The Hub Console

Helping enterprise developers provision and debug cloud services 29% faster by transforming a complex backend into an intuitive, trustworthy console.

29%

Time-to-Debug Reduced

Less

Errors & Support Calls

Less

Steps and Tools

Discovery: Miro, Figjam, User Journeys
Design: Figma
Product/Backlog: Atlassian

Problem

Developers had to navigate long, cryptic service lists and external logging tools to understand system health and resolve failures.

User Pain Points

This resulted in:

Slower deployments and debugging
High Mean Time to Resolution (MTTR)
Increased operational cost
Heavy reliance on support teams

Technical Constraints

Discovery

To understand real workflows, I conducted:

Interviews with developers and operators
Shadowing of debugging sessions
Analysis of support tickets
Reviews of infrastructure documentation

Key Insights that guided the decisions:

Developers memorised unsafe workarounds
Logs were the main debugging tool, but hard to access
Users prioritised speed over elegance
Fear of mistakes slowed decision-making
Most errors happened during provisioning

Key Decisions

1. System Health Was Hard to Understand

Insight

Developers could not quickly determine whether services were healthy or failing, which delayed troubleshooting and increased reliance on external tools.

Decision

Designed a status-first service list with clear visual indicators (Online, Warning, Failed) and progressive disclosure for technical detail.

Impact

Developers could prioritise issues immediately, reducing time spent scanning logs and improving time-to-debug.

Progressive Disclosure, Visibility of System Status, Minimal Cognitive Load

2. Debugging Required Constant Context Switching

Insight

Developers had to leave the console to access raw Kubernetes logs, breaking their workflow and increasing Mean Time to Resolution.

Decision

Integrated inline error alerts and a full-screen log viewer that allowed users to navigate logs without leaving the interface.

Impact

Debugging workflows became continuous and focused, reducing resolution time and improving developer productivity.

Progressive Disclosure, Visibility of System Status, Minimal Cognitive Load

3. Configuration Errors Were Costly and Frequent

Insight

Small mistakes in selecting plans, regions, or replication modes often resulted in failed or expensive deployments.

Decision

Designed guided provisioning flows with step-by-step validation, live pricing feedback, and plan comparisons.

Impact

Provisioning errors dropped significantly, leading to more reliable deployments and lower operational risk.

Error Prevention, Immediate Feedback, Consistency & Standards

4. Users Were Afraid of Destructive Actions

Insight

Developers hesitated when restarting or deleting services because mistakes could cause system outages or data loss.

Decision

Introduced context-aware confirmation patterns showing instance name, status, and consequences before execution.

Impact

Destructive actions became safer and more predictable, reducing accidental outages and increasing user confidence.

Error Prevention, Immediate Feedback, Consistency & Standards

Outcome

How I Worked?

Design System Components

1. Align & Frame

Aligned with product, engineering, and compliance to define scope, risks, and success criteria.

2. Observe & Research

Interviewed developers, analysed support data, and shadowed real debugging workflows.

3. Model the System

Mapped permissions, dependencies, and failure states in a multi-tenant environment.

4. Structure & Strategise

Designed status-driven navigation, safety patterns, and progressive disclosure rules.

5. Design & Test

Built interactive prototypes and validated them through usability testing and scenario walkthroughs.

6. Ship & Improve

Partnered with engineering on delivery and iterated post-launch using real usage data.