Generate verified datasets at scale.
Quality data is the biggest blocker for most LLM projects. LightningRod makes it easy to generate, transform, and verify datasets grounded in real sources—in just a few lines of Python.
import lightningrod as lr
# Get antitrust news to train a domain expert
seeds = lr.NewsSeedGenerator(
query="antitrust investigation",
start_date="2025-01-01"
)
# Define the scope and style of the questions
questioner = lr.QuestionGenerator(
instructions="Write forward-looking, self-contained questions with explicit dates/entities.",
examples=[
"What is the likely outcome of the DOJ lawsuit?",
"Which specific Sherman Act violations are cited?"
]
)
# Verify answers against live sources
labeler = lr.WebSearchLabeler()
# Run pipeline
pipeline = lr.Pipeline(seeds, questioner, labeler)
dataset = pipeline.batch(100)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."
"We needed a complex dataset to help us test a hypothesis for a new advisory tool. LightningRod quickly understood our needs and gave us a quality dataset that allowed us to move forward."
Why Lightning Rod?
Grounded, Not Hallucinated
Every answer is verified with evidence and confidence grading. No more synthetic data of questionable quality.
No Data? No Problem
Bootstrap datasets from scratch using public news feeds, filings, and web sources. Start with zero labeled data.
Total Provenance
Every data point includes citations and source documentation. Know exactly where your training data comes from.
Reproducible Pipelines
Treat data as code. Track lineage, version datasets, and reproduce results with deterministic pipelines.