for spotting automation fit on calls
A single-file, embeddable process modeller that collapses three traditions — BPMN modelling rigour, case-handling logic, and RPA-style system orchestration — into one mental model the operator actually thinks in: a process is a chain of artifacts. Each node has a type. You talk, the flow builds, and the AI argues for where it belongs.
A conversational process modeller delivered as a single embeddable HTML file. The operator describes the process in plain English — "a WhatsApp order comes in, we interpret it, log it, then a human reviews it and routes it" — and the studio builds the flow live, one artifact at a time, while the transcript argues for every choice it's making.
Every node is one of seven artifact types: Trigger, Task, Decision Gate, AI Agent, Human-in-the-Loop, System / Integration, and Output. That's deliberately fewer primitives than BPMN and more expressive than an RPA step list — enough to describe any real operational flow without needing a notation PhD to read it.
It runs fully client-side: one file you can paste into a sales call, a customer workshop, or a discovery deck. No server, no install, no account.
Most "AI automation" conversations today happen at the tooling layer: point an agent at a CRM field, a ticket queue, an inbox. It automates one step. The process around it — who got the message, what triggered the next action, where the human still has to intervene — stays implicit.
This studio argues the other way. The unit of automation is the process, not the tool. The AI Agent is one node type out of seven, sitting in a flow next to Triggers, Systems, and Humans-in-the-Loop. That reframes the design question from "what can the agent do?" to "what should the agent be responsible for, given everything else the process is already doing?"
In practice: the operator sees exactly which steps an agent owns, which steps a system owns, and which steps stay with a person. Responsibility is a shape on the canvas, not an assumption in a vendor pitch.
Enterprise AI buyers almost never struggle with the question "could AI do something here?" — the answer is always yes. They struggle with "where, specifically, and with what trade-off?"
Normally, getting to that answer takes a run of discovery workshops and formal BPM mapping before anyone even names where the bottleneck sits. This tool is built to fold that discovery into the call itself, so the sales team walks out of a demo with a real answer instead of a follow-up meeting.
A live process modeller answers that question while the customer is in the room. You describe the current-state flow; the studio draws it. You mark where the pain is; it proposes an agent node and tells you what the agent is taking on — and, just as importantly, what stays human. The transcript keeps the reasoning visible so nothing looks like magic.
The output isn't a generated diagram — it's a shared artifact the room just built together. That artifact is the most valuable thing a discovery call can produce: a concrete, node-by-node picture of where automation pays off, where it doesn't, and why.