At Axis Robotics, our vision is to build a compounding data engine—one that connects large-scale pretraining data, corrective post-training data, model deployment, and failure feedback in a continuously improving loop. Over the next 6–12 months, we will advance this vision across three connected fronts. On the product side, we plan to scale our egocentric data pipeline in September, with tens of thousands of hours already collected and product requirements being shaped with frontier labs. In October, we will expand our simulation data across more robot embodiments and atomic capabilities. By year-end, we plan to release a large-scale post-training dataset built through human-gated DAgger (HG-DAgger), where the policy acts autonomously and contributors intervene only when it needs correction. On the network side, we will expand our contributor ecosystem into Latin America and Eastern Europe, strengthen our 100K+ contributor network and grow toward 10K DAU. This expansion is designed to support the production of more than 500 hours of egocentric data and 50 hours of simulation data per day while building capacity for corrective post-training data. On the commercialization side, we plan to complete two to three new paid pilots by year-end and work toward becoming a preferred vendor for foundation model companies in Q1 next year. The longer-term goal is to embed the data engine directly into the training and deployment workflows of robot hardware companies, model developers, and industrial operators. These are not separate tracks. They reinforce the same flywheel: broader data coverage produces stronger models; stronger models reach new states; and new failures reveal what data should be collected next. That is the future we are building toward: Scale to produce data continuously. Diversity to reflect the complexity of the physical world. A closed loop to turn deployment feedback and failures into the next round of model improvement. Our north star is not simply more data. It is faster model evolution.