Train with timestamps.
Not labels.
Go from messy raw data to verified datasets in minutes.
What teams are saying
"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."
Unblock your AI
Your data has timestamps, not clean labels.
LightningRod turns messy history—docs, tickets, emails—into verified datasets automatically.
Go from raw data to deployable AI in hours, not months.
Why Lightning Rod?
Verified Datasets
Every row traceable to sources, with checks.
Zero Cold Start
Bootstrap from public feeds + your history.
Total Provenance
Source + timestamp lineage for each example.
Reproducible Pipelines
Versioned runs you can rerun exactly.
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)Better data, better models
Train specialized models you own and control.
Beat the giants
Specialized models outperform generic frontier LLMs
Run anywhere
On-prem, your VPC, or any cloud
Data stays private
No sending sensitive data to third-party APIs
Smaller & faster
Purpose-built models cost less to run
How it works
Pick Sources
News, filings, documents, APIs
Generate & Verify
AI generates, web search verifies
Export Dataset
Parquet, CSV, HuggingFace
Train Model
High-signal supervision at scale
Beat frontier AIs 100x larger.
We used this approach to beat frontier AIs 100x larger on live prediction benchmarks, and demonstrated success in everything from financial forecasting to supply chain disruptions.
Read the Research