{"id":1462,"date":"2026-02-16T14:35:15","date_gmt":"2026-02-16T14:35:15","guid":{"rendered":"https:\/\/ravam.co\/?page_id=1462"},"modified":"2026-02-16T15:16:52","modified_gmt":"2026-02-16T15:16:52","slug":"data-analytics","status":"publish","type":"page","link":"https:\/\/ravam.co\/sr\/data-analytics\/","title":{"rendered":"Data Analytics"},"content":{"rendered":"\n<div class=\"wp-block-group alignfull is-style-default is-layout-flow wp-container-core-group-is-layout-e603688c wp-block-group-is-layout-flow\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">\n<!--\n  RAVAM DATA & ANALYTICS \u2014 \/data-analytics\/\n-->\n<meta name=\"title\" content=\"Data &#038; Analytics | RAVAM \u2014 From Drone Scanning to Predictive Intelligence\">\n<meta name=\"description\" content=\"RAVAM's three-tier data pipeline transforms raw drone sensor data into actionable infrastructure intelligence. Multi-sensor scanning, geophysical processing, and AI-powered predictive analytics.\">\n<meta name=\"keywords\" content=\"drone data analytics, geophysical survey data, infrastructure monitoring data, predictive maintenance, AI analytics, sensor fusion, pipeline inspection data, thermal analysis, LiDAR processing, digital twin\">\n<meta name=\"robots\" content=\"index, follow, max-image-preview:large, max-snippet:-1, max-video-preview:-1\">\n<meta name=\"theme-color\" content=\"#0a0e1a\">\n<link rel=\"canonical\" href=\"https:\/\/ravam.co\/data-analytics\/\">\n<meta property=\"og:type\" content=\"website\"><meta property=\"og:url\" content=\"https:\/\/ravam.co\/data-analytics\/\">\n<meta property=\"og:title\" content=\"Data &#038; Analytics | RAVAM\"><meta property=\"og:description\" content=\"Three-tier data pipeline: multi-sensor scanning, geophysical processing, AI predictive intelligence.\">\n<meta property=\"og:image\" content=\"https:\/\/ravam.co\/wp-content\/uploads\/ravam-data-analytics-og.jpg\">\n<meta property=\"og:site_name\" content=\"RAVAM\"><meta property=\"og:locale\" content=\"en_US\">\n<link rel=\"alternate\" hreflang=\"en\" href=\"https:\/\/ravam.co\/data-analytics\/\">\n<link rel=\"alternate\" hreflang=\"sr\" href=\"https:\/\/ravam.co\/sr\/data-analytics\/\">\n<link rel=\"alternate\" hreflang=\"x-default\" href=\"https:\/\/ravam.co\/data-analytics\/\">\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"WebPage\",\"name\":\"RAVAM Data & Analytics\",\"url\":\"https:\/\/ravam.co\/data-analytics\/\",\"breadcrumb\":{\"@type\":\"BreadcrumbList\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/ravam.co\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Data & Analytics\",\"item\":\"https:\/\/ravam.co\/data-analytics\/\"}]}}<\/script>\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What data does RAVAM collect?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"RAVAM drones collect 12+ data types: surface (RGB, thermal, LiDAR, multispectral) and subsurface (magnetometry, GPR, electromagnetic, IP). All RTK-geotagged to centimeter accuracy.\"}},{\"@type\":\"Question\",\"name\":\"What analytics does RAVAM provide?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"AI-powered anomaly detection, defect classification, predictive failure modeling, change-over-time analysis, and automated reporting via dashboards, GIS exports, PDF reports, and API.\"}},{\"@type\":\"Question\",\"name\":\"What output formats are available?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"GeoTIFF, 3D point clouds (LAS\/LAZ), DEM, shapefiles, GeoJSON, KML, PDF reports, CSV\/Excel, real-time dashboards, and REST API. Compatible with ArcGIS and QGIS.\"}},{\"@type\":\"Question\",\"name\":\"How does the AI detect defects?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Multi-sensor fusion cross-references data from different sensor types to eliminate false positives. ML models achieve 95%+ accuracy with predictive forecasting based on historical trends.\"}}]}<\/script>\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"Organization\",\"name\":\"RAVAM\",\"url\":\"https:\/\/ravam.co\",\"logo\":\"https:\/\/ravam.co\/wp-content\/uploads\/2026\/01\/R-LOGO.png\"}<\/script>\n\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=Outfit:wght@300;400;500;600;700;800&#038;display=swap\" rel=\"stylesheet\">\n<style>\n*,:after,:before{box-sizing:border-box}\n.rd{font-family:'Outfit',sans-serif;background:#0a0e1a;color:#f0f0f5;-webkit-font-smoothing:antialiased;line-height:1.7}\n.rd img{max-width:100%;display:block}.rd a{text-decoration:none;color:inherit}\n.rd .ctn{max-width:1140px;margin:0 auto;padding:0 2rem}\n.rd .sec{background:#0a0e1a;padding:4rem 0}\n.rd .sec-alt{background:#070b16;padding:4rem 0}\n.rd .grad{height:2px;background:linear-gradient(90deg,#e63946,#f59e0b,#10b981,#3b82f6);opacity:.5}\n.rd 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.fmts{grid-template-columns:1fr 1fr}.rd .tg{grid-template-columns:1fr}}\n<\/style>\n\n<div class=\"rd\">\n\n<!-- HERO -->\n<div class=\"hero\">\n<div class=\"ctn\">\n<span class=\"hero-tag rv\">Data &amp; Analytics<\/span>\n<h1 class=\"rv d1\">From Raw Sensor Data to Predictive Intelligence<\/h1>\n<p class=\"lead rv d2\">Our drones collect. Our platform processes. Our AI predicts. Every flight generates a structured data pipeline that transforms multi-sensor readings into decisions that prevent failures and extend asset life.<\/p>\n<\/div>\n<\/div>\n\n<div class=\"grad\"><\/div>\n\n<!-- PIPELINE OVERVIEW -->\n<div class=\"sec\">\n<div class=\"ctn\">\n<span class=\"tag rv\">The Data Pipeline<\/span>\n<h2 class=\"st rv d1\">Three Tiers of Intelligence<\/h2>\n<p class=\"sd rv d2\">Every autonomous mission flows through a three-stage pipeline &mdash; from raw collection to processed analysis to predictive action.<\/p>\n<div class=\"pipe rv d3\">\n<div class=\"ps\"><div class=\"pn\">Tier 1<\/div><div class=\"pi\">&#x1F4F7;<\/div><h3>Scanning &amp; Collection<\/h3><p>Multi-sensor raw data captured by autonomous drone flights. Surface and subsurface, geotagged to centimeter accuracy.<\/p><\/div>\n<div class=\"pa\">&rarr;<\/div>\n<div class=\"ps\"><div class=\"pn\">Tier 2<\/div><div class=\"pi\">&#x1F30D;<\/div><h3>Geophysical Processing<\/h3><p>Raw data transformed into geological models, orthomosaics, 3D reconstructions, and subsurface maps by domain experts.<\/p><\/div>\n<div class=\"pa\">&rarr;<\/div>\n<div class=\"ps\"><div class=\"pn\">Tier 3<\/div><div class=\"pi\">&#x1F9E0;<\/div><h3>AI Analytics &amp; Prediction<\/h3><p>Machine learning detects anomalies, classifies defects, predicts failures weeks in advance, and generates automated reports.<\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"div\">&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;<\/div>\n\n<!-- TIER 1: SCANNING -->\n<div class=\"sec-alt\">\n<div class=\"ctn\">\n<div class=\"tc rv\">\n<div>\n<span class=\"tag\">Tier 1<\/span>\n<h2 class=\"st\">Multi-Sensor Scanning &amp; Collection<\/h2>\n<p class=\"sd\" style=\"max-width:none\">Every autonomous mission captures structured data across 12+ sensor types. Two data categories &mdash; surface and subsurface &mdash; collected simultaneously, all RTK-geotagged to centimeter accuracy.<\/p>\n<h4 style=\"font-size:.82rem;font-weight:700;color:#93c5fd;margin-bottom:.6rem\">&#x25CF; Surface &amp; Terrestrial Data<\/h4>\n<div class=\"dtt\">\n<span class=\"dt s\">RGB Photography<\/span><span class=\"dt s\">Thermal Imaging<\/span><span class=\"dt s\">LiDAR Point Clouds<\/span><span class=\"dt s\">Multispectral<\/span><span class=\"dt s\">Hyperspectral<\/span><span class=\"dt s\">Orthomosaics<\/span><span class=\"dt s\">Night Vision<\/span><span class=\"dt s\">4K Video<\/span>\n<\/div>\n<h4 style=\"font-size:.82rem;font-weight:700;color:#fcd34d;margin:1rem 0 .6rem\">&#x25CF; Subsurface &amp; Geophysical Data<\/h4>\n<div class=\"dtt\">\n<span class=\"dt u\">Magnetometry<\/span><span class=\"dt u\">Ground Penetrating Radar<\/span><span class=\"dt u\">Electromagnetic (TEM\/EM31)<\/span><span class=\"dt u\">Magnetic Gradiometry<\/span><span class=\"dt u\">Induced Polarization<\/span><span class=\"dt u\">Resistivity<\/span><span class=\"dt u\">Gravity Surveys<\/span>\n<\/div>\n<\/div>\n<div class=\"ci\"><div class=\"ph\"><div class=\"phl\"><img decoding=\"async\" src=\"https:\/\/ravam.co\/wp-content\/uploads\/6a.jpg?w=800&#038;q=80\" alt=\"Drone sensor array in flight or multi-sensor payload closeup\"><\/div><\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"div\">&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;<\/div>\n\n<!-- TIER 2: GEOPHYSICAL -->\n<div class=\"sec\">\n<div class=\"ctn\">\n<div class=\"tc rev rv\">\n<div>\n<span class=\"tag\">Tier 2<\/span>\n<h2 class=\"st\">Geophysical Processing &amp; Modeling<\/h2>\n<p class=\"sd\" style=\"max-width:none\">Raw sensor data is processed by our team of 4 geophysics PhDs into calibrated, interpretable outputs. From magnetic anomaly maps to 3D subsurface models, this tier turns signal into structure.<\/p>\n<div class=\"tg\">\n<div class=\"tc2\"><h4>&#x1F9F2; Magnetic Anomaly Analysis<\/h4><p>Pipeline integrity diagnostics, corrosion mapping, joint\/weld identification. Ferrosonde and proton magnetometer data processed into defect classifications.<\/p><\/div>\n<div class=\"tc2\"><h4>&#x1F4D0; 3D Subsurface Modeling<\/h4><p>Multi-sensor inversion combining GPR, EM, and magnetometry into volumetric geological models. Mineral deposit identification and underground infrastructure mapping.<\/p><\/div>\n<div class=\"tc2\"><h4>&#x1F5FA; Surface Reconstruction<\/h4><p>LiDAR point clouds and photogrammetry processed into orthomosaics, DEMs, and 3D terrain reconstructions via <a href=\"https:\/\/ravam.co\/mapper\/\" style=\"color:#e63946\">RAVAM Mapper<\/a>.<\/p><\/div>\n<div class=\"tc2\"><h4>&#x1F321; Thermal &amp; Spectral Analysis<\/h4><p>Thermal gradient mapping for leak detection, solar panel hotspot identification, and multispectral vegetation health indices.<\/p><\/div>\n<\/div>\n<\/div>\n<div class=\"ci\"><div class=\"ph\"><div class=\"phl\"><img decoding=\"async\" src=\"https:\/\/ravam.co\/wp-content\/uploads\/6b.jpg?w=800&#038;q=80\" alt=\"Geophysical data output magnetic anomaly map, 3D subsurface model, or processed survey\"><\/div><\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"div\">&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;<\/div>\n\n<!-- TIER 3: AI ANALYTICS -->\n<div class=\"sec-alt\">\n<div class=\"ctn\">\n<div class=\"tc rv\">\n<div>\n<span class=\"tag\">Tier 3<\/span>\n<h2 class=\"st\">AI Analytics &amp; Predictive Intelligence<\/h2>\n<p class=\"sd\" style=\"max-width:none\">The RAVAM AI engine fuses multi-sensor data streams, detects anomalies in real-time, and models degradation trends to predict failures weeks before they occur.<\/p>\n<div class=\"tg\">\n<div class=\"tc2\"><h4>&#x26A0; Anomaly Detection<\/h4><p>Real-time identification of defects, hot spots, leaks, structural deformations, and vegetation encroachment. Multi-sensor cross-referencing eliminates false positives.<\/p><\/div>\n<div class=\"tc2\"><h4>&#x1F4C8; Predictive Maintenance<\/h4><p>Historical trend analysis models degradation timelines. Forecasts when components will fail and recommends intervention windows &mdash; weeks in advance.<\/p><\/div>\n<div class=\"tc2\"><h4>&#x1F504; Change-Over-Time Analysis<\/h4><p>Baseline comparison across repeated survey flights. Detects progression of corrosion, subsidence, vegetation growth, and thermal drift.<\/p><\/div>\n<div class=\"tc2\"><h4>&#x1F4CB; Automated Reporting<\/h4><p>AI-generated inspection reports with annotated imagery, severity classifications, GPS coordinates, and recommended actions. PDF, dashboard, or API.<\/p><\/div>\n<\/div>\n<\/div>\n<div class=\"ci\"><div class=\"ph\"><div class=\"phl\"><img decoding=\"async\" src=\"https:\/\/ravam.co\/wp-content\/uploads\/page04c-2-1.jpg?w=800&#038;q=80\" alt=\"AI analytics dashboard, anomaly detection heatmap, or automated inspection report\"><\/div><\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"div\">&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;<\/div>\n\n<!-- OUTPUT FORMATS -->\n<div class=\"sec\">\n<div class=\"ctn\">\n<span class=\"tag rv\">Deliverables<\/span>\n<h2 class=\"st rv d1\">What You Receive<\/h2>\n<p class=\"sd rv d2\">Structured, standards-compliant outputs that integrate directly with your existing GIS, SCADA, and enterprise systems.<\/p>\n<div class=\"fmts rv d3\">\n<div class=\"fmt\"><div class=\"fmi\">&#x1F5FA;<\/div><div><div class=\"fmn\">Orthomosaics &amp; Maps<\/div><div class=\"fmd\">GeoTIFF, high-res stitched imagery<\/div><\/div><\/div>\n<div class=\"fmt\"><div class=\"fmi\">&#x1F4CD;<\/div><div><div class=\"fmn\">3D Point Clouds<\/div><div class=\"fmd\">LAS \/ LAZ format, colorized<\/div><\/div><\/div>\n<div class=\"fmt\"><div class=\"fmi\">&#x1F3D4;<\/div><div><div class=\"fmn\">Digital Elevation Models<\/div><div class=\"fmd\">DEM \/ DSM rasters<\/div><\/div><\/div>\n<div class=\"fmt\"><div class=\"fmi\">&#x1F4CA;<\/div><div><div class=\"fmn\">Interactive Dashboards<\/div><div class=\"fmd\">Real-time via Command Center<\/div><\/div><\/div>\n<div class=\"fmt\"><div class=\"fmi\">&#x1F4C4;<\/div><div><div class=\"fmn\">PDF Reports<\/div><div class=\"fmd\">AI-generated with annotations<\/div><\/div><\/div>\n<div class=\"fmt\"><div class=\"fmi\">&#x1F4C1;<\/div><div><div class=\"fmn\">GIS Exports<\/div><div class=\"fmd\">Shapefile, GeoJSON, KML<\/div><\/div><\/div>\n<div class=\"fmt\"><div class=\"fmi\">&#x1F4C2;<\/div><div><div class=\"fmn\">Tabular Data<\/div><div class=\"fmd\">CSV \/ Excel with coordinates<\/div><\/div><\/div>\n<div class=\"fmt\"><div class=\"fmi\">&#x1F517;<\/div><div><div class=\"fmn\">API Access<\/div><div class=\"fmd\">REST API for system integration<\/div><\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"div\">&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;<\/div>\n\n<!-- CASE STUDY: PIPELINE -->\n<div class=\"sec-alt\">\n<div class=\"ctn\">\n<span class=\"tag rv\">Proven in the Field<\/span>\n<h2 class=\"st rv d1\">Pipeline Integrity &mdash; From Scan to Decision<\/h2>\n<p class=\"sd rv d2\">Our most advanced data pipeline in action: UAV-based aeromagnetic diagnostics for underground pipeline corrosion detection.<\/p>\n<div class=\"tc rv d3\">\n<div>\n<div class=\"tg\" style=\"grid-template-columns:1fr\">\n<div class=\"tc2\"><h4>1&#xFE0F;&#x20E3; Scan<\/h4><p>Heavy-lift UAV with ferrosonde three-component magnetometer flies low-altitude survey lines along the pipeline corridor.<\/p><\/div>\n<div class=\"tc2\"><h4>2&#xFE0F;&#x20E3; Process<\/h4><p>Magnetic field data (X, Y, Z, F components) processed to identify pipe joints, welds, and anomalous zones at up to 7,000 nT resolution.<\/p><\/div>\n<div class=\"tc2\"><h4>3&#xFE0F;&#x20E3; Analyze<\/h4><p>AI compares before\/after magnetic signatures to detect corrosion, metal loss, and crack formation. Sensitivity confirmed to &gt;1,000 nT shifts.<\/p><\/div>\n<div class=\"tc2\"><h4>4&#xFE0F;&#x20E3; Deliver<\/h4><p>Geo-referenced defect map with severity classification, inspection priorities, and 6-month re-survey schedule. GIS layers + PDF report.<\/p><\/div>\n<\/div> \n<\/div>\n<div class=\"ci\"><div class=\"ph\"><div class=\"phl\"><img decoding=\"async\" src=\"https:\/\/ravam.co\/wp-content\/uploads\/2026\/02\/tech-proof.jpg?w=800&#038;q=80\" alt=\"Pipeline scan magnetic anomaly chart and defect map\"><\/div><\/div><\/div>\n<\/div>\n<div style=\"text-align:center;margin-top:2rem\" class=\"rv d4\">\n<a class=\"bo\" href=\"https:\/\/ravam.co\/pipeline-scan\/\">Read Full Pipeline Scan Case Study &rarr;<\/a>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"div\">&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;<\/div>\n\n<!-- PLATFORM INTEGRATION -->\n<div class=\"sec\">\n<div class=\"ctn\">\n<span class=\"tag rv\">Platform Integration<\/span>\n<h2 class=\"st rv d1\">Where the Data Lives<\/h2>\n<p class=\"sd rv d2\">All data flows into RAVAM&rsquo;s cloud ecosystem. Collect, process, visualize, and act &mdash; from a single pane of glass.<\/p>\n<div class=\"tg rv d3\" style=\"grid-template-columns:repeat(3,1fr)\">\n<div class=\"tc2\" style=\"text-align:center;padding:2rem\"><div style=\"font-size:2rem;margin-bottom:.8rem\">&#x1F3AE;<\/div><h4 style=\"justify-content:center\"><a href=\"https:\/\/ravam.co\/comman-center\/\" style=\"color:#e63946\">Command Center<\/a><\/h4><p>Mission control, live video, fleet management. Real-time data streams.<\/p><\/div>\n<div class=\"tc2\" style=\"text-align:center;padding:2rem\"><div style=\"font-size:2rem;margin-bottom:.8rem\">&#x1F5FA;<\/div><h4 style=\"justify-content:center\"><a href=\"https:\/\/ravam.co\/mapper\/\" style=\"color:#e63946\">Mapper<\/a><\/h4><p>Photogrammetry engine. Orthomosaics, 3D models, point clouds, DEMs.<\/p><\/div>\n<div class=\"tc2\" style=\"text-align:center;padding:2rem\"><div style=\"font-size:2rem;margin-bottom:.8rem\">&#x1F9E0;<\/div><h4 style=\"justify-content:center\">AI Engine<\/h4><p>Anomaly detection, predictive modeling, automated reporting.<\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n<div class=\"div\">&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;&#x2500;<\/div>\n\n<!-- FAQ -->\n<div class=\"sec-alt\">\n<div class=\"ctn\">\n<span class=\"tag rv\">Common Questions<\/span>\n<h2 class=\"st rv d1\">Data &amp; Analytics FAQ<\/h2>\n<div class=\"fi rv d2\"><button class=\"fq\" onclick=\"this.parentElement.classList.toggle('open')\"><span>What data does RAVAM collect per flight?<\/span><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" width=\"16\" height=\"16\"><line x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\/><line x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\/><\/svg><\/button><div class=\"fa\"><div class=\"fin\">Each mission captures 2&ndash;4 sensor types. Surface: RGB, thermal, LiDAR, multispectral. Subsurface: magnetometry, GPR, electromagnetic. All RTK-geotagged to centimeter accuracy, stored onboard (512 GB) and uploaded via the nest station.<\/div><\/div><\/div>\n<div class=\"fi rv d3\"><button class=\"fq\" onclick=\"this.parentElement.classList.toggle('open')\"><span>How accurate is the AI anomaly detection?<\/span><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" width=\"16\" height=\"16\"><line x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\/><line x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\/><\/svg><\/button><div class=\"fa\"><div class=\"fin\">95%+ accuracy through multi-sensor fusion. A thermal anomaly confirmed by a magnetic signature and visual defect is far more reliable than any single sensor. False positive rates drop with each additional data layer.<\/div><\/div><\/div>\n<div class=\"fi rv d3\"><button class=\"fq\" onclick=\"this.parentElement.classList.toggle('open')\"><span>Can I integrate RAVAM data with my existing GIS?<\/span><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" width=\"16\" height=\"16\"><line x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\/><line x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\/><\/svg><\/button><div class=\"fa\"><div class=\"fin\">Yes. Standard GIS formats: Shapefile, GeoJSON, KML, GeoTIFF. ArcGIS and QGIS compatible. For SCADA\/EAM\/ERP, we provide REST API access and custom integration.<\/div><\/div><\/div>\n<div class=\"fi rv d4\"><button class=\"fq\" onclick=\"this.parentElement.classList.toggle('open')\"><span>How far in advance can RAVAM predict failures?<\/span><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" width=\"16\" height=\"16\"><line x1=\"12\" y1=\"5\" x2=\"12\" y2=\"19\"\/><line x1=\"5\" y1=\"12\" x2=\"19\" y2=\"12\"\/><\/svg><\/button><div class=\"fa\"><div class=\"fin\">Weeks to months ahead, depending on asset type and degradation rate. For pipelines, magnetic signature progression across 6-month survey cycles builds robust degradation curves for forecasting.<\/div><\/div><\/div>\n<\/div>\n<\/div>\n\n<!-- CTA -->\n<div class=\"cta\">\n<div class=\"ctn\">\n<h2>See Your Infrastructure Data Differently<\/h2>\n<p>Schedule a demo to see real data outputs, or discuss how RAVAM&rsquo;s data pipeline fits your operations.<\/p>\n<div class=\"cb\">\n<a class=\"bp\" href=\"https:\/\/ravam.co\/contact\">Request a Data Demo<\/a>\n<a class=\"bo\" href=\"https:\/\/ravam.co\/pipeline-scan\/\">View Pipeline Case Study<\/a>\n<\/div>\n<\/div>\n<\/div>\n\n<\/div>\n\n<script>\ndocument.addEventListener('DOMContentLoaded',function(){\nvar o=new IntersectionObserver(function(e){e.forEach(function(n){if(n.isIntersecting)n.target.classList.add('visible')});},{threshold:.06});\ndocument.querySelectorAll('.rd .rv').forEach(function(el){o.observe(el)});\n});\n<\/script>\n\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Data &amp; Analytics From Raw Sensor Data to Predictive Intelligence Our drones collect. Our platform processes. Our AI predicts. Every flight generates a structured data pipeline that transforms multi-sensor readings into decisions that prevent failures and extend asset life. The Data Pipeline Three Tiers of Intelligence Every autonomous mission flows through a three-stage pipeline &mdash; [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"no-title-sticky-header","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-1462","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/pages\/1462","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/comments?post=1462"}],"version-history":[{"count":11,"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/pages\/1462\/revisions"}],"predecessor-version":[{"id":1475,"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/pages\/1462\/revisions\/1475"}],"wp:attachment":[{"href":"https:\/\/ravam.co\/sr\/wp-json\/wp\/v2\/media?parent=1462"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}