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.
Real-world data for AI
Training Data That Comes From Real World
We work alongside real businesses and turn real-world work into high-quality training data for AI companies building the next generation of models and agents.
Workflow
Real-world task data
Domain
Rebecca Torres
Real-world reviewer
Examples
- Real customer workflowDone
- Expert-reviewed outcomeDone
- AI response evaluatedDone
- Grading criteria definedIn Progress
- Training example preparedNext
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 real work to training data
We work with businesses, participate in real workflows, and turn those experiences into structured, human-reviewed training data for AI companies.
1
Work With Real Businesses
We partner with businesses and help them generate and fulfill real work. That gives us a continuous source of real tasks, contexts, decisions, and outcomes.
2
Capture What Actually Happens
AI works through real tasks, while experienced humans review the results, correct mistakes, and establish what successful work looks like.
3
Turn Work Into Training Data
We structure the resulting work, context, expert decisions, and outcomes into high-quality training data that AI companies can use to improve their models and agents
A data pipeline built on real work
Our existing business relationships give us an ongoing source of real-world work that can become training data at scale.
Real Businesses
We don't manufacture scenarios. Our data comes from businesses actively doing real work across their day-to-day operations.
Continuous Supply
Because we're embedded in ongoing workflows, new tasks and outcomes continuously enter the pipeline instead of being created as one-off datasets.
Built for Quality
Human review, structured tasks, clear outcomes, and evaluation create training examples grounded in what actually works.
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 distinguish a correct result from one that only looks right.
Evaluation
Every example can be judged against clear criteria, making quality measurable rather than subjective.
Structure
We turn complex real-world work into structured examples that AI systems can learn from and improve against.
What You Get
Training data you can build on
Real-world context, expert judgment, and structured outcomes come together to create training data designed for models that need to work beyond the lab.
Real-world context
Clear outcomes
Expert-reviewed
Domain-specific
Structured examples
Built continuously
Our Guarantee
Train AI on real work
Real businesses. Real work. Human-reviewed outcomes. Training data grounded in how work actually happens.