We present TATOS (Text-Angle-Trajectory-Optimized-Sequence), a novel architecture for language representation that operates on geometrically-grounded concept sequences rather than conventional token streams. A proprietary compression codec maps natural language to 2,048 canonical concept vectors, achieving a 25x vocabulary reduction compared to standard transformer approaches. A 304M parameter model trained on 2.5 million concept sequences achieves 90.5% validation accuracy and 74.5% token accuracy on unseen data, trained on a single consumer GPU for under $0.30. The system demonstrates a consistent scaling curve from 10M to 304M parameters with no observed ceiling. All results produced at BeccaLabs, Morgan MN, May 2026.