Build AI without pretending hardware is infinite.
Geocentric is an AI company focused on compact models, efficient training, local inference, and the engineering required to make all three better.
Closer to the machine. Closer to the model.
Geocentric works across the full model lifecycle: architecture, pretraining, post-training, evaluation, inference, vision, and release engineering.
The company starts from a practical constraint: strong model work should not require unlimited hardware. That pressure forces better systems, sharper measurements, and more disciplined model design.
Engineering discipline is part of the product.
Measure before scaling
Profile speed, memory, context use, and model quality before turning an experiment into a claim.
Keep the architecture practical
Training tricks should not force exotic inference formats unless the gain justifies the complexity.
Make small models earn their size
Capability per parameter matters when the target machine is a consumer GPU instead of a datacenter cluster.
Own the full loop
Model quality depends on data, architecture, kernels, evaluation, post-training, and inference working together.