The Context Engineering Bottleneck
While Scale AI focuses on labeling general data for model builders, most enterprises don't want to train models: they want to use existing ones effectively. The real bottleneck isn't prompt engineering. It's context engineering. Your medical protocols, legal precedents, and technical processes are trapped in documents and people's heads, inaccessible to AI systems that could transform your operations.
Context engineering is becoming recognized as the primary bottleneck for effective AI deployment. As Andrej Karpathy predicted, context engineering represents the next major value unlock in AI systems. Foundation model companies are hungry for specialized training data that traditional labeling services cannot provide. Your production deployment occupies a unique position: neither traditional data labeling companies nor foundational model companies are solving the expertise structuring problem.
Our Approach
Our platform is home to your FDEs and Agent Product managers. It enables clean collaboration between your team and the domain experts to capture institutional knowledge, decision-making processes, and edge cases that define expertise. We transform scattered expertise into context-engineered datasets that AI systems can consume and understand at a deep level. This enables immediate use for superior AI responses while creating valuable datasets.
Trace Labeling as Data Labeling
Every trace your agents produce is an opportunity. We help you capture, annotate, and learn from real production behavior, turning operational data into continuous improvement cycles.