Transform your repetitive workflows into scalable automations that can run while you sleep. Magical uses Agentic AI to automate entire processes, with zero human involvement required.
30k apps
700k users
60k companies
Robotic Process Automation (RPA) workflows fail as soon as they encounter something they weren’t predefined to complete. Because they can’t understand nuance—they become a nuisance instead.
Long and costly implementation means it can take months to set up even a simple automation.
RPA can only automate workflows that follow exact rules and steps. They struggle with complexity.
Automations are rigid and break easily, which leads to more manual work and ongoing maintenance costs.
Maximize efficiency with full process automations. Move data between systems, navigate forms, and submit info—all without any human inputs.
AI agents use reasoning models, real-time data retrieval, and goal-based execution to make your automations more reliable than traditional rule-based approaches.
Scale up your automations and batch processing together with Agentic AI agents that live on virtual machines. No more limitations on how much you can automate.
Move and transform data between apps automatically. Magical handles date conversions, text extraction, and formatting – no manual cleanup needed.
Extract data from any PDF and populate it into online forms instantly. From medical records to insurance forms, Magical's AI agents handle it all.
Our AI agents adapt to changes and handle edge cases automatically, ensuring your automations keep running reliably.
Agentic automation is an AI-powered solution that autonomously perceives, decides, and acts to achieve its stated goals while adapting to new situations based on predefined instructions.
Agentic AI combines advanced techniques like large language models (LLMs) with traditional AI methods, creating autonomous agents. These agents can analyze data, make decisions, and perform actions with minimal human intervention. They are designed to adapt and learn from their experiences, continuously improving their performance. Unlike traditional rule-based automation, agentic AI excels in dynamic environments where adaptability and decision-making are crucial.
While traditional automation relies on pre-defined rules and structured processes, agentic AI operates more like a human worker. It can understand context, adapt to changing situations, and make judgments based on the available data. This makes it suitable for automating more complex, unstructured tasks that require decision-making and problem-solving abilities.
Agentic AI systems rely on a combination of technologies: 1) Large language models (LLMs): These models process and understand human language, enabling agents to interact with users, understand instructions, and extract meaning from text. 2) Machine learning algorithms: These algorithms allow agents to learn from data, identify patterns, and make predictions, improving their decision-making over time. 3) Integration with enterprise systems: Connecting agents to various data sources across an organization gives them a comprehensive understanding of the business context, allowing for more informed actions.
Agentic AI offers numerous potential benefits: 1) Increased efficiency and productivity: Agents can handle complex tasks, freeing human workers to focus on strategic and creative endeavors. 2) Enhanced customer experiences: Agents can provide personalized and responsive support around the clock, leading to increased customer satisfaction. 3) Improved decision-making: Agents can analyze vast amounts of data to identify trends and insights, supporting more informed business decisions. 4) New possibilities for automation: Agentic AI expands the scope of automation, enabling businesses to optimize complex processes that were previously challenging to automate.
Agentic AI offers a number of features that make it well-suited to addressing the complexities of revenue cycle management:
Agentic AI can understand and adapt to the nuances of complex processes. Revenue cycle management workflows often involve a series of interconnected steps, requiring analysis of unstructured data, and decision-making based on a variety of factors. Agentic AI's ability to understand context, adapt to changing information, and learn from experience makes it ideal for navigating these dynamic processes.
Agentic AI can interact with multiple systems. Agentic AI agents can be integrated with various systems involved in the revenue cycle, such as electronic health records (EHRs), billing systems, and payment gateways. This allows for seamless data flow and process automation across different departments and platforms.
Agentic AI can improve efficiency and accuracy. By automating tasks such as claims processing, payment posting, and follow-up, agentic AI can reduce manual effort, minimize errors, and accelerate the revenue cycle.
