Synthetic Has Limits
Synthetic examples can teach patterns, but they don’t fully capture the messy context, edge cases, and decisions found in real work.
Training Data That Comes From Real World
We work with businesses and independent experts to turn real tasks, verified AI failures, and expert corrections into training data for the next generation of models and agents.
The Challenge
AI needs to learn from the real world
Models can only become useful in real work when they learn from the complexity, context, and decisions that real work demands.
Real Work Is Complex
Real tasks come with context, constraints, tools, exceptions, and outcomes that are difficult to reproduce outside the real world.
Quality Needs Humans
The best training examples come from real work reviewed by people who understand what a correct outcome actually looks like.
How It Works
From AI failures to useful data
Each real task starts with a model attempt. We verify where it falls short, capture how an expert solves it, and select useful examples for training and evaluation.
Start with a controlled attempt at a real business task. Record the model version, context, available tools, and attempted execution.
Grade and re-test the result. Separate genuine capability failures from missing context, unclear instructions, and tool errors.
An expert completes the work. Capture their actions, revisions, and validated outcome so the example shows how the task was solved.
Combine the reproducible model failure, successful expert execution, and grading criteria. Only select work with permission and a relevant use for AI teams.
Successful model attempts still deliver value to the business. Only verified gaps enter the failure dataset.
Book a CallPowerHouse / WorkflowIllustrative example
01 / Model attempt
Controlled model attempt
Real customer requestTask context and requirements
Model setupVersion and available tools recorded
Attempted executionActions and initial result captured
Evidence to review and reproduce
Start with a real task.
Keep the context, tools, and model attempt together.
The Expert Marketplace
Real work powers the data
Businesses get work done. Independent experts bring the skills to deliver it. Our marketplace connects both, supplying real tasks and the expertise that makes the data useful.
Real business workflows flow into expert human review, then into structured training data. New tasks continuously feed the pipeline.
- 01Source
Real Tasks
Businesses access expertise for the work they need done. Ongoing engagements bring in real context, tools, constraints, and outcomes.
- 02Refine
Expert-Led Outcomes
Independent experts use their judgment to correct AI failures and deliver the work. Their actions and revisions become candidate training material.
- 03Structure
Selected, Usable Data
Completed work becomes a data product only when it meets the agreed rights, quality, and relevance requirements.
A continuous source of work. A selective process for data.
Human Judgment
AI learns better from real expertise
Our data is grounded in real work and reviewed by people who understand the task, the context, and what a successful outcome looks like.
Expertise
Real practitioners bring the judgment and domain knowledge needed to correct AI failures and take responsibility for a successful outcome.
Evaluation
Clear grading criteria and held-out tasks help test whether models improve beyond the examples used in training.
Structure
Verified model failures are paired with expert actions, corrections, and successful outcomes, giving AI teams structured examples to learn from.
For AI Teams
Useful data, in the right format
Selected work can become training examples, evaluation sets, or repeatable environments. The format depends on the capability gap and the scope agreed with your team.
Training examples
Verified model failures paired with expert actions, corrections, and successful results.
Train on the capability gap.
Evaluation sets
Separate, held-out tasks with clear grading criteria and reference outcomes.
Test beyond the training examples.
Repeatable environments
Resettable workspaces with tasks, tool access, and reliable feedback for models to practice in.
Scoped separately, with additional engineering.
Start With a Pilot
Train AI on real work
Let’s define the target task, data rights, and quality criteria.
A scoped pilot can test usefulness and, where feasible, measure improvement on held-out tasks.