Hedra Model Factory is an autonomous post-training and inference platform for robotics, manufacturing, biology, and defense teams that need models trained on their own video, sensor, and simulation data, then served with low latency on specific hardware. The company says the recursive post-training loop behind frontier research has stayed inside a few labs, while these teams need models that can be retrained from production evidence and served under tight response-time constraints. The San Francisco company, backed by Andreessen Horowitz, Index Ventures, Abstract Ventures, and the Alexa Fund, built that stack while shipping its own video and world models. Founder and CEO Michael Lingelbach previously worked on reinforcement learning for visual and physical systems at Stanford and NVIDIA, and the research team draws people from Stanford, NVIDIA, and Meta Superintelligence Labs. Teams do not need a finished model or dataset to start. Forward-deployed researchers define the target behavior, prepare data, and develop or source reinforcement learning environments, then run supervised fine-tuning or reinforcement learning with research agents on GPU sandboxes that use Hedra's inference engine. They evaluate against the behavior the application needs, optimize the model and serving stack for the hardware, and deploy on Hedra or on the customer's infrastructure. Researchers set the goals and decide which changes to keep, agents run the experiments, and after deployment only versions that pass the customer's evals are released. Sandbox capacity does not require a long-term GPU commitment, and an existing model can be served on the same engine without a training engagement. With the launch, Hedra reported that distillation and inference optimization cut 2K image edits from about 22 seconds to 3.6 seconds on the same four AMD Instinct MI355X GPUs, moving from a 30-step bf16 model to a distilled 14-step model in under a week. In a separate simulation study, the company said matching a new gripper's reach and grasp point in the wrist camera restored a robot policy's task success from near zero to near 100% with no retraining.