Batch AI Generation: Many Assets, One Run

The workflow preview above is not a diagram, it is a live batch you can open. Four prompt nodes sit on the left, one per product, and each feeds its own Nano Banana Lite image node. All four image nodes collect into a single Batch Preview node, so one run produces the whole set instead of four separate sessions.

Because every prompt has its own node, the inputs stay legible: you can see exactly which prompt made which image and edit one without touching the rest. Add another prompt and image pair and the batch grows; the graph is the record of what ran. This is the same canvas behind our bulk image generation page, pointed at a product set.

Nothing here reads a spreadsheet with per-row seeds or temperature. You wire prompt nodes on the canvas, or send the inputs through the published API. The graph handles the fan-out and gathers every result in the preview.

A batch grows by copying a pattern, not by rewriting logic. Each item on this graph is a prompt node wired to a model node, so a bigger run is just more of those pairs pointed at the same Batch Preview. Ten products, fifty products, the shape does not change, and neither does the way you read the results.

When the input list lives outside the canvas, publish the graph and send the items through its API instead of adding nodes by hand. That is the point where a batch stops being a canvas exercise and becomes a repeatable job, much like an AI pipeline automation step you call whenever the list changes. The graph is built once; the inputs are all that move.

Every published workflow is both a REST endpoint and an MCP tool. Your backend can POST a set of inputs and get every asset URL back in the response, with webhook delivery for longer runs. Connect Wireflow through the Claude connector and the same batch shows up as a typed tool the agent can call, so it sends the list and receives the finished set in the conversation.

Be clear about the boundary: Wireflow is the generation layer, not the reasoning brain. It does not write your prompts, decide your product strategy, or parse CSV columns into per-row settings. It runs the graph you built across the inputs you send and returns organized outputs, the same way our AI workflow templates turn a proven graph into a job you rerun.

The batch on this page runs Nano Banana Lite on every image node because it is fast and cheap for volume, and running one model across the set is what keeps a batch looking like a set. Consistency here is not a setting you tune per item; it is a consequence of every node calling the same model with the same node config.

Change your mind and you change one thing. Repoint the image nodes to Nano Banana Pro when a run needs finer detail, and the whole batch upgrades together, no per-item edits. Because each stage is a node you can swap, the same pattern extends to video or audio nodes when a batch needs more than images, chained the way our AI model chaining pages describe.