Waylay is a platform that enables automation, orchestration, and decision-making for IoT (Internet of Things), GenAI (Generative AI), and LLM (Large Language Models) use cases. It provides a rule-based engine and workflow automation capabilities that allow developers to integrate data from different sources, process it, and trigger intelligent actions.
Waylay in IoT
In IoT, Waylay acts as a sensor data processing and automation engine. It ingests real-time data from IoT devices, applies rules and logic, and triggers actions based on sensor readings. Key features include:
- Data fusion: Combines sensor data from multiple sources.
- Event-driven automation: Uses rules to detect conditions (e.g., temperature exceeding a threshold) and trigger actions (e.g., send alerts, turn off a machine).
- Integration with cloud and edge computing: Works with IoT platforms like AWS IoT, Azure IoT, and Google Cloud IoT.
Waylay in GenAI & LLMs
Waylay’s automation capabilities extend to AI and LLM orchestration, allowing AI-driven workflows. In this context, it helps:
- Automate decision-making: Uses LLMs to analyze inputs (e.g., repair manuals, customer requests) and generate structured responses or actions.
- Connect AI to external APIs & tools: Facilitates seamless interaction between AI models and enterprise systems, databases, and external services.
- Hybrid AI + rule-based reasoning: Combines rule-based logic with GenAI capabilities for more reliable automation (e.g., an LLM suggests an action, but Waylay’s rules validate before execution).
Bringing IoT & AI Together
Waylay’s ability to integrate IoT and AI enables advanced use cases such as:
- Predictive maintenance: IoT sensors detect anomalies, and an LLM helps summarize the issue for human operators.
- Automated troubleshooting: AI interprets IoT data and suggests fixes.
- Voice-controlled automation: LLM-powered chatbots allow users to control IoT devices via natural language.
- Interactive CRM bot applications
Waylay Agentic Studio – RPA & Agentic Apps
- A plugin interface for structured automation workflows.
- A unified approach to handling APIs and external connectors for both RPA tasks and AI agents.
- AI-enhanced rule execution where LLMs assist in decision-making but operate within defined constraints.