Oysterworkflow

OysterWorkflow is a platform for building AI workers that learn from how a team actually does its work. Instead of scripting a workflow by hand, the product observes real operations, including the apps used, steps taken, decisions, corrections, exceptions and handoffs, while people continue working as usual. The vendor describes a four stage loop. Oyster observes the work, compares each new case with what the worker already knows, proposes reviewable updates such as missing branches and exceptions, and then reuses approved experience across the team. Nothing becomes part of a worker until a person approves it. Learned workflows can run around the clock on computers the team owns, and approved know how can be shared among coworkers. The site illustrates this with an inbound sales qualification playbook that learns to separate qualified requests, suspicious senders and low value inquiries, then prepares a follow up or a polite decline for review. It is aimed at operations teams with repeated, judgment heavy workflows that are hard to capture in a fixed automation. The suggested way to start is with one repeated workflow, adding real cases until the worker covers more of the decisions and exceptions. A free desktop download is offered along with a demo request. The vendor does not publish paid plan prices.