Akridata

Akridata, Inc. operates as a software company focused on managing and processing large-scale visual data for artificial intelligence applications. Founded in 2018 by Kumar Ganapathy, Vijay Karamcheti, and Sanjay Pichaiah, the company aims to solve the challenges associated with the massive volumes of data generated by sensors at the edge, such as cameras and LiDAR. The founders are serial entrepreneurs; Ganapathy previously founded VxTel, acquired by Intel, and Virident, acquired by HGST, while Karamcheti also co-founded Virident. This experience in data-intensive enterprises informs Akridata's approach to handling exascale-class data problems for AI.

The company's core business revolves around its Data-Centric AI platform, which provides an end-to-end suite of products for the smart ingestion and exploration of visual data. This platform is designed to help data scientists and machine learning engineers in industries like automotive, manufacturing, retail, and healthcare to manage the data lifecycle more efficiently. Clients include major enterprises such as Toyota Motor North America. Akridata's business model is based on providing its software solutions, which are available on cloud marketplaces like Microsoft Azure and are built on services such as Amazon Web Services. The company has secured a total of $20 million in funding over two rounds, including a $15 million Series A in October 2021, led by TeleSoft Partners, Accel, and MFV Partners.

Akridata offers a suite of products including the Akridata Edge Data Platform, Data Explorer, and a set of visual inspection tools named Vision Assist, Vision Command, and Vision Copilot. The Edge Data Platform is a decentralized software layer that creates smart data pipelines from the edge to the cloud, allowing for automated workflows and faster access to relevant data. The Data Explorer platform enables data science teams to search, analyze, and curate large, unlabeled visual datasets without manual labeling, reducing data preparation time. The visual inspection suite leverages AI and deep learning to automate quality control in manufacturing, detecting defects in real-time and reducing false positives for sectors such as medical devices and electronics. These products collectively aim to reduce infrastructure costs and improve the productivity of data science teams by allowing them to focus on the small fraction of data that holds the most value.

Keywords: data-centric AI, visual data management, edge computing, AI data pipelines, MLOps, deep learning, computer vision, data curation, automated visual inspection, unstructured data, data exploration, AI model training, autonomous vehicles, manufacturing AI, smart ingestion, exascale data, data labeling, defect detection, quality control solutions