Hi, I'm Lloyd Handyside, founder of NeuroForge.

I started ERAIS from a fairly simple question. Why do we treat brute-force compute and throwing away already-trained models as inevitable parts of advancing AI?

ERAIS, the Efficient Routed Adaptive Inference System, E-R-A-I-S, is a novel sparse and adaptive AI architecture designed to use computation selectively rather than activating everything uniformly.

We're developing two paths into the same architecture. New models can be trained natively as ERAIS, and the second path is to convert existing dense models into ERAIS, with the goal of preserving useful capability while reducing the active compute needed to run, update, and continue training them.

Supporting that architecture is a rigorous research and analysis stack: exact checkpoint recovery, model and data lineage, controlled evaluation, the ERAIS Observatory, and evidence controls that keep measured results separate from projections and unproven claims.

We're already seeing strong, bounded engineering results across CPU and GPU systems, but the bigger point is how we do the research. Negative results stay in the record, and claims move forward only when the evidence does.

The goal is straightforward: make advanced AI more efficient, more accessible, and easier to keep improving without throwing away what already works.

That's what we're building at NeuroForge.