Lightning Rod: Training Data Generator

Rohan Chaubey, Sviatoslav Dvoretskii, Neil Verma, Himani Sah, and the rest of the Lightning Rod team looked at one of the biggest bottlenecks in AI development—the painstaking process of labeling training data by hand—and said "we're done with this." They're building a system that turns raw documents and public sources into verified training sets without a single human labeler touching the data. The secret sauce? Real-world outcomes with timestamps become labels. Historical events that already happened become the ground truth. That's not annotation—it's retrieval.

They're showing examples across domains that make it clear this isn't a toy. Policy forecasting datasets with 5,000+ RL questions about geopolitical events. Medical QA sets drawn from actual textbooks. Supply chain disruption predictions using Federal Reserve data. Portfolio risk analysis from board presentations. Each one auto-labeled with real-world timestamps and outcomes, ready to fine-tune models on actual signal rather than expensive guesswork. Trusted by enterprise, government, and startups—though they kept specific numbers quiet.

The Lightning Rod Agent handles the heavy lifting: you describe what you want to predict, it scrubs sources, generates questions, and outputs verified training examples. No install required. No labeling army required. Just prompts and outcomes. For anyone building domain-specific AI models, this cuts the single most painful part of the workflow from weeks to minutes.

Based in the AI infrastructure space, Lightning Rod is looking for early adopters building expert models in finance, healthcare, policy, and supply chain. If your model needs training data that actually reflects reality instead of crowdsourced guesses, these are the people to talk to.