Orom AI delivers neural geometric perception technology that enables real‑time, AI‑native localization and mapping for any modality and environment. The system runs fully on edge using standard monocular RGB cameras—integrating LiDAR or IMU when available—and achieves 70 FPS inference with a 500 MB GPU footprint, cutting hardware and compute costs 5–10× versus comparable setups. It is offered as developer primitives for agentic robotics, retail, and industrial applications, feeding directly into AI planning pipelines or providing spatial intelligence to human operators.
Funding
Funding not disclosed
Founders
Product
Problem
Robotic and autonomous systems often rely on expensive, specialized sensors and cloud-based processing to achieve accurate localization and mapping, which limits deployment on low-cost edge devices and restricts operation in diverse environments.
Solution
Orom AI offers a neural geometric perception engine that provides real-time, AI-native localization and mapping using any sensor modality—including monocular RGB cameras, LiDAR, or IMU—directly on edge hardware. The engine runs at 70 FPS with a 500 MB GPU memory footprint, delivering specialist-level accuracy while reducing hardware and compute costs by 5–10× compared to traditional setups. By delivering perception outputs as developer primitives, Orom AI enables seamless integration into robotics, retail, and industrial AI planning pipelines or direct use by human operators for spatial intelligence. The solution operates deterministically on commodity CPUs and mobile GPUs, eliminating the need for cloud reliance and specialized sensor suites.
Target Audience
Primary customers are robotics developers, retail automation engineers, and industrial automation teams that need high‑accuracy, low‑latency spatial perception on edge hardware.
Features
- Multi-modality support for monocular RGB, LiDAR, and IMU inputs without requiring domain-specific fine‑tuning
- Real-time 3D reconstruction and mapping at 70 FPS on edge devices with a 500 MB GPU memory footprint
- 5–10× lower hardware and compute cost versus comparable specialized perception systems
- Deterministic inference on commodity CPUs and mobile GPUs, enabling offline operation
- Developer‑focused primitives that output geometry ready for direct consumption by AI planning, decision‑making, and control modules
- Compact, lightweight architecture designed for deployment in diverse real‑world environments