background:#001836" >
< div class = "container" >
< div class = "section-label" style = "color:rgba(255,255,255,.75)" > Contradiction detection< / div >
< h2 class = "section-title" style = "color: #ffffff" > Flagging what doesn’ t add up< / h2 >
< p class = "section-sub" style = "color:rgba(255,255,255,.6)" > When observed data conflicts with official sources by more than 2 standard deviations, the observation is automatically flagged for review rather than silently averaged away.< / p >
< div class = "contradiction-detail-card" style = "cursor:pointer;" onclick = "location.href='#contact'" role = "button" tabindex = "0" >
< div class = "cd-threshold" >
< div class = "cd-sigma" > > 2σ < / div >
< div class = "cd-desc" >
< h3 > Automatic contradiction flag threshold< / h3 >
< p > Any observation diverging from official sources by more than 2 standard deviations triggers a structured review process before inclusion in scores.< / p >
< / div >
< / div >
< div class = "cd-flags" >
< div class = "cd-flag" >
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< / svg >
< span > Flagged for human review< / span >
< / div >
< div class = "cd-flag" >
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< span > Source conflict logged< / span >
< / div >
< div class = "cd-flag" >
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< span > Contradiction report generated< / span >
< / div >
< div class = "cd-flag" >
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< span > Included in client delivery< / span >
< / div >
< / div >
< / div >
< / div >
< / section >
<!-- DATA RETENTION -->
< section id = "retention" style = "background:var(--surface)" >
< div class = "container" >
< div class = "section-label" > Data retention< / div >
< h2 class = "section-title" > Full provenance, every observation< / h2 >
< p class = "section-sub" > Landvex stores observations with a complete provenance chain. Nothing is anonymised away if it’ s needed for audit.< / p >
< div class = "retention-grid" >
< div class = "retention-card" style = "cursor:pointer;" onclick = "location.href='#contact'" role = "button" tabindex = "0" >
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< / svg >
< h3 > Contributor ID hash< / h3 >
< p > Each observation is linked to a hashed contributor identity. Allows quality tracking without exposing personal data in standard outputs.< / p >
< / div >
< div class = "retention-card" style = "cursor:pointer;" onclick = "location.href='#contact'" role = "button" tabindex = "0" >
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< / svg >
< h3 > Timestamp< / h3 >
< p > Precise capture timestamp stored with every observation. Temporal accuracy is critical for decay weighting and freshness scoring.< / p >
< / div >
< div class = "retention-card" style = "cursor:pointer;" onclick = "location.href='#contact'" role = "button" tabindex = "0" >
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< h3 > Device metadata< / h3 >
< p > Device model, OS version, and sensor data retained for quality auditing and anomaly detection. Used internally; not surfaced in standard reports.< / p >
< / div >
< div class = "retention-card" style = "cursor:pointer;" onclick = "location.href='#contact'" role = "button" tabindex = "0" >
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< / svg >
< h3 > GPS trace< / h3 >
< p > Full GPS trace stored per observation session. Enables movement-pattern validation and retroactive quality review if anomalies are detected.< / p >
< / div >
< / div >
< / div >
< / section >
<!-- ENTERPRISE -->
< section id = "enterprise" style = "background: var(--bg-dark)" >
< div class = "container" >
< div class = "section-label" > Enterprise< / div >
< h2 class = "section-title" > Deeper access for enterprise clients< / h2 >
< p class = "section-sub" > Standard Landvex output includes final scores and contradiction flags. Enterprise clients can go deeper.< / p >
< div class = "enterprise-box" >
< h3 > Full audit trail on request< / h3 >
< p > Enterprise clients can access raw confidence scores via API, review individual observation-level provenance, and receive structured audit trails for regulatory or investment committee use.< / p >
< div class = "enterprise-features" >
< div class = "ent-feature" >
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< span > Raw confidence scores via API< / span >
< / div >
< div class = "ent-feature" >
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< span > Full audit trail on request< / span >
< / div >
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< span > Observation-level provenance< / span >
< / div >
< div class = "ent-feature" >
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< / svg >
< span > Regulatory-grade documentation< / span >
< / div >
< / div >
< a class = "btn btn-lg" href = "#contact" > Discuss enterprise access → < / a >
< / div >
< / div >
< / section >
<!-- CONTACT -->
< section id = "contact" >
< div class = "container" >
< div class = "contact-wrapper" >
< div class = "section-label" > Get in touch< / div >
< h2 class = "section-title" > Questions about data quality?< / h2 >
< p class = "section-sub" > Whether you need technical documentation, API access, or audit trails — get in touch and we’ ll respond within one business day.< / p >
< form class = "contact-form" id = "contact-form" >
< div class = "form-group" >
< label for = "name" > Name< / label >
< input type = "text" id = "name" name = "name" placeholder = "Your name" required >
< / div >
< div class = "form-group" >
< label for = "email" > Email< / label >
< input type = "email" id = "email" name = "email" placeholder = "you@organisation.com" required >
< / div >
< div class = "form-group" >
< label for = "message" > Message< / label >
< textarea id = "message" name = "message" placeholder = "Describe what you need — API access, audit documentation, methodology detail..." > < / textarea >
< / div >
< button type = "submit" class = "btn btn-lg" style = "width:100%; justify-content: center;" >
Send message →
< / button >
< p class = "form-note" > We don’ t spam. Your information stays private.< / p >
< div id = "form-message" > < / div >
< / form >
< / div >
< / div >
< / section >
< footer >
< div class = "footer-inner" >
< div >
< div class = "footer-logo" > LandveX< / div >
< div class = "footer-copy" > © 2026 Landvex Inc · Landvex AB (org.nr 559141-7042)< / div >
< div class = "footer-tagline" > Decision intelligence for the physical world.< / div >
< / div >
< div class = "footer-links" >
< a href = "https://landvex.com/" > Home< / a >
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< a href = "mailto:contact@landvex.com" > contact@landvex.com< / a >
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< a href = "https://www.linkedin.com/company/landvex" target = "_blank" rel = "noopener" class = "footer-social" title = "LandveX on LinkedIn" >
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< / a >
< / div >
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< / footer >
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