Enterprise Partner Ecosystem
I designed the core architecture for a Partner Relationship Management platform handling 50M+ users and high-load billing cycles. The system covers the full partner lifecycle: from onboarding to financial reporting.
I designed the core architecture for a Partner Relationship Management platform handling 50M+ users and high-load billing cycles. The system covers the full partner lifecycle: from onboarding to financial reporting.
Role: Senior Product Designer (UX Architecture, UI).
Domain: Fintech / Enterprise B2B.
Platform: Web Desktop.
Team: Cross-functional squad of 20 specialists
(incl. 8 Backend Devs & 4 Analysts).
Domain: Fintech / Enterprise B2B.
Platform: Web Desktop.
Team: Cross-functional squad of 20 specialists
(incl. 8 Backend Devs & 4 Analysts).
THE CONTEXT
B2B PLATFORM
Validation
Systematization
Audit
Prototyped a logic flow where incompatible blocks simply cannot connect (Error prevention by design).
Grouped variables into "Objects" and "Attributes".
Analyzed 50+ existing Groovy scripts to identify common variables
(Region, Segment, Tariff Plan).
(Region, Segment, Tariff Plan).
Deconstructing the Logic
I couldn't just "draw a form."
I had to understand how the backend logic worked to translate it into a UI.
I couldn't just "draw a form."
I had to understand how the backend logic worked to translate it into a UI.
THE PROCESS
The "Groovy" Bottleneck
The billing logic relied on hard-coded Groovy scripts. To change a simple tariff commission or add a region condition, business managers had to request a developer.
The billing logic relied on hard-coded Groovy scripts. To change a simple tariff commission or add a region condition, business managers had to request a developer.
THE CHALLENGE
Human Error
Slow Time-to-Market
High Cost
Manual scripting led to critical billing mistakes.
Launching a new partner campaign took days.
Developers spent hours on routine config changes.
Defining the Scope
Before applying complex math, the system needs to know who and when to pay.
In Step 1, the manager sets the Rule Scope: filtering target Segments (e.g., "Gold Partners"), Regions, and specific Trigger Events (e.g., "Activation" or "Billing").
This pre-filtering reduces system load by processing only relevant data.
Before applying complex math, the system needs to know who and when to pay.
In Step 1, the manager sets the Rule Scope: filtering target Segments (e.g., "Gold Partners"), Regions, and specific Trigger Events (e.g., "Activation" or "Billing").
This pre-filtering reduces system load by processing only relevant data.
Context & Hierarchy
The process begins at the Calculation Scheme level—the global container that holds all financial logic for a specific period.
Instead of configuring partners one by one, managers define a master scheme that is automatically inherited by child entities.
This ensures consistency across the entire Partner Network.
The process begins at the Calculation Scheme level—the global container that holds all financial logic for a specific period.
Instead of configuring partners one by one, managers define a master scheme that is automatically inherited by child entities.
This ensures consistency across the entire Partner Network.
THE SOLUTION
The Visual Logic Engine
Body: The core of the solution. Replaced legacy SQL scripting with a No-Code Constructor.
Managers build logic using natural language blocks:
Body: The core of the solution. Replaced legacy SQL scripting with a No-Code Constructor.
Managers build logic using natural language blocks:
- Conditions: Set qualifiers (e.g., Revenue > Threshold).
- Formula: Drag-and-drop dynamic Tokens to build math expressions (Amount * Rate). The system validates logic in real-time, preventing syntax errors before they hit production.
RESULTS
Scalability
Successfully handling billing cycles for a 50M+ user base.
10x Speed
Tariff setup time reduced from 2 days to ~1 hour.
Zero-Code Workflow
Business managers now configure 95% of rules without developers.
This case study is a conceptual representation of my work.
Visuals have been redesigned and data has been anonymized to comply with NDA policies. All specific metrics and names are illustrative.
Visuals have been redesigned and data has been anonymized to comply with NDA policies. All specific metrics and names are illustrative.