Lightning Rod: AI Forecasting API
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Lightning Rod - Turn real-world data into training datasets fast

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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.

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Maker
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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 , and to train domain expert models on everything from to .

Create your first dataset free at . 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!

 the logo looks like the one of the Wallet of Satochi - please consider changing it! ? This might be copyright violation!

 Congrats on the launch Benjamin and team! Good hunt, :)

As a marketer, I’m thinking about using this for content datasets. Any examples you have seen in my niche?

Maker

Thanks - yes, content marketing is a very natural fit for us.

One strong use case is generating training data to predict which messages, hooks, claims, or creative variants are most likely to perform with a given audience. We’re currently working on a case study around predicting outcomes of content experiments.

Over time, that can mean generating large sets of message ideas, ranking the ones most likely to land, and helping teams iterate faster on what works.

We don’t have a public example yet, but we’re hoping to share results within the next month.

 Congratulations on the launch! What's one underrated data source (like support tickets or emails) you've seen unlock massive gains in custom LLM training for non-tech founders?

Maker

 Thanks! Any timestamped internal docs where you already know what happened next — quarterly reports, risk assessments, customer communications. Stuff companies have years of and never think of as training data.
The other really powerful source for domain-expert models is news. In most domains, forcing a model to learn to predict outcomes from news forces the model to really learn everything about that domain. So its a really fast and scalable way of training domain-expert AIs on the fly.

Maker

 our Future-as-Label method () makes it possible to train on really anything with timestamps! For this model we leaned heavily on world news, but absolutely there's plenty of untapped signal in internal records like emails / tickets / reports etc.
Lately we've been doing a ton with unstructured patient records (), and I think there's a TON of potential there

Any benchmarks you can share?

Maker

 Yes - we have a page with a handful of our wins and published research here:

Maker

 Hi Kumar, I will add that we did a test on an earlier model we trained with this data generation technique where we made live predictions for questions on polymarket with our model and a handful of much larger frontier models, wait about a month for most of the questions to resolve, and then see who did better - results here:

Congrats!! Any plans to a no-code interface for non-technical teams?

Maker

 Hi Himani, we do have a no-code interface in our dashboard: - you can either chat with an agent to set something up or manually configure a data generation pipeline in the UI. And we will definitely be expanding on that in the near future!

Maker

 Yes! We just launched our "Prompt to fine-tune" agent as well to help non-technical users build datasets and fine-tune models without any code. I'd love to hear what you think!

How does the quality scoring work... Is it model-based or rule-based filtering?

Maker

 We support a combination of both. Here is an example of LLM model based scoring:

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 🙌

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?

Maker

@davitausberlin good question!

We know the training data is high-quality because of the results we've achieved across a variety of benchmarks and domains. We often beat frontier LLMs much larger (10-100x) by using this to fine-tune small models. Not just evals we designed on our own questions, but often in independent leaderboards. You can see a few wins / proof points here:

On validation: Yes, we have a bunch of quality checks built in, and by default low-confidence answers get dropped automatically. All steps are configurable, and you can also attach LLM-scored filters at the seed and question level with your own rubrics to filter by:

Before training we also run deduplication and other configurable data preparation steps:

I'd love to hear your feedback if you give it a shot.

Maker

  Great question - We do have a configurable deduplication step in our pipeline before fine-tuning. On our larger training runs we have also generated samples from the GDELT project which is an aggregate database of "events" which are in a sense de-duplicated news articles, and we will select the top events over time to generate forward-looking training samples from. Our pipeline offers a seed generator that uses this same system, which is good for building or evaluating over general forecasting questions. If you are fine-tuning on a specific domain you can also generate seeds from specific news queries or sources.

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?

Maker

 That is a good point! Lightning Rod SDK fits easily into any kind of data processing pipeline so if you did want to redact PII before creating seeds you definitely could. In the Lightning Rod SDK though you can include instructions for how to turn the seed data into questions, and examples. That could include instructions and examples for how to mutate any PII or just what type of questions you want to generate from your data. Of course any data uploaded is secure and scoped to your organization. Let me know if you want to me to walk you through sometime how to configure that!

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?

Maker

 100% - we (unsurprisingly) see the strongest improvements over frontier models when training on proprietary internal data.

If you want to try the SDK, we have some example notebooks for this here

Also happy to meet and hear about your use case if we can help you get started!

Maker

 Hi Ben, we definitely support bringing your own data to transform it into training samples or augment it with additional context or labels. There are different ways to approach this. We have an example here for how to create a dataset from your own data (pdfs, csvs, etc) that can be processed further with our pipeline .

We also support as Gretchen mentioned creating custom "Filesets" which can be used to process those documents by chunking them, or by indexing in a RAG database and generating specific types of questions that way. This is how we trained our for example.

If you do want to do an experiment with custom data I'd definitely encourage finding time to chat more about your use case.

What ways could I validate that the training data is actually improving downstream model performance?

Maker

Good question!

The SDK has a built-in evaluation module so you can measure improvement over your base model directly on held-out test sets:

You can also run rollouts against frontier LLMs on the same questions and score everything against ground truth (Brier score, calibration error, etc.):

Examples of how we've done this in our notebooks () and research papers ().

Maker

 For this model, we've run validation against live forecasting questions, both from our own system and from prediction markets like Polymarket, and compared the results before / after training, as well as compared to top Frontier AIs. We also compete on 3rd party benchmarks like ForecastBench and ProphetArena.
If you're looking to train your own model – we have a whole eval suite in our SDK:

Very interesting! And if I have a source with outdated content, will your system be able to find and exclude all old data?

Maker

 Yes! We can filter out outdated data, or use time-aware training to learn what we can from the older data, while making sure the model is updated with the latest learnings.

Maker

 Foresight-v4 is a trained model, but our SDK sounds like it's what you're looking for – we make it really easy to take your messy unstructured data and turn it into high quality training data:

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