| Senior Manager, User Experience
Defining a CX modelling system for complex customer conversations
A system that enabled teams to design and test conversational CX workflows, simulating real customer interactions before implementation.
FOCUS: Product Strategy | CX Architecture | Workflow Architecture | Cross-Functional Alignment
Product context
Platform: Cyara Velocity
Domain: CX Testing & Conversation Design
Contact centres were increasingly automating customer requests across voice and chat channels.
Designing these experiences required navigating fragmented configuration tools, scripts and system interfaces. Conversation designers, product managers and engineers often worked across different representations of the same customer journey.
As conversational systems grew more complex, teams struggled to maintain a clear model of conversational paths, making it difficult to simulate and test CX behaviour reliably.
The opportunity was to create a visual CX modelling system that structured these conversations and aligned with the underlying platform architecture.
Key insight
Conversational experiences are not simply flows. They are decision systems that determine how customer requests are interpreted, routed and resolved.
Traditional flows often hid this complexity because the logic driving the experience lived across configuration layers.
The design challenge became:
How might we create a visual CX modelling system that represents conversational logic clearly enough for teams to simulate and test customer interactions?
Early exploration mapped how conversational systems combine intent recognition, decision logic and modular CX paths.
Product decisions
1. Visual CX Model as the source of truth
We designed a visual CX modelling system where conversational experiences were defined as structured models rather than disconnected flows.
The model allowed teams to define:
-
Entry points
-
Routing logic
-
Intent recognition
-
Conversational states
-
System responses
Why it matters:
Provided a shared CX model that product, design and engineering teams could use to structure and test CX paths.
2. Decision nodes for deterministic routing
Decision nodes represented structured logic conditions that determined how a conversation should proceed.
These nodes allowed teams to route interactions based on:
• User input
• Customer attributes
• Contextual conditions
Why it matters:
Made routing logic visible and easier for teams to simulate and validate CX paths.
3. NLU nodes for intent recognition
Natural Language Understanding (NLU) nodes represented points where the system interpreted user input through trained intent models.
These nodes allowed designers to define:
• Intent recognition boundaries
• Fallback behaviour
• Confidence thresholds
Why it matters:
Separated intent interpretation from routing logic, making conversational paths robust, easier to model and test.
4. Dataset-driven routing
Some conversational decisions were driven by large datasets such as:
-
Customer profiles
-
Product catalogues
-
Eligibility rules
​
Instead of embedding complex logic directly in nodes, we introduced dataset-driven routing.
Datasets could be connected to decision nodes to dynamically route conversations based on structured data inputs.
Why it matters:
This reduced complexity in the visual model while enabling powerful routing logic.
5. Modular CX modelling for collaboration
Large conversational systems often involve many contributors and required teams to break CX models into manageable modules.
To support collaboration, the modelling system introduced modular CX structures that allowed teams to compartmentalise different sections of the experience.
​
Modules allowed teams to:
-
Isolate specific conversation segments
-
Reuse common interaction patterns
-
Maintain clarity in large CX models
Why it matters:
This enabled cross-functional teams to collaborate on a single CX model without overwhelming complexity.
Product impact
The CX modelling system became the foundation for structuring conversational experiences within the platform.
It enabled teams to:
• Visualise complex customer interaction paths
• Align CX design with platform architecture
• Simulate conversational behaviour before deployment
• Improve the reliability of CX testing across voice and chat channels
Strategic takeaways
This project reinforced that designing conversational products requires thinking beyond interfaces.
The core challenge was not simply visualising a flow, but representing the underlying decision systems that drive conversational AI.
By aligning the visual model with the platform’s architecture, including decision logic, intent recognition and data-driven routing, the CX modelling system became more than a design tool.
It became a shared framework that allowed product, design and engineering teams to collaborate on the structure of conversational experiences.
