Celesnity
Celesnity is building a "world model" for industrial manufacturing by capturing a continuous physical record of every action on the shop floor and its consequences, then training a single model to learn how the entire plant behaves. The platform connects humans, software, hardware sensors, and robotics into a shared context, enabling predictive maintenance, root-cause analysis, and forward-looking operational guidance. The system is designed to go live in a real plant within six weeks, processing tens of thousands of events daily.
- Manufacturing / Industry 4.0
- Industrial Automation
- Artificial Intelligence
- Internet of Things
- AI Agents
- Software Only
- Hardware
- Data & Analytics
- Robotics
Funding
Founders
Founder details are not available yet.
Product
Problem
Today's factory is not one system but a collection of disconnected parts, each running on a different version of reality. Managers see the consequences of operational issues but rarely the root causes, and the lack of shared context prevents timely, informed decision-making across the enterprise.
Solution
Celesnity is building the world model for industry by capturing a continuous physical record of every action on the shop floor and what it caused, then using that record to train one model that learns how the whole plant behaves. The platform connects humans, software systems, hardware sensors, and robotics into a shared context, giving operators and managers a unified view of plant operations. It answers forward-looking questions such as "what happens next before you act," enabling predictive maintenance by reading equipment wear and servicing before failure, and root-cause analysis to fix issues at the source. The model acts as the brain while the platform, hardware, and robots serve as its body, distributing intelligence across human and machine layers for coordinated action.
Target Audience
Manufacturing operations managers, plant directors, and industrial automation teams seeking to unify disconnected systems and enable autonomous decision-making across their factory floors.
Features
- World model architecture that learns whole-plant behavior from a captured physical record of actions and their consequences
- Human layer that keeps people in control with contextual guidance and shared operational awareness
- Software layer that provides one shared context across existing enterprise systems
- Hardware layer with on-floor presence to capture physical signals and events continuously
- Robotics layer that provides insight into motion and enables coordinated physical responses
- Predictive capabilities to read equipment wear, shift batches to preserve stock, and find root causes at the source
- Cross-plant learning where each joining plant makes the model smarter for all of them