Rigor is the copilot that doesn't. You call the method first — then it checks every assumption in code, refuses the tests your data can't support, and computes every figure on a validated engine, never the language model. So the statistics behind your certification, your project, or your next decision actually hold up.
A Black Belt asked ChatGPT for a one-way ANOVA and got a clean, confident answer: p = 0.03 — significant, buy the equipment. The validated software said p = 0.21 — not significant. The AI hadn't calculated anything. It predicted what a plausible answer looks like.
This is the failure Rigor removes by design: it commits you to a method first, checks the assumptions in code, and computes every figure on the kernel — so the number you hand an examiner is one you can defend, not one that merely sounds right.
Type a real request — or tap one below. Vera commits to the method she'd defend, runs it on a sample dataset — chart and computed result — and refuses the ones the data can't support. Every figure comes off the kernel, never the model.
Six gates between your raw data and a claim you can defend. The one in red is the one general AI skips.
No tool menus, no 40-hour learning curve. One project, one methodology brain, and an interface that makes you better instead of dependent.
A short scenario assessment — five real situations — measures how you think, not what you've memorized. Rigor configures itself — coaching depth, mode, emphasis — and your answers become your measured baseline.
Before any analysis runs, you commit to your method call — then Rigor reveals its selection, the checked assumptions, and the why. Correct calls build your evidence log; misses become coaching.
Your DMAIC storyboard assembles itself as you work — charts to SPC convention, every figure provenance-stamped. Export, then have the independent examiner verify it.
Calibration starts with a short skills assessment — and the gaps it finds become your coaching emphasis, automatically. From there, every method call you make updates a live mastery estimate per skill, tracked down the side of your workbench as you complete real work.
Answer-vending AI tutors made students score 17% worse on the unassisted exam. Rigor is built to move that number the other way — and to show you it moving.
Minitab's AI summarizes results after you've already chosen the analysis. ChatGPT will compute capability on an unstable process without blinking. Rigor is built the other way around.
Every statistic runs on a deterministic kernel validated against NIST and Minitab reference outputs. LLM output physically cannot contain a computed number — a post-processor enforces it.
Method selection runs through methodology playbooks and a DMAIC state machine. Preconditions are checked in code — an analysis whose assumptions fail doesn't run.
Every figure carries its run ID, dataset hash, kernel version, and your decision trail. Hand it to a grader, a boss, or an auditor — "how did you get this?" is answered on the page.
Your project is one causal argument — Y=f(x), from problem to proof. Rigor keeps the thread: solutions must trace to confirmed causes, "improved" is a refused claim until post-change data proves it, and controls attach to the causes you actually verified.
"Run capability on this data." Your process shows a special cause on July 11 and fails the stability check. Capability on an unstable process is meaningless — let's chart it, find the cause, and then measure capability. That refusal is the product.
Employers know certificates can be bought. So we built the thing they can check: an independent adversarial examiner re-computes every claim in your storyboard from your raw data — then issues a tamper-evident record any recruiter can verify in one click.
The ladder steps down as we grow — earlier members get more, forever. We give away time and locked prices, never the credential: verification always costs something, because a registry only matters if its stamp can't be handed out. And the counters are real — a company named Rigor doesn't do fake scarcity.
Rigor isn't open to the public yet — we're onboarding the Founding 100 first. Leave your details and your spot is held, price locked, no card. Loading the live count…
Your exam costs $295–438. Your prep course cost hundreds more. Rigor is the cheapest part of your certification — and the only part that produces checkable evidence.
Time our tools did the same. Five pilot spots open this month.
Join the founding waitlistWelcome — before anything else, a short skills assessment. Five real situations, not trivia. Two things happen with every answer: your skill profile forms live (watch the panel on the right), and the gaps we find become my coaching emphasis — where I slow down, where I probe, where the predict-first loop works hardest.
After each answer I'll also ask how sure you were. Knowing what you don't know is half of exam readiness — and I coach overconfidence differently than honest uncertainty.