By AIM · AIM Media House
Large AI models can summarize a TikTok clip or generate a short film script, but video context remains a blind spot. Most models struggle beyond an hour or two of footage, a limitation that makes AI brittle in sectors like security and marketing where context is often built over longer periods.
That’s the problem Memories.ai, a San Francisco-based startup, is trying to solve.
Founded by two former Meta Reality Labs researchers, Shawn Shen and Ben Zhou , the company recently raised an $8 million seed round led by Susa Ventures, with participation from Samsung Next, Crane Venture Partners, Fusion Fund, Seedcamp, and Creator Ventures.
The round, originally targeted at $4 million, was oversubscribed: an indication of the perceived demand for AI systems that have high recall. A Memory Layer for Machines Memories.ai’s pitch is centered on infrastructure.
Specifically, the team is building what it calls a Large Visual Memory Model (LVMM), a system designed to enable machines to “see, understand, and recall” visual information persistently. The platform indexes video data across long timeframes and makes it searchable through natural language queries.
Its use cases are broad, but current traction is concentrated in two areas: surveillance and marketing. Security firms use the technology to query months of footage for specific actions or objects. Marketing teams apply it to track brand visibility and sentiment across social video platforms.
The model powers both a web-based chatbot interface and an API, letting developers integrate long-term video memory into applications like robotic agents and AR interfaces. Traditional video analysis tools load an entire clip into memory.
Memories.ai looks to apply a multi-layered process that compresses footage, strips away irrelevant data, indexes useful frames, and aggregates insights.
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