Stop training your models on LLM hallucinations. Meet Alrithm a Verified Reasoning Data API for AI.

Hey everyone 👋
The AI industry is hitting a wall: we’re running out of high quality human data. The default answer has been synthetic data using LLMs to generate training data for other LLMs. But probabilistic generation breeds hallucinations and accelerates model collapse.

That’s why I built Alrithm.
We remove the noise so you can train on mathematical truth. Instead of asking an LLM to "guess" an answer, the ALR Engine writes and executes real Python code, running differential fuzzers across thousands of edge cases to deterministically prove every single row before it’s ever generated.

What makes Alrithm different under the hood:

• 100% Deterministic Ground Truth: If a generated script fails a single fuzzer case, the row is destroyed. Every row comes with SHA-256 provenance.
• Extreme Mathematical Rigor: Our latest ALR Aerospace 0.2 release features 60-digit interval enclosures across closed-form orbital mechanics problems and <10⁻¹¹ integrator agreement across RK4, DOP853, and Radau ODE solvers.
• Zero Train/Test Leakage: Strict deduplication with zero overlap in sample_id, instance_key, or provenance_hash across splits—so your benchmarks actually mean something.
• Plug-and-Play Streaming: No more wasting compute scraping and cleaning messy datasets. Point your training pipeline at a single URL and pipe pre-formatted, execution-verified NDJSON directly into PyTorch or HuggingFace.

Whether you’re training reasoning models, coding agents (check out our Code Platform 0.2), or Scientific ML / aerospace simulations, we provide the verified data backbone.

Would love to hear your thoughts, feedback, or what scientific/reasoning domains you'd like to see added next!