Lightning Rod SDK turns real-world data — like news, filings, or your own documents — into verified, production-ready training datasets in hours using just a few lines of Python. Skip manual labeling and synthetic guesswork.
Hi Product Hunt! Ben here, founder of Lightning Rod.
We started Lightning Rod because training data is the blocker for most AI projects. Companies have a huge amount of valuable historical data and access to rich public sources, but turning it into something AI can actually learn from is too slow and expensive.
Today we’re launching our training data SDK, which lets you automatically generate LLM-ready training data from raw documents or public sources. We use real-world sources and outcomes over time as supervision — no labeling or annotation required ⚡
Here’s what you get:
Go from idea to dataset, fast. Define your criteria and data source. We collect and label training data for you — ready in minutes, from just a few queries or examples.
Use your own data or start from public data sources. Generate training data from internal documents like emails, tickets, and logs, or from integrated public data sources.
Provenance in every row. Every record links back to its source, so you can audit what went into your model.
Quality built in. Automated scoring and filtering remove low-confidence examples and outputs that do not follow your instructions.
Turn historical data into training signal. We use real-world outcomes over time to convert your timestamped docs, tickets, logs, and news into grounded supervision automatically.
Create your first dataset free at lightningrod.ai. Use code ProductHunt50 for $50 in free credits.
Thanks for checking us out — I’ll be here all day reading and replying. If there’s a dataset or model you’ve wanted to build, drop it in the comments and we’ll help you get started!
What ways could I validate that the training data is actually improving downstream model performance?
We're doing some ML work on our side for matching and recommendations so this is relevant. Can the SDK work with proprietary data like internal user behavior logs, or is it mainly designed around public sources for now?
Very interesting concept. Getting training data for my AI Project 8 years ago for my capstone was a huge bottleneck. Using data that already exists and vetted to some degree democratizes training and building. I'm excited to give this a test!
Congrats team! Question: How do you ensure the generated datasets are actually suitable for fine tuning, given the noise, bias, and duplication often present in public news sources? Do you apply any validation, deduplication, or labeling quality checks, and can users control how the data is structured or filtered for specific domains or tasks?
Congrats on the launch! Very relevant problem - everyone talks about models, but high-quality training data is still the real bottleneck. Love the emphasis on provenance and production-ready datasets. Strong positioning. Wishing you a great launch today 🙌
Generate training data? What does it mean? Congrats on the launch, @bturtel!
How does the quality scoring work... Is it model-based or rule-based filtering?
Using real-world outcomes over time as automatic supervision instead of requiring manual labeling is a fundamentally different approach to training data generation — it means the dataset quality improves with historical depth rather than human annotation effort, which should scale much better for domain-specific fine-tuning. The claim of beating frontier models 100x larger with data generated through this platform is compelling; for teams working with internal documents like support tickets or emails, how does Lightning Rod handle PII in the source material — is there automated redaction before training data generation, or does that fall on the user?
Hi Product Hunt! Ben here, founder of Lightning Rod.
We started Lightning Rod because training data is the blocker for most AI projects. Companies have a huge amount of valuable historical data and access to rich public sources, but turning it into something AI can actually learn from is too slow and expensive.
Today we’re launching our training data SDK, which lets you automatically generate LLM-ready training data from raw documents or public sources. We use real-world sources and outcomes over time as supervision — no labeling or annotation required ⚡
Here’s what you get:
Go from idea to dataset, fast. Define your criteria and data source. We collect and label training data for you — ready in minutes, from just a few queries or examples.
Use your own data or start from public data sources. Generate training data from internal documents like emails, tickets, and logs, or from integrated public data sources.
Provenance in every row. Every record links back to its source, so you can audit what went into your model.
Quality built in. Automated scoring and filtering remove low-confidence examples and outputs that do not follow your instructions.
Turn historical data into training signal. We use real-world outcomes over time to convert your timestamped docs, tickets, logs, and news into grounded supervision automatically.
We’ve already used data generated with this platform to beat frontier models 100x larger, and to train domain expert models on everything from corporate risk to sports predictions.
Create your first dataset free at lightningrod.ai. Use code ProductHunt50 for $50 in free credits.
Thanks for checking us out — I’ll be here all day reading and replying. If there’s a dataset or model you’ve wanted to build, drop it in the comments and we’ll help you get started!