Custom AI for your business
Trained on data you already have
We train expert models for your business using the messy operational data you already have.
No manual labeling required.
Trusted by enterprise, government, and startups
Trained on data you already have
We train expert models for your business using the messy operational data you already have.
No manual labeling required.
Train compact models that know your domain and beat frontier AI on your use case.
Run smaller models at a fraction of frontier-model cost.
Deploy in your cloud or ours, with models trained for your data and workflows.
Lightning Rod turns messy timestamped data into standardized RL training environments. We handle data preparation, training, evaluation, and deployment. Train specialized models to run your workflows or power your products—in your cloud or ours.
The history you already have
Real outcomes. No manual labeling.
Will this deal close this quarter?
Unlikely—I'd put it at 38%. Legal is stalled1, with no reply in three weeks2…
In your cloud or ours
With Future-as-Label, real-world outcomes provide the feedback models need to learn—no manual labeling required. Models learn which signals predict outcomes in your business.
Our platform trains compact models that outperform frontier AI—from forecasting world events to predicting business risks.
Foresight models have reached Superforecaster range on ForecastBench and 1st place on ProphetArena—all on a single GPU.
Our domain-expert models have outperformed frontier models across use cases, from clinical outcomes to supply chain.
Our Future-as-Label method was awarded one of 10 spotlights at ICML’s AI Forecasting Workshop.
We got back 10,000 high-quality, citable QA pairs in hours — we were fine-tuning the next day.
Lightning Rod is the only solution that turns messy sources into high-quality, verified training data.
Thousands of high-confidence Q&A pairs in an incredibly short time — something that would have taken our team weeks manually.
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.
10,000 labeled examples that we immediately put to work in our eval pipeline, teleporting us weeks ahead.
Incredibly easy way to generate high-quality datasets from public sources.
