Trained on data you already have
Lightning Rod turns messy real-world data into compact expert models that predict outcomes, reason about cause and effect, and outperform frontier AI.
No manual labeling required.
Train compact models that know your domain and beat frontier AI on your use case.
Run smaller models at a fraction of frontier-model cost.
Deploy in your cloud or ours, with models trained for your data and workflows.
Lightning Rod trains on real-world outcomes found in messy real-world data.
Our models have beaten frontier systems on live forecasting benchmarks, domain prediction tasks, and peer-reviewed research.
Foresight-v3 is #1 in Sports and Politics, and the only model with a positive edge in both.
Outperformed Gemini 3 Pro, Claude Sonnet 4.5, and o3 on the Forecasting Research Institute benchmark.
Beating frontier models using our novel Future-as-Label methodology.
Use our SDK to generate datasets, train and evaluate models, and automate workflows with custom AI models.
from lightningrod import Pipeline pipeline = Pipeline([ NewsSeedGenerator(query="AI regulation"), ForwardLookingQuestionGenerator( instructions="Generate questions about future AI regulations and rulings" ), WebSearchLabeler() ]) dataset = pipeline.run(n_samples=100)
We got back 10,000 high-quality, citable QA pairs in hours — we were fine-tuning the next day.
Lightning Rod is the only solution that turns messy sources into high-quality, verified training data.
Thousands of high-confidence Q&A pairs in an incredibly short time — something that would have taken our team weeks manually.
We went from idea to deployment in a single sprint. Without this, we would have been stuck in a proof-of-concept loop for months.
10,000 labeled examples that we immediately put to work in our eval pipeline, teleporting us weeks ahead.
Incredibly easy way to generate high-quality datasets from public sources.
Lightning Rod enterprise paltform generates labeled forecasting datasets from domain-specific sources, fine-tune custom forecasting models, evaluate them, and serve them through the same APIs.
HuggingFace Examples