Multi-agent constraint satisfaction framework that reveals hidden conflicts, interaction effects, and strategic insights that scalar scoring methods miss.
Most decision frameworks hide the complexity that matters
A decision might score "82/100" overall while having catastrophic consent violations (20/100) averaged with technical benefits (95/100). The aggregate masks the hazard.
89% of multi-criteria decision methods assume criteria independence. They can't model how improving fairness may reduce accuracy, or how privacy limits utility.
Different reviewers emphasize different values: technical feasibility vs user autonomy vs institutional risk. No systematic way to surface these conflicts.
Ethics checklists provide principles but lack enforcement mechanisms. Compliance becomes checkbox exercise rather than substantive review.
From problem to structured insights in 15 minutes
Submit your complex decision problem with stakeholder perspectives, key domains, and ethical criteria.
6+ specialized AI agents independently analyze your decision from different perspectives using constraint satisfaction.
System identifies where agents disagree, revealing genuine value conflicts requiring human judgment.
Receive detailed analysis table showing all perspectives, interactions, and strategic implications.
Addressing documented gaps in multi-criteria decision analysis
Addresses the 89% gap: most MCDA methods can't model how criteria interact. Zx3 explicitly models synergies, redundancies, and non-linear effects.
Disagreement between independent agents is signal, not noise; it reveals genuine value conflicts requiring explicit adjudication.
Every assessment is inspectable. No black-box scoring. See exactly how each agent reached its conclusions.
See how Zx3 reveals hidden structure in complex decisions
Analysis across Stakeholder × Domain × Criteria dimensions
[-1] Safety testing insufficient for edge cases affecting vulnerable users...
[+1] Clear opt-in mechanisms with transparent data usage...
[0] Partial rollback capability exists but requires manual intervention...
⚠️ Conflict Detected: Safety agent flagged [−1] on user protection while Economy agent flagged [+1] on business value. This tension requires explicit human adjudication before deployment.
Professional decision analysis at a fraction of traditional consulting costs
Quick assessment
Comprehensive analysis
Full strategic analysis
Get structured insights that reveal what traditional methods miss
Start Your AnalysisAny complex decision with multiple stakeholders, interacting criteria, and genuine tradeoffs. Common use cases: AI deployment decisions, policy interventions, organizational strategy, research directions, ethical dilemmas, resource allocation.
Most MCDA methods (89%) assume criteria independence and use additive scoring. Zx3 explicitly models criteria interactions, uses multi-agent verification to surface conflicts, and preserves multi-dimensional complexity instead of aggregating to single scores.
Decision-makers facing genuinely complex choices: AI labs evaluating deployments, organizations considering policy changes, researchers navigating ethical tradeoffs, teams resolving strategic disagreements, anyone needing structured analysis beyond "pros and cons lists."
15-30 minutes depending on tier. You submit your decision, pay, and receive detailed analysis via email when complete. The multi-agent assessment runs automatically.
Yes. Decisions are analyzed confidentially and stored only for delivering your results. We collect anonymized metadata for research validation but never share identifiable decision content.
We offer satisfaction guarantee. If the system fails to deliver useful analysis, contact us for a full refund. Our goal is providing genuine value, not processing payments.
Yes! The framework is open source (MIT license). You can implement it internally using our published methodology. Contact us for enterprise consulting, training, and custom deployment support.