This startup develops multi-agent AI systems for advanced automation and simulation using Model-Based System Engineering and Agent-Based Modeling. They create and customize these systems to enhance automation capabilities across various applications.
Funding
Funding not disclosed
Founders
Product
Problem
Many organizations struggle with fragmented workflows and siloed automation, leading to inefficiencies and hindering real-time process optimization. Traditional ERP systems rely on static data and manual processes, lacking the adaptability to changing business conditions. This makes it difficult to achieve seamless operation and efficiency across all functions.
Solution
Bili offers an agentic layer for enterprise AI, providing an end-to-end ecosystem that enables human-AI collaboration through a unified suite of microservices and specialized Gen-AI solutions. The platform leverages Model-Based Systems Engineering (MBSE) and a multi-agent framework to create AI replicas of employees, automating tasks across departments and driving efficiency. Bili's AI digital twins model entire organizational processes, ensuring every function is interconnected and aligned with strategic goals. The system dynamically manages and reallocates tasks between AI agents and human employees, operating autonomously across finance, HR, operations, and other functions. This approach allows for real-time process optimization and continuous improvement, ensuring operations are always running at peak efficiency.
Target Audience
Bili targets organizations seeking to enhance productivity, streamline workflows, and optimize resource allocation through AI-driven digital twins and multi-agent automation.
Features
- Multi-agent automation for end-to-end process management
- ERP simulation using Agent-Based Modeling (ABM) to model organizational dynamics in real time
- Intuitive no-code user interface for agent creation and management
- Real-time collaboration between human employees and AI agents
- Dynamic dashboard for monitoring and managing AI agent performance
- Federated fine-tuning for continuous learning and optimization
- Model-Based Systems Engineering (MBSE) for streamlined operations and interconnected workflows
- Compatibility with existing AI/ML tech stacks