A digital twin models your process. GREENBOX Certified Clone proves it. A new standard, backed by mathematics and first principles, that shows what actually happened and lets anyone verify it. Proof, not promises.
We make your process provable. You hand us the run · we hand back a mathematical proof that it met its mark, one any auditor can check without trusting us and without ever seeing your recipe. Data in, proof out.
A new standard for what actually happens inside your process, backed by mathematics and first principles, not a checklist. It cannot be faked and it never rests on your word, and the same proof holds in any domain you run, from a pharma batch to a defense system.
We turn your completed run into a sealed, audit-ready record with a proven bound. In hours, not days.
We run the model forward on your live process to catch drift before a run is lost.
We trace which input, which step, and whether the process would have failed anyway. Causal, not correlation.
Pruthvi PatelCo-Founder · Physical Systems
Lisa YangFounder & CEO
Dr. Abel KarimiCo-Founder · AI Systems
We build from first principles and rare mathematics, and we prove everything we claim. We do not ship what we cannot prove, and the same standard extends to any regulated process, one at a time.That discipline is the product.
A fast, accurate model of your process that ships with a proven, adversarially-tested error bound and a sealed proof anyone can check offline. A digital twin predicts. A Certified Digital Clone proves what it predicts, and flags itself the moment your process leaves the range we certified.
It is neither AI nor a black box. It is deterministic and grounded in the settled science of your process, which is why the error can be bounded and why it holds up as evidence for your auditors. Everyone else hands you a prediction and asks you to trust it. We hand you a proof you check yourself, offline, without us. Others model your process; we certify it.
Accuracy here is a number you can check, not an average that hides the bad days. For any prediction we give a worst-case bound that has been adversarially tested: the most it can be off, within the range we certify. It stays tight for near-term calls and widens honestly the further ahead you look. The bound is built on the real noise and drift in your own instruments, and the moment your process leaves the range we checked, it flags that instead of guessing.
Real data. We have certified clones on real, measured data across several physical domains, not simulations, and each one holds its bound on data the model never saw. In some cases it held even on an entire unit it had never been shown. Your pilot is where we ground it on your data and prove the bound carries over to your own equipment.
You keep the proof: a certificate, the proof packet behind it (the evidence, tests, and bound), and a small offline verifier that re-checks the signature with no network and no contact with us. However your clone is set up, your data stays governed and private to you, and we keep no raw copy of it. We use it only to certify, then return or delete it with a deletion receipt. Standing it up takes weeks, and certifying each run after that takes hours, not the days a manual review costs.
Less than most people expect. A handful of historical runs with their outcomes is usually enough, and it does not need to be clean first. Reconciling messy sensors, gaps, and mismatched records is our job, not yours. Before we promise anything, we tell you plainly whether your data can support a bound, and exactly what to add if it cannot.
Every proof is scoped to your process as it is today. When it changes, we re-certify, on request, on a schedule, or when it drifts past the range we certified. The certificate's signature never expires; what lapses is its currency, and only we can refresh it.
It is designed to fit the standards you already answer to, including 21 CFR Part 11, GMP, ALCOA+, GDPR, CMMC, and NIST 800-171, and to extend to new ones as they emerge. The clone produces sealed, offline-verifiable evidence an auditor can check independently, which is exactly what these frameworks ask you to produce.
Reach out, and if there’s a fit, we’ll show you the proof on your own process.
