Cheat Layer

Teams use Cheat Layer by turning a routine into a written set of steps and letting the platform run it across the tools they already use. A typical workflow starts with choosing a trigger—such as a new form submission, an inbound email, a calendar event, or a spreadsheet update—then describing what should happen next, including checks, conditions, and the final output. The AI turns that instruction into a working process you can test, adjust, and rerun until it matches how your business actually operates.

In day-to-day operations, it’s often applied to lead flow and follow-up. You can route new contacts to the right pipeline, enrich basic details, score or qualify based on rules you define, and send the right message or task to the right person. Marketing teams can use it to run simple experiments by generating variations, tracking results, and iterating campaigns without rebuilding the same logic each time. For product and ops work, it can collect data from multiple sources, clean and format it, make decisions when fields are missing or conflicting, and publish outcomes like updated records, drafts, decks, or a basic landing page.

When a process is small, users can assemble steps with the visual builder and add AI-driven actions where needed. For more involved automations, Project Atlas is used as a prompt-driven workspace to describe multi-step behavior, exceptions, and branching paths in plain language. Once running, workflows become reusable playbooks: swap inputs, change constraints, and keep the same logic to produce consistent results. If something breaks or an edge case appears, you edit the instruction and regenerate or refine the automation instead of starting from scratch. Help is available through the community Discord and email support.