Expert demonstrations
Supervised fine-tuning
Purpose-built examples show how to interpret a request, use the available context, and produce a result that meets an explicit standard.
Building the data foundation for AGI.
Human expertise, structured for AI. We create the datasets, feedback, and environments that help models do meaningful work.
Designed around the way intelligence develops
DemonstrateEvaluateInteractImprove
Expert knowledge. Structured for learning.
The challenge
The hard part of a task is rarely in the final answer.
A useful result depends on the right assumptions, evidence, and checks. Training data needs to expose those choices, especially when instructions are incomplete or several answers look plausible.
Give expertise a form models can learn from.
Our focus is the work between a request and a reliable outcome: interpreting requirements, using tools, resolving uncertainty, and checking whether the result holds up.
Our approach
Build the learning signal around the task.
Silo Data brings task design, domain knowledge, and data engineering into one workflow. Each engagement starts with a capability to develop and a clear way to assess it.
Four ways to make expertise useful to a model.
Feedback with a clear standard
Rubrics, preferences & rewards
Grading criteria and comparison data make quality assessable. Reward design starts with the behavior the task actually requires.
Environments for useful action
Tools, APIs & agents
Structured tasks connect instructions to tools and observable outcomes, including the decisions needed when a workflow does not go to plan.
Demonstrations across software
Computer & browser use
Sequences of actions, interface states, and completion checks show how a task unfolds inside the software people use.
Perspectives
Our thinking on the data, judgments, and learning environments behind capable AI.
Start with the task, not the dataset.
A precise capability target gives every example a reason to exist.
Judgment needs a standard.
A rubric should make disagreement informative and quality inspectable.
An agent task needs a visible outcome.
Actions become useful training material when their consequences can be checked.
Careers
We welcome people who care about data quality, technical depth, and the details of skilled work. Introduce yourself and tell us what you would like to build.