Home / Solutions / Real-time AI video
Primary solution

You optimised inference to 40 ms a frame. Then the last mile added 300.

Built for world models, generative characters and interactive video: the delivery layer for teams whose pixels don't exist until the instant a user sees them.

The pain, specifically

Frame-by-frame is harder than cloud gaming, not easier

No pre-encoded buffer

A game engine can render a few frames ahead. A generative model produces the next frame only once it knows what happened to the last one. There is nothing to buffer.

Irregular frame timing

Inference time varies frame to frame. Delivery has to absorb that jitter without the user perceiving it as network jitter.

The loop collapses, it doesn't degrade

Miss a frame in an interactive generative loop and the model's next output is already wrong. There's no graceful drop to a lower bitrate, only broken or not.

Fit

Sell them the pipe, not another model

We're not a model-optimisation company and don't compete with your inference stack. We integrate below it: you generate the frame, XtreamLINK gets it to the user's screen at conversational latency, over two paths, natively in the browser.

Who this is for

Real-time world model labs, generative video and character platforms, AI-native consumer apps, and inference clouds that want a delivery story for their customers.

What changes for you

Keep your model, your inference host, your app. Point your output frames at us and we take it from there. Get per-session QoE data from day one.

Design partner program

Three slots for real-time AI teams

Free integration, discounted production usage for 12 months, and a co-published benchmark once the numbers hold in your production traffic.

Apply for a slot →