Meet Vintra: The Deep-Learning Startup Leveraging Purpose-Built AI Algorithms on their Mission to Improve Safety and Security
San Jose, CA – March 11, 2019 – 7:00 AM PT / 10:00 AM ET – On a mission to create safer communities, companies, and environments, Vintra uses artificial intelligence to augment the established surveillance systems deployed to protect the spaces where people work, learn, and travel. Vintra works with private companies, higher education, and law enforcement to transform visual data from any camera – including, remarkably, mobile sources like drones and dash cams – into actionable, tailored and trusted intelligence with proprietary deep learning algorithms. Backed by investors including Bonfire Ventures, Vertex Ventures and London Venture Partners, Vintra has raised $4.8 million to date.
Founded in 2016 by entrepreneur Brent Boekestein and leading computer vision experts, Dr. Ariel Amato and Dr. Angel Sappa, Vintra saw a critical need developing with the rise of security systems and proliferation of cameras, including mobile surveillance cameras that are burgeoning in use. As the use of surveillance and security cameras grows, the amount of data collected per year requires billions of hours of manual interpretation if done by human operators. With Vintra’s unique platform, this overwhelming amount of video data can be organized, analyzed and monitored through a purpose-built deep learning system. Vintra’s team of deep learning experts build their own algorithms from the ground up and GPU-optimize their models to take advantage of the latest hardware and acceleration techniques. These models can be deployed now to augment the majority of safety and security scenarios, with future plans to empower customers to create their own analytics in order to be responsive to the specific needs of each installation environment.
Vintra has created FulcrumAI, an AI-powered video analytics solution capable of continued evolution and improvement over time that works on video data from any source that can be leveraged in two ways: as a tool to more accurately monitor live video feeds and produce actionable alerts, and as a tool to analyze footage in post-event investigative scenarios. The flexible system can be deployed in two ways to provide different benefits depending on the needs of the installation environment:
- FulcrumAI can be used “on-premises” at an organization’s location, to augment human resources by transforming overwhelming numbers of live video feeds into preventative alerts, timely situational awareness, and instantly searchable video forensic data.
- FulcrumAI can be used in the cloud, as a powerful post-event investigation solution that enables fast, accurate, and advanced video forensics at industry leading speeds.
Today, Vintra structures previously unstructured video from live cameras and recorded clips to make that data monitorable and searchable with industry leading accuracy, speed, and cost in a way previously unobtainable by human operators. Additionally, Vintra strives to ensure their algorithms are ethically-developed in regard to data bias via continuous internal review to identify and correct issues prior to production. Vintra is committed to building solutions capable of reducing the unintended bias that can affect AI-powered security systems, and to creating safe spaces for everyone while respecting individual privacy concerns.
“Every year, billions of dollars in time and resources are spent on security personnel to monitor live streams as well as post-event investigations by law enforcement and analysts. Vintra exists because we knew we could use computer vision and deep learning to build a new way forward for video analytics that took mobility and customization into account,” said Brent Boekestein, CEO of Vintra. “One of the most important parts about building AI solutions is ensuring that customers, the public, and communities can trust the solutions being deployed. To that end, Vintra makes it easy for customers to test its solutions and review performance data on publicly available datasets.”
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