Generate training data from real world sources.
Instantly.
Go from messy historical data to verified datasets in minutes.
What teams are saying
"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 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."
"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."
"Incredibly easy way to generate high-quality datasets from public sources."
"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 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!"
"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."
Real-world data has timestamps.
Not clean labels.
We turn historical data into verified training datasets automatically using Future-as-Label.
Use our built-in public sources—news, SEC filings, web—or bring your own docs, emails, and tickets.
Go from zero data to deployable AI in hours, not months.
Simple, powerful API
Generate verified datasets in a few lines of code. Our SDK handles the complexity.
- Grounded in retrieved evidence, not synthetic slop
- Bootstrap with public feeds: news, SEC filings, Wikipedia
- Complete provenance with citations and source docs
from lightningrod import Pipeline
pipeline = Pipeline([
NewsSeedGenerator(query="AI regulation"),
QuestionGenerator(
instructions="Forward-looking questions with dates"
),
WebSearchLabeler()
])
dataset = pipeline.run(n_samples=100)How it works
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.
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.
