Real Results, Verified Outcomes

How leading organizations use MIZ OKI 3.5 to transform decision-making with measurable, auditable results

63×
Average decision velocity improvement
↓35%
Revenue leakage reduction
3.2mo
Average payback period
89%
Automation coverage achieved

Featured Case Studies

Across industries, MIZ OKI 3.5 delivers verifiable autonomous decision intelligence

Media Buying / Performance Marketing

Breaking Through the Learning Limited Wall

Fortune 500 DTC Brand — $42M Annual Ad Spend

A major DTC brand was experiencing chronic "Learning Limited" status across 80% of their Meta ad sets, causing volatile ROAS and unpredictable scaling. MIZ OKI's ReLU-aware optimization identified systematic threshold failures and restructured their entire campaign architecture.

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+47% ROAS improvement within 90 days
-68% Ad sets in Learning Limited status
12→3 Days to stable performance after launch

We went from constantly firefighting volatile campaigns to having the system automatically detect threshold issues before they impacted performance. The Counterfactual Simulation Engine showed us what we were leaving on the table.

VP of Growth — Fortune 500 DTC Brand

Financial Services / Risk Management

From Quarterly Reviews to Real-Time Risk Decisions

Regional Bank — $8.2B AUM

A regional bank's risk committee met quarterly to review portfolio allocations, missing market signals and reacting to crises after the fact. MIZ OKI's Decision Control Plane now autonomously rebalances within policy constraints while maintaining full audit trails for regulators.

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74× Faster risk signal response time
+18% Risk-adjusted returns vs. benchmark
100% Regulatory audit compliance

The Validation Layer gave our compliance team confidence that autonomous decisions weren't black boxes. Every rebalancing decision comes with causal reasoning and counterfactual analysis that satisfies our regulators.

Chief Risk Officer — Regional Bank

Healthcare / Revenue Cycle Management

Eliminating Revenue Leakage in Claims Processing

Multi-Hospital Health System — 12 Facilities

A health system was losing $4.2M annually to coding errors, missed authorizations, and claim denials. MIZ OKI's causal GraphRAG identified root causes of denials and autonomously routes claims through optimal pathways while maintaining HIPAA compliance.

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-41% Claim denial rate reduction
$3.1M Annual revenue recovered
-62% Time to first-pass resolution

The system identified patterns we'd been blind to for years—specific procedure codes that consistently triggered denials from certain payers. Now it proactively routes those claims differently before submission.

Director of Revenue Cycle — Multi-Hospital Health System

Retail / Inventory Optimization

Autonomous Inventory Decisions That Don't Break the Supply Chain

National Specialty Retailer — 340 Locations

A specialty retailer's inventory team was overwhelmed by 12,000+ SKUs across 340 locations. Previous AI attempts created cascading stockouts. MIZ OKI's Counterfactual Simulation Engine tests every reorder decision against supply chain constraints before execution.

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-28% Inventory carrying costs
+15% Same-store sales from availability
-73% Stockout incidents

Previous systems would optimize one metric and destroy another. MIZ OKI runs multi-objective optimization that balances availability, carrying costs, and supplier relationships simultaneously. The simulation engine caught issues before they hit our stores.

SVP of Supply Chain — National Specialty Retailer

Manufacturing / Predictive Maintenance

From Reactive Repairs to Verified Autonomous Maintenance

Industrial Equipment Manufacturer — 4 Plants

An equipment manufacturer's maintenance costs were consuming 23% of operating budget. Existing predictive maintenance tools generated too many false positives. MIZ OKI's Validation Layer validates predictions with multiple agent perspectives before scheduling interventions.

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-52% Unplanned downtime
-34% Maintenance costs
-81% False positive maintenance alerts

The Validation Layer was the breakthrough. Instead of one model crying wolf, we have Planner, Risk, and Verifier agents that must agree before scheduling maintenance. False positives dropped from 40% to under 8%.

VP of Operations — Industrial Equipment Manufacturer

Deep Dive: Media Buying Transformation

How the ReLU-aware approach changed everything for a $42M ad operation

The Challenge

  • 80% of ad sets stuck in Learning Limited status
  • ROAS volatility of ±40% week-over-week
  • 15+ campaigns with fragmented budgets below activation thresholds
  • No institutional memory of what worked in previous launches
  • Daily manual adjustments resetting learning progress

The MIZ OKI Approach

  • SENSE-ADC detected threshold proximity across all ad sets
  • Causal GraphRAG identified true performance drivers vs. noise
  • Counterfactual Simulation tested consolidation scenarios
  • Decision Control Plane enforced stability windows
  • TCO Knowledge Graph retained learnings across campaigns

Key Interventions

  • Consolidated 15 campaigns to 4 with concentrated budgets
  • Shifted optimization events from Purchase to Add-to-Cart for low-volume segments
  • Implemented 7-day stability windows preventing premature edits
  • Established Event Match Quality optimization via CAPI
  • Created threshold alerts before Learning Limited triggers

Results Timeline

  • Week 2: Learning Limited dropped from 80% to 45%
  • Week 4: ROAS volatility reduced to ±15%
  • Week 8: First profitable scaling of 25%+ budgets
  • Week 12: +47% ROAS vs. pre-implementation baseline
  • Ongoing: Autonomous threshold management with human oversight

Typical Implementation Timeline

From kickoff to measurable results in weeks, not months

Week 1

Discovery & Data Integration

Connect data sources, configure industry templates, establish baseline metrics

Week 2

Knowledge Graph Hydration

TCO-KG populated with historical data, causal relationships identified, initial models calibrated

Week 3

Autonomous Operations Begin

SRDPV-DAL pipeline active, first autonomous decisions with human oversight, validation layer engaged

Week 4

Performance Optimization

Counterfactual simulations refining decisions, confidence thresholds calibrated, initial results measured

Week 6-8

Scaled Autonomy

Expanded scope, reduced human oversight on proven patterns, continuous learning active

Week 12+

Full Production

Measurable ROI achieved, autonomy level optimized, ongoing refinement

Trusted by Industry Leaders

Organizations across sectors rely on MIZ OKI for verified autonomous decisions

[Financial Services]
[Healthcare]
[Retail]
[Manufacturing]
[Media]

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