Lexius, a Berkeley-based artificial intelligence startup building real-time shoplifting detection software for retail environments, has raised funding to expand its AI-powered loss prevention platform that transforms existing security cameras into intelligent monitoring systems.

The company develops computer vision technology that integrates directly with a retailer’s existing CCTV infrastructure, enabling real-time identification of suspicious behavior and potential theft without requiring additional hardware installations. Lexius positions its product as a plug-and-play AI layer for physical retail spaces designed to reduce shrinkage, improve response times, and provide automated alerts to store staff through mobile notifications.

Lexius was founded in 2022 and is part of the Y Combinator Winter 2026 cohort. Its core product focuses on analyzing live video feeds using machine learning models that detect patterns consistent with shoplifting activity. When potential incidents are identified, the system sends immediate alerts with contextual video clips, allowing employees to intervene before a suspect exits the store. The company also emphasizes privacy-focused design principles, stating that its models rely on behavioral signals rather than demographic attributes.

The startup has raised a total of $500,000 across multiple early-stage funding rounds, including a recent convertible note round completed in January 2026. The round was backed by Y Combinator, which also provides accelerator support and early-stage capital to the company as part of its Winter 2026 cohort participation.

Earlier funding activity includes participation from European early-stage ecosystem groups such as European Innovation Academy Porto, alongside a Switzerland-based investor group known as Virtual Network. These investors have contributed to Lexius’s development during its formative stages as it built and tested its AI-driven retail security platform.

Lexius has positioned itself within the rapidly growing AI video analytics and retail security market, competing with companies developing computer vision systems for theft prevention and in-store operational intelligence. Its approach focuses on leveraging existing infrastructure rather than replacing hardware, which it argues lowers adoption barriers for retailers and accelerates deployment across multiple store locations.

The company’s platform has already been piloted in select retail environments, where it is being used to monitor store activity, flag suspicious behavior, and generate structured incident reports for loss prevention teams. Early deployments highlight the system’s ability to process large volumes of video data in real time while reducing the manual burden of reviewing surveillance footage.

With new funding secured, Lexius plans to expand its engineering team, improve model accuracy under real-world retail conditions, and scale its pilot deployments across additional retail partners. The company is also working on enhancing its alerting system and refining its behavioral analytics models to reduce false positives and improve detection reliability.

Lexius’s leadership team believes that AI-driven surveillance systems will become a core layer of modern retail operations, particularly as retailers seek to reduce financial losses caused by shrinkage while maintaining efficient in-store operations. The company is positioning its platform as an infrastructure upgrade for existing security systems, aiming to turn passive cameras into active intelligence tools.

As it continues to develop its technology and expand its customer base, Lexius is seeking to establish itself in the emerging category of AI-powered physical security systems that combine computer vision, real-time analytics, and automated response capabilities.

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