Our Commitment to Transparency & Rigor

Methodology

Every assessment is powered by a fixed scoring framework, measuring the same seven dimensions for every individual. No matter when you take the quiz, the logic remains consistent: your answers are scored against pre-registered scales. The result is a stable, reproducible baseline — not a diagnostic, but a reflection of your natural decision tendencies.

For reports that include optional birth information, the analysis is a pattern-classification framework informed by birth-timing data. To be precise about what that means: birth data acts as one additional input variable to the classification model alongside your questionnaire answers — it does not determine, overrule, or outweigh what you tell us. You can decline to provide it entirely and still receive a complete questionnaire-based report. What the framework never does: predict future events, assign fate or fortune, or claim scientific proof. It classifies patterns and suggests actions — you decide.

Data & accuracy disclosure

We publish our internal validation numbers with their full context, because a bare percentage without a protocol is marketing, not transparency.

  1. The number. In our most recent internal review cycle, the classification engine agreed with independent case re-analysis in 149 of 180 cases (82.8%). This is an internal consistency rate against our case-library re-review — not a prediction accuracy, and not a measure of how satisfied you will be.
  2. The protocol. The 180 cases come from our internal case library (historical decision scenarios with documented outcomes, re-labeled by analysts blind to the engine's output). Agreement means the engine's classification matched the re-analysis label.
  3. The 31 failures. Roughly 1 in 6 cases disagreed. We publish this deliberately: pattern classification has real error rates — which is why every report marks conclusions with traceable references, and why the refund promise below exists.
  4. The known limitation — and what we're doing about it. Our classification framework was primarily developed and validated on East-Asian decision scenarios. We are actively extending cross-cultural validation: every report now carries built-in feedback capture, early-adopter reports feed directly into the next validation cycle, and we will publish what we learn — including corrections. Early adopters are partners in that process, and their pricing reflects it (see Early Adopter terms below).

Want to see the real thing before paying? Read an illustrative full report (fictional profile, real structure) — including how every conclusion is traced to its inputs.

Refund promise (7 days, defined)

Paid reports carry this commitment: if any single conclusion in your report cannot be traced back to your inputs (your questionnaire answers, or your optional birth data) or to the methods published on this page, you get a full refund within 7 days — no form-fighting. Operationally: reply to your delivery email pointing at the conclusion; within 48 hours we either show you the exact input-and-rule it came from, or we refund in full. If we cannot show the trace, you do not have to argue — the refund is automatic. During our launch phase we would rather return your money than defend a claim we cannot ground.

Transparency

We are building a reputation based on the quality of our insights, not on aggregated user testimonials – yet. The samples you may see on this site are illustrative patterns, clearly labeled, not quotes from real users. When we publish real feedback, it will be verifiable and attributed.

Why we ask for birth information (optional)

  1. A fourth classification dimension. The classification framework works on questionnaire answers alone — that is the default mode. Adding birth information unlocks a fourth input dimension (a time-coordinate axis alongside the questionnaire axes), which lets the framework distinguish patterns that questionnaire answers alone cannot separate. The framework's rules are built on classical case records with complete four-pillar birth data (year, month, day, hour), and validated on a 220-case library, all with full birth-timing recorded (two of which carry variant hour readings from the original sources, recorded as-is). The full audit results — and their implications for rule confidence — are published openly in this review.
  2. Statistical model enhancement. Birth date and, optionally, birth time are treated as a weak-correlation cohort enhancement: aggregated, anonymized group-level patterns that help calibrate how results are described. It is never a required input for your label or your free map.
  3. Strictly statistical, never predictive. We do not generate astrological, deterministic, or fortune-telling outputs from birth data. No individual prediction is made — the tool is a mirror of how you answer, not a forecast of what will happen.
  4. Minimal and purpose-bound. We store only what you choose to provide, tied to the email address used for report delivery, and use it solely for the statistical purpose above. We never sell it and never share it with third parties for their own use.
  5. Deletion anytime. You can remove your stored birth information at any point through an automated process — no questions asked, no delay beyond a quick verification step.

Deletion & privacy controls

If you entered birth information and would like it removed from our systems, visit the deletion page and submit the email address you used. Your birth data is erased immediately after verification, and you receive a confirmation email. You can also reach us at [email protected].

Methodology review status

Where our rules stand, stated plainly:

  1. Rule base. The classification rules derive from a documented internal case library of decision scenarios (the 180-case validation set above). A completeness audit of that library is published: 220/220 cases carry full four-pillar birth data (two with variant hour readings from original sources, recorded as-is).
  2. Review channel. Rule changes pass an internal adversarial review (shadow-testing against the case library before any rule is released; failed changes are discarded and logged). Until an independent external review is completed, our reports should be treated as structured self-reflection — not as predictive or prescriptive conclusions.
  3. Change log. Rule corrections driven by user feedback are named (anonymized) in each published update, so you can see what changed and why.
  4. Full methodology is available on request at [email protected]; a public methodology document is in preparation for this page.

Early Adopter terms

Because cross-cultural validation is in progress, reports sold in this launch phase carry Early Adopter status — a two-way commitment, not fine print:

  1. You get launch pricing. Report prices during this phase are locked in for you: when validation extends and prices rise, early adopters keep their rate on future reports.
  2. You get upgrade rights. When the next validation cycle publishes (including cross-cultural calibration), early adopters receive the updated report for their profile at no charge.
  3. You get a direct line. Every report's feedback taps are reviewed in the next cycle — and where your feedback changes a rule, the change log names it (anonymized). Price lock and free upgrade apply to the same type of individual report, for the first published validation cycle only.
  4. One dependency to know: if you delete your birth information, future upgraded reports can only be generated in questionnaire-only mode (the birth-timing dimension drops out). Nothing else changes — but if you want the full upgraded report later, you'd need to re-enter that data.

In exchange, we ask for honest feedback via the report's built-in taps. That is the deal: you are not paying to be tested — you are being paid in pricing and upgrades to help us validate faster.

Our rigor is not just a feature — it is our reputation. We will never exaggerate claims.