/Pricing
Open source at the core. Optional consultation and implementation support.
Recommended
Early Adopter ProgramApply now via the contact form.
Note: This is joint R&D. We're validating ZeroProofML in new domains and subsidizing early adopters accordingly. You get affordable expert help; we get real-world validation.
A quick primer on ZeroProofML’s approach and what to expect.
No. Where your function is well‑defined, we leave it alone. We only add safe behavior where standard math breaks so training can continue.
Slightly, due to safety checks. But you avoid failed runs and retries—so you typically finish faster overall.
Every operation returns a meaningful value: REAL numbers, ±∞ when appropriate, and a special Φ (‘nullity’) for undefined‑but‑handled cases—no NaNs or crashes.
Fewer crashes and restarts during training, lower error where it used to spike near cutoffs/resonances, and reproducible results when you set a seed.
Install, swap in a rational layer and safe normalization for brittle spots, then run a quick demo or your own pipeline with determinism enabled.
Email hello@zeroproofml.com for general questions or dome@zeroproofml.com for collaborations and implementation support.