The startup develops a geospatial data platform that utilizes artificial intelligence, specifically reinforcement learning and computer vision, for object detection in mobile mapping data. This technology enables infrastructure and asset management professionals to efficiently monitor and analyze assets throughout their entire life cycle.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Monitoring infrastructure assets, such as roads and power grids, is a complex and costly process. Traditional methods often involve manual inspection, which is time-consuming, prone to human error, and difficult to scale. This can lead to delayed detection of critical issues, resulting in increased maintenance costs and potential safety hazards.
Solution
EnliteAI offers a geospatial data platform that leverages computer vision and reinforcement learning to automate infrastructure asset monitoring. The platform, called Detekt, uses mobile mapping data to automatically identify and classify objects, such as road signs, road markings, and road defects. By applying reinforcement learning, the platform optimizes power grid operations, improving reliability and reducing congestion. This technology enables infrastructure and asset management professionals to efficiently monitor and analyze assets throughout their entire life cycle, leading to proactive maintenance and reduced operational costs.
Target Audience
The primary target audience includes infrastructure and asset management professionals, power grid operators, and organizations involved in geospatial data analysis.
Features
- GeoAI technology for identifying road signs, road markings, and road defects from mobile mapping data
- Reinforcement learning framework (Maze) for power grid optimization, enhancing adaptability and reliability
- AI-driven analytics for proactive maintenance and reduced operational costs
- Support for multi-step and multi-agent scenarios in complex industrial decision problems
- Integration with Hydra Config System for manageable application and experiment configuration
- Tools for imitation learning from teacher policies and policy fine-tuning