Custom model training

LetzAI trains image models on your own photos, products, and visual style. A Beam agent watches a shared folder for approved reference images, reads the file metadata, and queues a training job under the model your rule specifies. When images are low resolution or a batch mixes unrelated subjects, the agent pauses and asks a person to confirm the set. Someone on your team decides which references belong in a model and whether a finished model is ready to generate assets for a live campaign.

Image generation from prompts

LetzAI produces images from text prompts using the models you have trained. A Beam agent reads a prompt request from your brief or backlog, generates the image against the chosen model, and files the result in the campaign folder your rule points to. Prompts that describe a real person, a competitor, or a sensitive scene are routed to a person before any generation runs. A human reviews every generated image for brand fit and accuracy before it is used in any published material. The agent records which model and prompt produced each image so the team can trace it later if a question comes up.

Asset library organization

LetzAI keeps generated images and the models behind them in one library. A Beam agent reads new generations, reads their prompt and model tags, and applies your naming rule to file each asset where designers expect to find it. When an asset lacks tags or duplicates an existing one, the agent notifies the library owner. A person decides which assets are approved for reuse, which should be archived, and how the folder structure grows as new product lines appear. The agent keeps the library index current so designers always reach the latest approved version of an asset.