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  <description>Research, field notes, and visual intelligence from Graylark, the team behind Raven.</description>
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  <copyright>© 2026 Graylark Technologies Inc.</copyright>
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    <title>Geolocating a Suspected Stolen Car From a Single Social Media Post</title>
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    <pubDate>Thu, 03 Sep 2026 09:00:00 GMT</pubDate>
    <category>Workflow</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>One public listing, one low-context photo, no metadata. How Raven takes an investigator from a suspicious vehicle post to a street-level hypothesis, a visual match, an organized case, and nearby camera leads.</description>
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    <title>A Research Lab in Service of Its Customers</title>
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    <pubDate>Thu, 03 Sep 2026 09:00:00 GMT</pubDate>
    <category>Company</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>Graylark combines the rigor of defense research, the speed of Silicon Valley prototyping, and product design that makes advanced AI usable in the field.</description>
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    <title>What Is Image Geolocation? How Investigators Locate Photos Without Metadata</title>
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    <pubDate>Mon, 31 Aug 2026 09:00:00 GMT</pubDate>
    <category>Guides</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>What image geolocation is, how it differs from GPS and reverse image search, where it breaks down, and how investigators turn a low-context image into a verified location.</description>
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    <title>Why We Built GeoSpy and Why It Became Raven</title>
    <link>https://graylark.net/articles/why-we-built-geospy</link>
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    <pubDate>Sat, 29 Aug 2026 09:00:00 GMT</pubDate>
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    <dc:creator>Daniel Heinen</dc:creator>
    <description>GeoSpy started as a two-week experiment in a one-bedroom Boston apartment. The investigators who found it showed us what it needed to become.</description>
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    <title>GeoSpy 101: AI Image Geolocation, Explained</title>
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    <pubDate>Sun, 22 Dec 2024 09:00:00 GMT</pubDate>
    <category>Guides</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>What GeoSpy is, how visual geolocation works, and how it evolved into Raven by Graylark.</description>
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    <title>Where Was This Photo Taken? How AI Detects Locations From Visual Clues</title>
    <link>https://graylark.net/articles/where-was-this-photo-taken</link>
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    <pubDate>Thu, 17 Sep 2026 09:00:00 GMT</pubDate>
    <category>Guides</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>Wondering where a photo was taken? Learn how modern AI photo location finders analyze architecture, roads, foliage, and lighting to pinpoint locations without metadata.</description>
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    <title>How to Find Photo Location Without Metadata (EXIF Data)</title>
    <link>https://graylark.net/articles/how-to-find-photo-location-without-exif-metadata</link>
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    <pubDate>Thu, 17 Sep 2026 09:00:00 GMT</pubDate>
    <category>Workflow</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>Learn how to locate where an image was taken even when EXIF GPS metadata is completely stripped. Step-by-step investigative methods using AI geolocation, visual clues, and reverse tools.</description>
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    <title>Top 5 Visual Clues That Reveal Location in Any Photo</title>
    <link>https://graylark.net/articles/top-5-visual-clues-that-reveal-photo-location</link>
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    <pubDate>Thu, 17 Sep 2026 09:00:00 GMT</pubDate>
    <category>Guides</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>Discover the top 5 visual indicators experts and AI models use to geolocate photos: architecture, road markings, flora, utility infrastructure, and sun shadows.</description>
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    <title>AI Geolocation vs. Reverse Image Search: Which Works Better?</title>
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    <pubDate>Thu, 17 Sep 2026 09:00:00 GMT</pubDate>
    <category>Guides</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>Reverse image search vs AI photo geolocation: learn the fundamental differences, why Google Lens fails on personal photos, and how AI finds locations from new images.</description>
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    <title>The Science Behind AI Photo Geolocation: Neural Networks &amp; Geographic Embeddings</title>
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    <pubDate>Thu, 17 Sep 2026 09:00:00 GMT</pubDate>
    <category>Workflow</category>
    <dc:creator>Daniel Heinen</dc:creator>
    <description>Explore the deep learning architectures, Vision Transformers, and hierarchical geocells that enable AI systems to estimate photo locations across Earth in milliseconds.</description>
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