Training Data That Labels Itself

Go from messy historical data to verified training datasets — no labeling or annotation needed.

Real-world data has timestamps. Not clean labels.

Turn historical data into verified training datasets automatically using Future-as-Label.

Use built-in public sources News, SEC filings, web data
Or bring your own Docs, emails, tickets, transcripts

Turn messy data into training-ready datasets

Choose Sources

Public web, news, filings — or your own docs, emails, tickets.

Define Questions

Natural language instructions + examples. No schema required.

Auto-Label

Outcomes from later in the data become ground-truth labels.

Verify

Every row traceable to sources. Full provenance built in.

Simple, powerful API

Define your sources, time window, and question style. The SDK handles generation, labeling, and verification.

Bring your own files or use built-in public sources
Questions auto-generated from your domain context
Labels verified against real outcomes — no human annotation

from lightningrod import Pipeline

pipeline = Pipeline([\
    FileSetSeedGenerator(file_set_id="your-fileset-id"),\
    ForwardLookingQuestionGenerator(\
        instructions="Questions about business outcomes"\\
    ),\
    RAGLabeler()\
])

dataset = pipeline.run(n_samples=100)

AI you can trust for real decisions

Ground-truth labels from real outcomes, not LLM opinions
Verifiable every sample has citations and provenance
Auditable reasoning explains how each answer was resolved
Calibrated probabilities that reflect real uncertainty
Secure & efficient compact models that deploy on your infrastructure

{
  "question": "Will the EU AI Act be enforced against a major tech company by Feb 2025?",
  "correct_answer": 0,
  "resolution_reasoning": "Prohibited practices provisions took effect Feb 2, 2025. No enforcement actions announced...",
  "source_citations": [\
    "reuters.com/...",\
    "ec.europa.eu/..."\
  ]
}