Decision Engine
—decisions · — modeled
Execution-ready decisions ranked by weighted ROI
Strategy | —
Run a module to generate your decision plan
Revenue Decisioning Engine
Execution-ready configurations
Diagnose  →  Decide  →  Configure  →  Deploy  →  Learn
Coverage: Payments · Recovery · Revenue · Cost
Waiting for decisions
Run any module — Identifies, prioritizes, and defines execution-ready revenue decisions across the payments lifecycle with measurable ROI.
Not a payments analytics tool — a capital allocation system for revenue. Replaces coordination overhead, not teams.
System of Record: PSPs / payment processors
System of Action: orchestration layer
This system: decisioning + configuration layer

No findings yet

Open a layer in Analysis Modules, enter your numbers, and your ranked action plan appears here.

Assumptions: Based on blended PSP fees, issuer mix, and industry benchmarks. Actual results vary by merchant configuration and market.
Open any layer → enter your numbers → findings feed the Revenue Action Engine automaticallyContinuously optimizes revenue using live payment data.
0
Setup · Enter once, used everywhere
Business Profile
Your numbers cascade as defaults into every module below.
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01
Revenue Capture
Who can pay — and whether they do
Payment method coverage · Checkout conversion · Attach rate · Market expansion
Can they pay?
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02
Payment Optimization
How reliably revenue is collected
Billthrough · Decline taxonomy · Retry logic · Issuer performance · Anomaly detection
Do they pay?
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03
Revenue Intelligence
What revenue is collected — and what it compounds to
Billthrough → LTV · Cohort retention · Passive churn modeling · Forecasting
What is collected?
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04
Payments Economics
What revenue costs to process
Cost of payments in bps · PSP benchmarking · Interchange · Routing efficiency · Stack maturity
What does it cost?
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05
Execution Feedback Loop
Decision → Execution Plan → Deployed → Measured. Every execution closes the loop and improves the model.
→

Execution Feedback Loop

Every decision becomes an execution record. Log what was implemented, track actual vs expected uplift, and feed variance back into model confidence. This turns analysis into a learning system.

Log an implemented action
Action taken
Expected revenue uplift ($)
Actual result ($)
Status
Measurement window
System context — Revenue Optimization Engine
Payments Revenue Operating System
Ownership model
Decision owner Payments / Product
Execution owner Engineering / Payments Ops
Measurement owner Finance
System role: central decision engine across teams — replaces coordination overhead, not people.
System vs team
Without
Decisions fragmented across teams
ROI unclear
Execution inconsistent
Coordination overhead
With
Centralized decision logic
ROI-backed prioritization
Consistent execution
Measured outcomes
Replaces coordination overhead, not teams. One system, shared across PSP, product, and finance.
Architecture position
Sits above:
→ PSPs & payment processors
→ Orchestration layer (orchestrator, etc.)
→ Fraud & risk systems
Controls:
Routing logic · Retry strategy · Method selection
Not a dashboard — control logic for payments revenue.
Where this system sits
Decision
Orchestrator action
Retry optimization
Retry config spec
Multi-PSP routing
Routing rule spec
Cost optimization
Cost routing spec
Method expansion
Method config spec
This system outputs execution-ready configurations that plug into existing infrastructure.
System role
System of Record → PSPs & processors
System of Action → orchestration layer
This system → decisioning + configuration
Capital allocation view
Cost and revenue projections update automatically when you run modules. Based on top decision in Revenue Action Planning Engine.
Budget required
~$180K
est. from top decision effort
Revenue return
$500K+
first year recovery (60% flow-through)
Net EBITDA
$320K+
net after implementation cost
vs alternative uses of capital
Head of Payments hire: $250K/year · Agency: $150K/year
System ROI: 2.9x vs alternatives
System evolution
NOW
Decision support
Identifies, quantifies, prioritizes — outputs to team
P2
Semi-automated execution
Pushes approved decisions to routing, retry, PSP config
P3
Autonomous optimization
Self-adjusting based on outcome data — reduces manual decisions over time
Goal: reduce manual decision-making, not headcount. Each phase builds on real outcome data.