DeepReach

Real-World Ground Truth: Capturing the Nuance of Industrial Dexterity. Sim-to-real isn't just a technical challenge, it’s a data acquisition challenge. To move beyond laboratory prototypes, robotic models require exposure to the "messy" reality of actual work environments. We specialize in capturing high-fidelity, ego-centric (first-person) data directly from the frontlines of US-based warehouses, manufacturing plants, and agricultural sites. The Value of Our Data Acquisition: 1. Unstructured Reality: We go where lab cameras can’t. Our datasets capture the high-variance lighting, complex occlusions, and diverse object interactions inherent to real-world industrial workflows. 2. The Manipulation Edge: By focusing on the "last inch" of interaction through ego-vision, we provide the granular hand-object telemetry necessary to train models on production-grade dexterity. 3. Localized Context: Data captured within authentic US infrastructure, providing the specific environmental cues, packaging standards, and operational logic required for localized deployment. We provide the high-fidelity visual foundation that allows models to transition from general perception to task-specific physical mastery. Interested in our data acquisition capabilities or looking for specific industrial manipulation sequences? Let’s connect. #Robotics #DataAcquisition #ComputerVision #EgoCentric #IndustrialAI #Dexterity #EmbodiedAI #SimToReal #TrainingData #LogisticsAutomation

DeepReachAI Joins the NVIDIA Inception Program!! We’re excited to share that DeepReach has been selected into the NVIDIA Inception Program. This milestone comes at a meaningful moment for us. In January 2026, our ARR doubled, driven by growing demand for real-world robot deployment, tele-operation, and production-grade execution. It’s a strong signal that the market is ready to move beyond demos, and into robots doing real jobs. As we enter the next phase, our focus shifts into deep technical accumulation. We’re deploying robots into real production environments and training them to execute concrete actions and task modules. Through continuous in-the-wild data collection, tele-ops, and model iteration, we’re strengthening robots’ ability to generalize tasks and scale reliably across sites. We’re grateful for the intense effort and support from our team over the past few weeks, and for the trust and guidance from our key investors and partners who continue to back this journey. Being part of NVIDIA Inception reinforces our conviction that the future of robotics will be built at the intersection of deployment, data, and real-world execution. More to come as we keep building. #Robotics #PhysicalAI #NVIDIAInception #AIinProduction #RobotDeployment #Teleoperation #DeepTech

Physical AI has a data representation problem, not a model problem.

The "GPT moment" for robotics won't come from bigger models, it will come from a unified data language for the physical world. Great night in Palo Alto debating the "Dirty Data" problem with the best from NVIDIA, OpenAI, xAI and Meta. We took a deep dive into NVIDIA’s EgoScale-the conclusion? Scaling egocentric (POV) data is the only way to bridge the gap between pixels and action. At Deepreach.ai, we aren't just collecting data; we are building the infrastructure to encode real-world interaction into something models can finally "understand." Check out the full recap below. 👇 #PhysicalAI #SpatialAI #Robotics #EgoScale #DeepTech #MachineLearning #WorldModel #DeepReach