Unlimited AI clipping.
Runs
entirely on your Mac.
On-device inference. Neural Engine optimized. No cloud GPUs. No limits.
The problem with AI video tools isn't intelligence. It's infrastructure.
Every major AI clipping tool on the market runs on cloud GPUs. That's not a design choice, it's a constraint. General-purpose models are too heavy to run locally, so they offload to servers, charge per use, and put a ceiling on how much you can create.
We decided that was the wrong tradeoff.
Over the past year, we've built an inference engine designed from the ground up for Apple Silicon, using the Neural Engine, Metal Performance Shaders, and ANE-targeted model quantization to make on-device AI clipping not just possible, but fast. Our models aren't repurposed. They're fine-tuned specifically for video, trained to understand engagement patterns, speaker transitions, and clip structure in ways general models simply don't.
The result is a pipeline where the heavy lifting runs entirely on your machine, stays thermally stable across long sessions, and never relies on cloud GPUs. No per-clip cost. No usage cap. No video data leaving your hands.
This work was built in collaboration with the NVIDIA Inception Program, giving us access to hardware optimization infrastructure that doesn't exist in the open ecosystem.
Unlimited clipping isn't a pricing decision.
It's an engineering
one.
Frequently Asked Questions
How is unlimited AI clipping possible?
Unlike cloud-dependent tools that route your footage through remote inference servers and bill per API call behind the scenes, our core engine runs entirely on-device. We've built a custom multi-stage inference pipeline that leverages Apple Silicon's Neural Engine, the Metal Performance Shaders framework, and ANE-aware kernel fusion to execute model forward passes at near-native speeds. There's no egress cost, no GPU rental, no rate limits. The compute is yours.
Why doesn't it overheat or slow my Mac down?
Most locally-run AI tools are direct ports. They weren't written for Apple Silicon, they
were written for CUDA and cross-compiled. That means they miss the unified memory
architecture entirely and hammer your CPU cores instead of the Neural Engine, which is why
they throttle.
We rebuilt our inference stack from the ground up using Metal and Core ML with ANE-targeted
graph optimization. Our models are quantized and pruned specifically for the M-series die
layout, which means workloads that would throttle a naive implementation run cool and
consistently on ours, even across multi-hour sessions.
Are these just off-the-shelf AI models?
No. Foundation models are trained on general objectives, so they have no inherent understanding of pacing, speaker transitions, engagement density, or clip-worthiness. We spent over a year fine-tuning on domain-specific video datasets, with custom loss functions designed around what makes a clip actually work. The models that ship in our product bear little resemblance to what we started with. That delta is the product.
Why can't other tools do this?
The honest answer is that the stack required to do this well isn't one problem, it's six. Model accuracy, inference speed, thermal envelope, memory pressure management, ANE scheduling, and on-device fine-tuning signal all have to be solved simultaneously, and they interfere with each other. Most teams optimize one and break the others. We've been working on this specific intersection, with direct hardware-level support from the NVIDIA Inception Program, for long enough that the gap compounds.
Is my footage uploaded anywhere?
No video frames or metadata ever leave your machine. To ensure the highest accuracy possible, we temporarily use a secure cloud API strictly to generate the initial text transcript (which is instantly deleted and never stored). However, the core AI intelligence (analyzing the video itself, judging engagement, finding the hooks, and rendering the cuts) happens 100% locally on your Mac's Neural Engine. Your heavy media files stay local, period.
What Macs does it run on?
Any Mac with Apple M-series silicon. The Neural Engine and unified memory architecture present in M1 chips and later is what makes our inference pipeline viable at this speed. Intel Macs are not supported because the hardware substrate is fundamentally different.
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