Remap Studios was built on a simple observation: AI models learn too much. They hoard redundant knowledge, overfit to noise, and carry biases they don't need. Every unnecessary parameter costs compute, money, and time.
We're building the tools to fix that — to surgically remove what models don't need, while preserving what they do.
Smaller models mean less compute, lower costs, and faster inference. We help you get there without retraining from scratch.
Targeted unlearning — not brute-force pruning. Remove specific capabilities while keeping everything else intact.
Every operation is auditable. We show you exactly what changed, why, and the measurable impact on your model.
35–70%
Typical parameter reduction
90%
Less retraining compute
~0
Accuracy loss
10×
Faster iteration cycles
Our approach is grounded in peer-reviewed research on machine unlearning, neural network pruning, and knowledge distillation. We publish our methods, share our benchmarks, and invite scrutiny.
The core unlearning engine is open source. We believe in building in public and contributing to the ML community.