Lightning Rod Labs

Train with Timestamps, Not Labels.

Lightning Rod Labs automatically generates high-quality training data from your documents or public sources — no labeling or extraction required. Define your criteria in Python, and our SDK treats real-world outcomes as the label, producing high-signal supervision at scale. Models learn causal factors, not just tokens. Raw data to deployable specialized models in hours.

Website · SDK · Blog

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How It Works

We generate grounded, model-ready training data from documents or public sources (Google News, SEC filings, market data). You define your criteria in Python, and our SDK uses the future as the label — turning messy, timestamped history into training signal automatically. No labeling pipelines, no extraction, no human annotation.

This approach has been used to beat frontier AIs 100x larger on prediction-market benchmarks, and has demonstrated success in financial forecasting, risk estimation, and policy prediction.

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Research & Results

• SEC Risk Prediction: Foresight learning on raw SEC filings trains a 32B model to outperform GPT-5 at predicting public company risks.
• Future-as-Label: AI learns directly from raw chronological news data at unlimited scale, no human annotation.
• Outcome-based RL (TMLR): Using RL to improve LLM forecasting ability from real-world outcomes.
• Foresight-32B vs. Frontier LLMs: Live demonstration beating frontier models on Polymarket predictions.

Foresight-32B is consistently top-ranked on ForecastBench and ProphetArena, despite being 10x-100x smaller than frontier models.