Turn natural language into complex agentic automations and custom SaaS tools.
Cheat Layer is a sophisticated automation platform that leverages Large Language Models (LLMs) and a proprietary agentic framework called Project Atlas to bypass the complexities of traditional RPA. In 2026, it stands as a market leader in 'Semantic Automation,' where users describe workflows in natural language, and the system autonomously generates code, manages DOM selectors via machine learning, and executes cross-platform tasks. Unlike legacy tools like Zapier which depend on APIs, Cheat Layer operates at the user interface level, allowing it to automate any website or desktop application even if an official API does not exist. The platform's technical architecture is built for scalability, featuring a 'No-Code Custom GPT' builder that allows enterprises to deploy private, specialized agents. Its 2026 market positioning focuses on the 'Automation-as-a-Service' model, providing robust white-labeling capabilities that enable agencies to resell custom-built automation tools as standalone SaaS products. By utilizing a semantic engine, Cheat Layer automations are significantly more resilient to UI changes than traditional XPath-based bots.
A state-of-the-art agentic planner that decomposes complex goals into executable steps using custom fine-tuned LLMs.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses computer vision and LLM analysis to identify UI elements based on meaning rather than fragile HTML paths.
Infrastructure to wrap scripts into a branded UI with user authentication and payment processing.
The ability to create internal 'Company Brains' that interact with private web dashboards.
An execution engine that controls the mouse and keyboard at the OS level using computer vision.
Listen for external triggers and pass data into an LLM to decide the next automated action dynamically.
Serverless environments that execute browser scripts 24/7 without needing the user's computer to be on.
Manual LinkedIn prospecting is slow and conversion is low due to generic messaging.
Registry Updated:2/7/2026
Collecting data from multiple sites that block traditional scrapers.
Old desktop software requires manual data entry from modern web-based orders.