Generate training data from real-world sources
Go from messy historical data to verified training datasets — no labeling or annotation needed.
Real-world data has timestamps.
Not clean labels.
Turn historical data into verified training datasets automatically using Future-as-Label.
Trusted by teams building AI
"Super impressed by Lightning Rod. We thought data prep would take weeks. We handed them our internal docs and got back 10,000 high-quality, citable QA pairs in hours—we were fine-tuning the next day."
"10,000 labeled examples that we immediately put to work in our eval pipeline, teleporting us weeks ahead. The quality and thoroughness of the explanation made us highly confident to start using the data."
"Lightning Rod took a messy set of conversational transcripts and turned them into a complete training set ready for fine-tuning. The turnaround was fast enough that 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—instead, we got awesome results we could use on day one."
"We have an enormous amount of unstructured data about our portfolio companies, but it wasn't labeled or usable for training. Lightning Rod is the only solution that turns messy sources into high-quality, verified training data—unlocking real AI solutions to make smarter, better decisions."
"We had an excellent experience with Lightning Rod Labs. They delivered thousands of high-confidence Q&A pairs in an incredibly short amount of time—something that would have taken our team weeks to do manually. The cross-checking gave us strong confidence in the accuracy and reliability of the output. I highly recommend them to any team building AI!"
"We rapidly generated high-quality synthetic datasets to stress-test edge cases and policy variants that were difficult to source organically, significantly improving precision and recall in a fraction of the time."
"Incredibly easy way to generate high-quality datasets from public sources."
Turn messy data into
training-ready datasets
Choose Sources
Public web, news, filings—or your own docs, emails, tickets.
Define Questions
Natural language instructions + examples. No schema required.
Auto-Label
Outcomes from later in the data become ground-truth labels.
Verify
Every row traceable to sources. Full provenance built in.
Simple, powerful API
Generate verified datasets in a few lines of code. Our SDK handles the complexity.
- Grounded in real data, not synthetic generation
- Bootstrap with public feeds: news, SEC filings, Wikipedia
- Full provenance with citations and source docs
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)Each data point comes with evidence, citations, and confidence — not just a label.
- Ground-truth labels from real outcomes, not LLM opinions
- Full citations traceable to original sources
- Reasoning chain explaining how each answer was resolved
- Ready for fine-tuning — export as HuggingFace, Parquet, or JSON
{
"question": "Will the EU AI Act be enforced against a major tech company by Feb 2025?",
"correct_answer": 0,
"resolution_reasoning": "Prohibited practices provisions took effect Feb 2, 2025. No enforcement actions announced...",
"source_citations": [
"reuters.com/...",
"ec.europa.eu/..."
]
}The future is the label.
We pioneered Future-as-Label training: using temporal structure in historical data to generate supervision at scale. We used this to beat frontier AIs 100x larger on live prediction benchmarks.
