Making AI models leaner.
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.
Efficiency
Smaller models mean less compute, lower costs, and faster inference. We help you get there without retraining from scratch.
Precision
Targeted unlearning — not brute-force pruning. Remove specific capabilities while keeping everything else intact.
Transparency
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
AI models are bloated
A typical 7B parameter language model has billions of redundant weights. These parameters store memorized training data, overfitted patterns, and unnecessary knowledge that inflates model size without improving performance on your specific task.
Retraining from scratch is expensive — both in compute and engineering time. Most teams don't have the resources to do it, so they ship bloated models and pay the cost at inference time.
Surgical unlearning
Instead of retraining from scratch, Remap Studios identifies and removes unnecessary parameters while preserving the model's core capabilities. Our Retain-Aware algorithm uses a dual-objective loss function that simultaneously forgets target knowledge and protects everything else.
Built on scientific rigor
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.
Key areas of research include:
- →Selective neuron erasure with minimal collateral damage
- →Retain-aware gradient optimization for knowledge preservation
- →Efficient weight analysis for identifying redundant parameters
- →Benchmarking unlearning quality across 89 capability probes
Building the future of efficient AI
Harsh Dev
Founder & CEO
Building tools to make AI more efficient and accessible. Background in ML research and full-stack engineering.
Get in touch
Have questions, feedback, or want to partner? We'd love to hear from you.