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    The AI Platform For Visual Data

    Powering our autonomous future

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    Smart Ingestion + Smart Exploration for Visual Data

    Akridata provides an end-to-end suite of products that support both the smart ingestion and smart exploration of visual data to reduce cost and complexity while accelerating business value.

    Edge Data Platform

    Leading decentralized data management and tiering software optimized for Video, LIDAR, and domain-specific log and event data.Simplify AI data operations, ingest and organize streams of data, including from legacy stores, from edge to cloud, and with a decentralized catalog and global access.

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    Data Explorer

    First data-centric MLOps platform that helps data scientists explore, curate and use visual data at a large scale.
    Reduce hours of work to minutes. Cut time spent on data selection and curation to accelerate your path to model accuracy.

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    Akridata delivers value to Data Specialist.

    Data Scientists

    IT Professionals

    AI Experts

    “Most academic datasets are far from the industry's needs. In our case, we have rather small objects in fairly big images. This is probably a common problem, but in academia it is kind of a niche.”
    Helge Jacobsen,Senior Deep Learning Engineer at Veo
    “Advanced AI models are increasingly created and run on the cloud, but need good quality data from edge devices. The high volume and complex nature of this data has created a new exascale-class problem. Akridata is addressing the problem by managing smart pipelines for ingesting, filtering, curating, tracking, and staging of AI data.”
    Jon Jones,Director – Go-to-Market for AI/ML, EC2, and Autonomous Vehicles, AWS
    “Data-Centric AI solutions such as Akridata’s Edge Data Platform fill that need and help customers to match the sophistication of their analytics and models with high-quality and relevant data.”
    Kurt Niebuhr,Principal Program Manager, HPC/AI Ecosystem and Workload Incubation, at Microsoft Azure
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    2X Productivity

    • Access the right data in minutes or hours vs. hours or days
    • Free up time for critical tasks
    • Efficiently handle growing data volumes at scale

    4X Efficiency

    • Train and iterate AI models faster
    • Improved accuracy, improved safety
    • Reduce manual sorting and labeling

    4X Cost Reduction

    • Avoid moving large amounts of data to the cloud
    • Reduce spending on storage/compute
    • Decrease bandwidth and latency

    10X Faster Access

    • Real-time edge data access
    • Access at scale, anytime, anywhere
    • Frictionless deployment with existing infrastructure

    Akridata delivers value to Data Specialists

    “Most academic datasets are far from the industry's needs. In our case, we have rather small objects in fairly big images. This is probably a common problem, but in academia it is kind of a niche.”
    Helge Jacobsen,Senior Deep Learning Engineer at Veo
    “Advanced AI models are increasingly created and run on the cloud, but need good quality data from edge devices. The high volume and complex nature of this data has created a new exascale-class problem. Akridata is addressing the problem by managing smart pipelines for ingesting, filtering, curating, tracking, and staging of AI data.”
    Jon Jones,Director – Go-to-Market for AI/ML, EC2, and Autonomous Vehicles, AWS
    “Data-Centric AI solutions such as Akridata’s Edge Data Platform fill that need and help customers to match the sophistication of their analytics and models with high-quality and relevant data.”
    Kurt Niebuhr,Principal Program Manager, HPC/AI Ecosystem and Workload Incubation, at Microsoft Azure
    “One of the biggest challenges for data scientists focused on visual data is the ability to visualize three-dimensional visual data in a two-dimensional format and gain in-depth understanding from them.”
    Bikram Baruah,AI Technical Lead, Advanced Manufacturing Research Centre of The University of Sheffield
    “Today we have models that are extremely good at mimicking the data that you train them to mimic, but they can only become as good as that data, not better. The data can always get a little better and cover more corner-cases while models can at best be perfect within the data distribution that they know about.”
    Helge Jacobsen,Senior Deep Learning Engineer at Veo
    Previous
    Next

    Data Scientists

    IT Professionals

    AI Experts

    2X Productivity

    • Access the right data in minutes or hours vs. hours or days
    • Free up time for critical tasks
    • Efficiently handle growing data volumes at scale

    4X Efficiency

    • Train and iterate AI models faster
    • Improved accuracy, improved safety
    • Reduce manual sorting and labeling

    4X Cost Reduction

    • Avoid moving large amounts of data to the cloud
    • Reduce spending on storage/compute
    • Decrease bandwidth and latency

    10X Faster Access

    • Real-time edge data access
    • Access at scale, anytime, anywhere
    • Frictionless deployment with existing infrastructure

    Trusted By Leaders in Technology

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