4.7 out of 5 stars
(150 Ratings)
Streamline everything from proof of delivery and real-time driver tracking, through to route optimisation and customer updates. Save time, reduce operating costs, and boost productivity with Detrack.
Integrations
Integrate with the best accounting and SMS platforms. From order creation through to proof of delivery, everything works together seamlessly - orders sync automatically, customers get notified instantly and drivers receive jobs in real-time.
Test drive all features for free
It only takes a few minutes to add your drivers, upload or create jobs and start branding your customer comms. Our free trial gives you access to all features for 14 days. We know that there aren’t enough hours in the day and our experts are available 24/5 to help you get set up to achieve your goals faster. You can chat live or book a call back for practical, hands-on support.
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5.0 out of 5 stars
(54 Ratings)
Vehicle Acquisition Network (VAN) is private-party vehicle acquisition software for franchise and independent auto dealerships. It helps dealers buy used cars directly from consumers in their local market, alongside trade-ins and instead of paying up at auction.
VAN pulls for-sale-by-owner listings from 25+ online marketplaces into one dashboard, filters out dealer and scam listings and Do-Not-Call numbers, and adds VIN decoding and market valuation to each vehicle. Buyers call, text, and email sellers from inside VAN, and every conversation stays attached to the car it is about. On VAN Accelerate™, outreach and follow-up run automatically, KBB ICO leads get a text back within minutes, and AccuTrade leads get an automatic response.
For dealers without a buyer on staff, VAN Managed Buyer™ supplies a full-time buyer who is hired, trained, and managed by VAN. The dealership still makes the final buy decision and signs the check. Every plan includes an Acquisition Coach™, and VAN works with vAuto, VINCUE, CARFAX, AutoCheck, and most dealer CRMs.
VAN was founded in 2013 and serves dealers in the United States, Puerto Rico, and Canada. Plans start at $1,695 per month. To see VAN on your market, call 855-952-4949.
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Helm.ai
We provide licensing for AI software that spans the entire L2-L4 autonomous driving framework, which includes components like perception, intent modeling, path planning, and vehicle control. Our solutions achieve exceptional accuracy in perception and intent prediction, significantly enhancing the safety of autonomous driving systems. By leveraging unsupervised learning alongside mathematical modeling, we can harness vast datasets for improved performance, bypassing the limitations of supervised learning. These advancements lead to technologies that are remarkably more capital-efficient, resulting in a reduced development cost for our clients. Our offerings include Helm.ai's comprehensive scene vision-based semantic segmentation, integrated with Lidar SLAM outputs from Ouster. We facilitate L2+ autonomous driving capabilities with Helm.ai on highways 280, 92, and 101, which encompasses features such as lane-keeping and adaptive cruise control (ACC) lane changes. Additionally, Helm.ai excels in pedestrian segmentation, utilizing key-point prediction to enhance safety. This includes sophisticated pedestrian segmentation and accurate keypoint detection, even in challenging conditions like rain, where we address corner cases and integrate Lidar-vision fusion for optimal performance. Our full scene semantic segmentation also accounts for various road features, including botts dots and faded lane markings, ensuring reliability across diverse driving environments. Through continuous innovation, we aim to redefine the boundaries of what autonomous driving technology can achieve.
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NVIDIA Alpamayo 2 Super
NVIDIA Alpamayo 2 Super stands as a pioneering open model tailored for robotaxis and autonomous vehicles, designed to navigate rare and intricate driving scenarios while generating decisions that developers can analyze, verify, and rely upon. Utilizing the foundations of NVIDIA Cosmos 3 Super Reasoner and enhanced through reinforcement learning, it merges commercial accessibility with the ability to handle multiple tasks related to autonomous driving. The model comprehensively analyzes full-surround camera input, integrating perspectives from the front, sides, and rear to adeptly manage lane changes, merges, unprotected turns, and complex intersections. In addressing each driving scenario, it can produce a planned trajectory for the vehicle, a chain-of-causation that elucidates the decision-making process, a meta-action such as yielding or stopping, and reasoning auto-labels for both training and validation purposes, along with visual question-answering outputs anchored in specific image regions. These interconnected outputs facilitate the correlation between the model's observations and the actions it undertakes, thereby enhancing transparency in autonomous decision-making. Additionally, this functionality supports developers in refining and optimizing the model's performance in real-world applications.
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