Discovery & Taxonomy
We studied CADEX’s operational workflows and mapped the real-world email landscape.
- Email analysis
- Operations workshops
- Category mapping
- Edge-case identification
- Workflow analysis
CLIENT
B-ON & INOMO
TEAM
UX Designer, UI Designer, Product Designer, UX Writer
Services Used
The Challenge
Mobility products are inherently complex.
Drivers need simple access to vehicle information and actions. Fleet operators need to manage multiple assets, energy systems, and data sources. Mobility businesses need to communicate sophisticated technologies to both technical and non-technical audiences.
Our challenge was to make these experiences clear, useful, and scalable without losing the depth of the underlying technology.
B-ON
- High feature density
- Complex vehicle data
- Unclear task prioritization
- Balancing utility with community
The Challenge
INOMO Connect was specifically designed to consolidate telematics data and centralize energy, charging, asset, and fleet management.
INOMO Connect
- Multiple fleet types and data sources
- Fragmented telematics information
- Complex fleet and energy operations
- Need for centralized management
- Third-party system integration
INOMO Website
- Legacy website structure
- Complex technology portfolio
- B2B and B2C audiences
- Technical content complexity
- Need for stronger product discoverability
Research & Discovery
B-ON
We combined qualitative research, product data, UX audits, business requirements, and iterative validation to understand both user needs and operational complexity.
User Interviews 24 participants
Age22–45
Users92% mobile users
Analytics Review 60K+ sessions
Time Period 6 months
Drop- off65% drop-off
INOMO Connect & INOMO Website
The INOMO website research documentation specifically identifies the UX audit as the strongest documented research activity; unsupported research statistics have therefore been excluded.
User behavior analysis UX audit
User interviews Information architecture review
User flows and scripts Navigation analysis
Testing Content and product analysis
Business objective analysis Product discoverability review
Key Research Findings
58%
Wanted one place for vehicle information
46%
Needed clearer performance and maintenance guidance
42%
Showed interest in gamification and progress tracking
INOMO
The research highlighted the need to:
- Centralize complex information
- Improve information hierarchy
- Simplify technical content
- Create clearer operational workflows
- Align experiences with business objectives
Intelligent Email Preprocessing
The preprocessing layer prepares every message for downstream AI models.
- Email cleaning
- Content normalization
- PDF extraction
- Image OCR
- Attachment processing
- Structured data preparation
Hierarchical Intent Classification
Instead of forcing every email into a single category, the system progressively determines the message’s intent.
- Coarse intent detection
- Fine-grained classification
- Multi-intent handling
- Edge-case detection
- Manual review routing
Smart Workflow Routing
Once an email is classified, the system determines where it belongs operationally.
- Intent-based routing
- Queue assignment
- Context preservation
- Workflow-specific tagging
- Automated operational handoff
Measurable Impact
40%
Classification Accuracy
AI classification improved dramatically when moving from the earlier production model to the new workflow.
Before:40%
After:98%
+65%
SLA Performance
Improved routing and faster handling helped increase service-level adherence.
Before:Baseline
After:+65%
−30%
Average Handling Time
Automated classification and routing reduced the time agents spent on email triage.
Before:Manual-heavy handling
After:30% reduction
−50%
Manual Workforce Requirement
Automation reduced the workforce requirement associated with email triage.
Before:Fully manual-heavy process
After:~50% reduction
Model Adaptability
Human feedback transformed the AI from a static model into a continuously improving system.
Before:One-time model
After:Feedback-driven learning
Methodology & Testing
Email Taxonomy Design
Mapped real-world email types, edge cases, and operational workflows into a structured classification system.
Multi-Agent AI Design
Separated preprocessing, classification, routing, and feedback into specialized AI responsibilities.
Hierarchical Classification
Used progressive classification to handle complex and multi-intent messages more reliably.
“We’d tried machine learning before, but it never survived contact with our real email traffic. Galaxy helped us turn that idea into a production system that actually works. Accuracy jumped, SLAs impr
Operations Leadership, CADEX
Operations Leadership, CADEX
Key Learnings & Insights
Real-world data changes everything
A model that performs well on curated data can behave very differently in production.
Complex problems need modular AI
Separating responsibilities makes AI systems easier to monitor, tune, and scale.
Automation needs human oversight
Manual review provides a safe path for ambiguous or high-risk cases.
Feedback creates smarter systems
Human corrections can become structured learning signals for continuous improvement.
Design Process Deep Dive
AI Architecture & Prototyping
We translated the taxonomy into a modular AI workflow.
- Multi-agent architecture
- Classification models
- Routing logic
- OCR integration
- Feedback mechanisms
Testing & Optimization
The system was refined against real-world conditions and operational feedback.
- Classification testing
- Edge-case validation
- Human review
- Model tuning
- Performance monitoring
Implementation & Handoff
AI Workflow Integration
Integrated the AI pipeline into CADEX’s existing email and support environment.
Queue Integration
Connected classifications and tags to the appropriate operational workflows.
Monitoring
Enabled visibility into accuracy, SLA performance, handling time, and email volumes.
Developer Handoff Process
Architecture
Defined agent responsibilities, workflow logic, classification hierarchy, and routing rules.
Integration
Connected the AI workflow with existing email systems, queues, and operational processes.
Testing
Validated classification, routing, attachments, edge cases, and human-review scenarios.
Monitoring
Tracked model performance and operational metrics to support continuous optimization.
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