Unlimited AI clipping.
Runs
entirely on your Mac.
On-device inference. Neural Engine optimized. Built for Apple Silicon. No limits.
The problem with AI video tools isn't intelligence. It's architecture.
We spent over a year building 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 genuinely fast. The kind of fast that processes a full hour of footage in 90 seconds, on your machine, with no waiting.
The result is a product that runs entirely on your Mac.
Our models aren't repurposed general-purpose AI. They're fine-tuned specifically for video, trained to understand engagement patterns, speaker transitions, and clip structure in ways that general models simply don't. Because the heavy lifting happens on your device, there's no usage cap, no per-clip cost, and your footage never leaves 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?
Reelify AI uses an on-device Edge AI workflow. Instead of sending every video through
metered cloud servers, the core AI work runs on your Mac.
Since your device handles the heavy processing, we do not need to meter your usage with
monthly
credit limits.
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?
Never. No video frames, no audio files, and no media metadata ever leave your Mac. This isn't just a privacy policy promise. It's a structural one. The application's core architecture is built so the heavy video pipeline is strictly bound to your local environment. Your footage can't leave your Mac because the pipeline was never designed to send it anywhere.
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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