Custom AI
training data.
Build the training data your models need.
From human demonstrations to specialized workflows and post-training datasets, Streamlytics works with AI developers to create data tailored to their research objectives.
Discuss a Data PartnershipStart with the capability
you want to develop.
Better training data starts with understanding what your model needs to learn.
We work with AI labs and model developers to translate research objectives into custom data projects—from human-generated demonstrations and professional workflows to specialized post-training datasets.
Each engagement is shaped around your requirements, not a fixed inventory of available data.
Define the data.
Define the standard.
Built to your specifications
We work with you to establish the data formats, contributor qualifications, task requirements, and quality standards needed for your project.
Quality from collection to delivery
Every project is designed around clear requirements for consistency, completeness, provenance, and review. Pilot collections can help validate the approach before scaling production.
Delivery and licensing
Datasets are prepared and delivered in the formats your team needs, with clear licensing terms, permitted uses, and agreed quality criteria.
Data for real-world
AI capabilities.
Explore the types of specialized training data we can develop around your research needs.
Professional workflow data
Human demonstrations of real-world work, capturing the actions, decisions, and outcomes involved in completing professional tasks.
Computer-use demonstrations
Human interactions with software applications, including navigation, actions, task execution, and observed results.
Human-agent interactions
Real-world examples of how people work with AI agents, including instructions, feedback, corrections, and collaborative task completion.
Coding-agent trajectories
Development sessions documenting prompts, tool use, code modifications, debugging, testing, and task outcomes.
Projects are scoped according to your requirements, contributor availability, and agreed delivery terms.
Let’s define the data
your model needs.
Discuss your target capabilities, data requirements, and evaluation objectives with Streamlytics.