Overview
xyzt.ai provides interactive spatial and temporal analytics for mobility, traffic, maritime, and IoT data. The platform is described as scalable, no-code, and privacy-safe for analyzing billions of data points. It is purpose-built for floating vehicle data analytics and supports raw GPS traces, map-matched trip paths, and aggregated traffic statistics.
In the news
- It was great to see both familiar and new names in yesterday’s webinar with Fintraffic. We explored 3 practical use cases around traffic anomalies, road maintenance and weather data, showing how multiple data sources can be combined in xyzt.ai to support a clearer operational overview. 👉 Check out our article https://bit.ly/4hAbDLW #VisualAnalytics #Mobility #RealTimeData #MobilityAnalytics
- 𝗢𝗻𝗲 𝗿𝗼𝗮𝗱 𝗻𝗲𝘁𝘄𝗼𝗿𝗸. 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗱𝗮𝘁𝗮 𝘀𝗼𝘂𝗿𝗰𝗲𝘀. 𝗧𝗵𝗿𝗲𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀. In our upcoming webinar with Fintraffic, we’ll move beyond theory and look at how road network data is being used in day-to-day operations. Fintraffic will show how its teams use xyzt.ai to: 🔹 spot unusual patterns across the road network 🔹 support road maintenance with data-driven analysis 🔹 combine road and satellite data to better understand weather impacts The common thread? Making complex mobility and
- 𝗬𝗼𝘂𝗿 𝗺𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗱𝗮𝘁𝗮 𝗺𝗮𝘆 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗰𝗼𝗻𝘁𝗮𝗶𝗻 𝘁𝗵𝗲 𝗮𝗻𝘀𝘄𝗲𝗿. Port authorities and government agencies already collect enormous volumes of AIS, GPS, sensor and historical movement data. The challenge isn't always getting more data. It's being able to 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 𝗵𝗶𝗱𝗱𝗲𝗻 𝗶𝗻𝘀𝗶𝗱𝗲 𝗶𝘁. When an analyst asks: → Which vessels repeatedly stopped here? → What other assets were nearby? → Has this pattern occurred before? The answer shouldn't require custom scripts or help from
- 𝗠𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗺𝗲𝗮𝗻 𝗯𝗲𝘁𝘁𝗲𝗿 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Road authorities increasingly have access to traffic, infrastructure, weather, satellite and other geospatial data. The challenge is bringing those sources together in a way that helps teams quickly understand 𝘄𝗵𝗮𝘁 𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴, 𝘄𝗵𝘆 𝗶𝘁 𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗲𝗲𝗱𝗲𝗱. In our upcoming webinar, Fintraffic will show how its teams are doing this in
- 𝗧𝗵𝗲 𝗮𝗻𝗼𝗺𝗮𝗹𝘆 𝗶𝘀 𝗲𝗮𝘀𝘆 𝘁𝗼 𝗱𝗲𝘁𝗲𝗰𝘁. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗶𝘀 𝗵𝗮𝗿𝗱𝗲𝗿. A vessel deviates from its usual route. Interesting. But is it suspicious? Maybe the weather changed. The port was congested. Other vessels did the same. Or perhaps this vessel follows this route regularly. An anomaly tells you 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁. Context tells you 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. For analysts, that means quickly moving from an alert to the bigger picture: → Has this happened before? → What
- A great first day for the team at SMM Hamburg ! Great to reconnect with customers and partners, meet new maritime prospects, and explore opportunities with data and technology partners from across the industry. Especially valuable were the conversations around real-world maritime use cases: maritime surveillance, vessel behavior and anomaly detection, port traffic analysis, historical movement analysis and situational awareness. These are exactly the challenges where xyzt.ai – The Movement Intelligence Layer helps turn massive
- 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝘁𝘂𝗿𝗻 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝘃𝗼𝗹𝘂𝗺𝗲𝘀 𝗼𝗳 𝗿𝗼𝗮𝗱 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝘁𝗲𝗮𝗺𝘀 𝗰𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗮𝗰𝘁 𝗼𝗻? On 17 September, we’re hosting a live webinar with Fintraffic to look at exactly that. Fintraffic will share three practical examples of how its teams use xyzt.ai to investigate road network conditions: 🔎 Detecting unusual patterns and anomalies 🛣️ Supporting road maintenance through data-driven analysis 🌦️ Combining road and
- In busy port environments, vessel movement is not just something to visualize. It is something to understand. Ports need to know how vessels move through restricted areas, where congestion builds up, how large vessels maneuver, and whether certain behavior is unusual or potentially risky. This is where port intelligence can add value. By analyzing vessel activity across space and time, teams can move beyond static maps and start answering operational questions: 🔹What happened before this event? 🔹Is this behavior normal for
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