A task definition, optional reference documents, and the response or tool-use behavior a model should learn.
Synthetic training data
AfterImage
Generate training data around the work a model needs to do.
AfterImage creates conversational, tool-calling, structured-output, and preference datasets from task instructions and optional source documents. It provides the data-generation layer for teaching a model a specific task, with generated examples reviewed and evaluated before training.
01 / INPUT TO OUTPUT
Generate varied examples, inspect quality signals, and export the dataset to a separate training and evaluation workflow.
A reviewable dataset of conversational, structured-output, or other synthetic examples for model adaptation.
02 / WHAT IT DOES
Shape examples to a task
Generate multi-turn conversations, document-grounded questions, structured outputs, and tool-call examples.
Create preference data
Produce preference pairs for training methods such as DPO and use the quality checks available in the generation pipeline.
Export for training
Review datasets and export them in formats used by common model-training workflows.
03 / WORK WITH ALTAI
From a component
to your application.
ALTAI uses AfterImage to build training-data generation workflows for specific tasks. We define the task and example coverage, incorporate data review, and compare the trained model with a baseline on a separate test set.
Explore custom developmentWORK WITH ALTAI
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