About our Tech:
At AEFX, we’re building plugins on technology that simply wasn’t practical until very recently. Advances in neural networks, real-time inference, and modern CPU/GPU architectures have opened the door to a new kind of audio processing—one that captures the true behavior, tone, and complexity of analog gear instead of approximating it.
For decades, plugin design relied on simplified models and static algorithms. Today, with neural-based systems powered by frameworks like RTNeural, we can learn the sound of hardware directly—preserving the harmonic richness, nonlinearities, and subtle interactions that give real gear its character. And this is just the beginning. Every year, breakthroughs in AI and optimization are pushing this technology further, faster, and more efficiently.
We believe this is where the industry is heading.
AEFX is proud to be among the early developers bringing neural capture technology into mixbus and mastering workflows—not as a gimmick, but as a foundational shift in how audio tools are built and experienced.
That said, we believe in being transparent:
Neural processing is still demanding. Higher-quality captures require more CPU, and at larger buffer sizes (1024–2048), some systems may experience instability or performance issues depending on configuration. To ensure flexibility, we provide multiple capture tiers—allowing you to choose between maximum fidelity and efficient performance. In many real-world scenarios, lower-tier captures still deliver outstanding musical results with far less system load.
The good news? This space is evolving rapidly.
RTNeural—one of the core technologies behind our plugins—is preparing to release version 2.0, with significant improvements in efficiency and reduced CPU usage. As these advancements arrive, our platform will evolve alongside them.
Other area’s of interest are new grey box implementations of neural network behavior models. Meaning, variable control of devices can be built into the network thru back propagation opening the door to flexible controls instead of limited capture states. This area is the fastest evolving topic is real time audio neural network implementation. As this space grows, we will grow with it to stay at the forefront of what is conceptually possible for audio plugins.
Looking ahead, we are actively exploring GPU offloading to further reduce CPU strain and unlock even more powerful models in real time. The goal is simple: give you the most authentic sound possible, without compromise.
This is the future of audio processing—and we’re just getting started.