Token Compression
Memory-augmented reinforcement learning for token compression — keeping video understanding efficient as context stretches across millions of hours.
There is more in every frame
than the eye can ever hold
A single persistent representation spans an entire archive, so a question asked today can reach a moment recorded months ago.
At the core of Galvision is a large visual memory model: a system designed from the ground up to retain context across millions of hours of video. Rather than treating each clip as an isolated input, it carries a continuous, structured memory of what it has already seen.
General-purpose AI models were not built for this. They reason brilliantly over a paragraph or a single image, but they lack comparable long-form video context — the moment the footage runs long, the thread is lost. Galvision keeps the thread, indefinitely.
That persistence is what turns raw recordings into something a machine can actually understand: events connected across time, identities that hold from one scene to the next, and meaning that accumulates instead of resetting frame by frame.
Natural language is the interface. Talk to your footage in plain words — through end-user tools or directly through the developer API — and it answers in clips, text, and edits.
Galvision maintains an active research effort on the hard problems beneath long-form video understanding — and runs a fellowship that brings outside researchers into the work.
About the fellowshipOur research advances how machines compress, caption, edit, and identify across millions of hours of video — the building blocks of persistent visual memory.
Memory-augmented reinforcement learning for token compression — keeping video understanding efficient as context stretches across millions of hours.
Detailed captioning models and benchmarks built for the messiness of real, user-generated video — describing what actually happens, not what's easy to label.
Agentic systems that edit long-form narrative video directly from natural-language prompts — turning a written brief into a finished cut.
Multimodal speaker identification that fuses audio, vision, and persistent identity memory — knowing who is speaking, and remembering them across the archive.
Persistent visual memory is the layer general-purpose AI was missing. Galvision builds it as foundational infrastructure.
Anyone on your team can talk to their video — search it, summarize it, transcribe it, and edit it — without writing a line of code. The same visual memory that powers the model is one conversation away.
Put persistent visual memory inside your own product through a developer API, and extend it onto dedicated AI hardware built for video. When an off-the-shelf model isn't enough, Galvision customizes the layer for specific deployments.
Right now, across millions of hours of footage, every frame is being watched, remembered, and made answerable. This is just the beginning of machine memory.
Bring persistent visual memory to your own video.
Search it, summarize it, transcribe it, edit it — in the tools your team already opens every day, and on dedicated hardware built for video.
The same visual memory adapts to wildly different footage — from a camera in a warehouse to a feed on a robot — and to the questions each field needs answered.
And when an off-the-shelf model isn't enough, Galvision customizes the visual memory layer for specific deployments — tuned to your cameras, your environment, and the events that matter to you.
Talk to us about your use caseIdentifies and describes suspicious behavior as it unfolds — and issues a live alert the moment it does, with words a human can act on, not just a motion blip.
Tracks the same individual across many cameras, even when their appearance changes — following a person through a building instead of losing them at every doorway.
Automatically flags slips and falls and attaches the supporting video evidence — so an incident is documented the instant it happens, not hours later.
Confirms routine tasks like cleaning and restocking are actually getting done — turning "we think it's covered" into a verifiable record on every shift.
Searches across recorded video in plain language to surface past incidents in seconds — describe what you're looking for and find it without scrubbing days of tape.
Watches service interactions to cut wait times and the customers they cost you — spotting the queue that's forming before it becomes the review you didn't want.
The same memory reaches beyond the enterprise: it powers consumer home-security cameras and video doorbells — bringing real understanding, not just motion alerts, to the front door.