AI Chatbot Builder
AI Chatbot Builder
AI Chatbot Builder
Empowering Conversations: The Journey to Building a Scalable AI Chatbot Builder within a CRM platform to streamline customer interactions and enhance automation for improved conversion rates.
Empowering Conversations: The Journey to Building a Scalable AI Chatbot Builder within a CRM platform to streamline customer interactions and enhance automation for improved conversion rates.
Role
Research, Design, Prototype
Client
The String
Year
2023
Platform
Web-based CRM system (SaaS)
The Problem
Conversational tools were powerful, but complex
For teams managing customer communication inside a CRM, this wasn’t just a request for more features. It was a need for simplicity.
Marketers, customer support agents, and operations teams didn’t have time to wrestle with overly technical tools. They needed a way to create, understand, and scale conversations without relying on developers or deciphering complex logic.
Through user interviews and competitive analysis, three core challenges emerged:
The Problem
Conversational tools were powerful, but complex
For teams managing customer communication inside a CRM, this wasn’t just a request for more features. It was a need for simplicity.
Marketers, customer support agents, and operations teams didn’t have time to wrestle with overly technical tools. They needed a way to create, understand, and scale conversations without relying on developers or deciphering complex logic.
Through user interviews and competitive analysis, three core challenges emerged:
The Problem
Conversational tools were powerful, but complex
For teams managing customer communication inside a CRM, this wasn’t just a request for more features. It was a need for simplicity.
Marketers, customer support agents, and operations teams didn’t have time to wrestle with overly technical tools. They needed a way to create, understand, and scale conversations without relying on developers or deciphering complex logic.
Through user interviews and competitive analysis, three core challenges emerged:
The gap was clear: there was no intuitive way to design conversational experiences that balanced flexibility, clarity, and scale within a CRM environment.
The gap was clear: there was no intuitive way to design conversational experiences that balanced flexibility, clarity, and scale within a CRM environment.
The gap was clear: there was no intuitive way to design conversational experiences that balanced flexibility, clarity, and scale within a CRM environment.
Key Insights
How different teams experienced the problem
To better understand how this complexity played out in real workflows, I observed how different teams used chatbots inside the CRM.
Despite having different goals, their frustrations were closely aligned:
Key Insights
How different teams experienced the problem
To better understand how this complexity played out in real workflows, I observed how different teams used chatbots inside the CRM.
Despite having different goals, their frustrations were closely aligned:
Key Insights
How different teams experienced the problem
To better understand how this complexity played out in real workflows, I observed how different teams used chatbots inside the CRM.
Despite having different goals, their frustrations were closely aligned:
Developer-first tools
Capture leads quickly, but struggled with tools built for developers rather than marketers.
Rigid conversation logic
Fast, reliable answers without complex conversation logic.
Limited visibility
Visibility without fragmented tools and unclear flow behavior.
Across roles, one insight stood out: teams needed a chatbot builder that felt intuitive and predictable, while still being powerful enough to support real customer interactions.
Across roles, one insight stood out: teams needed a chatbot builder that felt intuitive and predictable, while still being powerful enough to support real customer interactions.
Across roles, one insight stood out: teams needed a chatbot builder that felt intuitive and predictable, while still being powerful enough to support real customer interactions.
The Approach
Making chatbot creation intuitive without limiting capability
The core focus was usability.
The chatbot builder needed to be powerful enough to handle real customer interactions, but simple enough for non-technical teams to use with confidence.
Instead of adding more options or configurations, the approach focused on clarity and structure:
The Approach
Making chatbot creation intuitive without limiting capability
The core focus was usability.
The chatbot builder needed to be powerful enough to handle real customer interactions, but simple enough for non-technical teams to use with confidence.
Instead of adding more options or configurations, the approach focused on clarity and structure:
The Approach
Making chatbot creation intuitive without limiting capability
The core focus was usability.
The chatbot builder needed to be powerful enough to handle real customer interactions, but simple enough for non-technical teams to use with confidence.
Instead of adding more options or configurations, the approach focused on clarity and structure:
By strengthening the usability of the system first, the builder could support more flexible conversations naturally — allowing users to move beyond rigid, predefined answers while maintaining control inside the CRM.
By strengthening the usability of the system first, the builder could support more flexible conversations naturally — allowing users to move beyond rigid, predefined answers while maintaining control inside the CRM.
By strengthening the usability of the system first, the builder could support more flexible conversations naturally — allowing users to move beyond rigid, predefined answers while maintaining control inside the CRM.
The Solution
A visual chatbot builder designed for everyday teams
At its core, the experience is visual. Conversations are built by arranging components into clear flows using a drag-and-drop interface, allowing users to understand chatbot behavior without technical knowledge. Designing a chatbot feels closer to assembling a flowchart than configuring a system.
To reduce setup friction, teams can start from pre-built templates for common use cases such as FAQs, lead generation, and support scenarios, or build flows from scratch when more control is needed.
Throughout the process, users can preview conversations in real time, making it easy to validate logic, spot issues, and iterate with confidence before deployment.
Once live, performance visibility is built directly into the CRM. Managers can track engagement, response behavior, and outcomes, ensuring chatbot performance is measurable and actionable.
The result is a chatbot builder that remains approachable for first-time users, yet capable enough to support real customer interactions at scale.
The Solution
A visual chatbot builder designed for everyday teams
At its core, the experience is visual. Conversations are built by arranging components into clear flows using a drag-and-drop interface, allowing users to understand chatbot behavior without technical knowledge. Designing a chatbot feels closer to assembling a flowchart than configuring a system.
To reduce setup friction, teams can start from pre-built templates for common use cases such as FAQs, lead generation, and support scenarios, or build flows from scratch when more control is needed.
Throughout the process, users can preview conversations in real time, making it easy to validate logic, spot issues, and iterate with confidence before deployment.
Once live, performance visibility is built directly into the CRM. Managers can track engagement, response behavior, and outcomes, ensuring chatbot performance is measurable and actionable.
The result is a chatbot builder that remains approachable for first-time users, yet capable enough to support real customer interactions at scale.
The Solution
A visual chatbot builder designed for everyday teams
At its core, the experience is visual. Conversations are built by arranging components into clear flows using a drag-and-drop interface, allowing users to understand chatbot behavior without technical knowledge. Designing a chatbot feels closer to assembling a flowchart than configuring a system.
To reduce setup friction, teams can start from pre-built templates for common use cases such as FAQs, lead generation, and support scenarios, or build flows from scratch when more control is needed.
Throughout the process, users can preview conversations in real time, making it easy to validate logic, spot issues, and iterate with confidence before deployment.
Once live, performance visibility is built directly into the CRM. Managers can track engagement, response behavior, and outcomes, ensuring chatbot performance is measurable and actionable.
The result is a chatbot builder that remains approachable for first-time users, yet capable enough to support real customer interactions at scale.
Designed for Reliability
Power with clear boundaries
To keep conversations reliable and predictable, the chatbot is designed to respond based on approved knowledge and defined behavior.
Rather than answering everything freely, responses are shaped by the content teams provide and the context in which the chatbot appears. When confidence is low or a request falls outside its scope, the system can defer or escalate instead of guessing.
This ensures conversations feel flexible for users, while remaining controlled and intentional for teams.
Designed for Reliability
Power with clear boundaries
To keep conversations reliable and predictable, the chatbot is designed to respond based on approved knowledge and defined behavior.
Rather than answering everything freely, responses are shaped by the content teams provide and the context in which the chatbot appears. When confidence is low or a request falls outside its scope, the system can defer or escalate instead of guessing.
This ensures conversations feel flexible for users, while remaining controlled and intentional for teams.
Designed for Reliability
Power with clear boundaries
To keep conversations reliable and predictable, the chatbot is designed to respond based on approved knowledge and defined behavior.
Rather than answering everything freely, responses are shaped by the content teams provide and the context in which the chatbot appears. When confidence is low or a request falls outside its scope, the system can defer or escalate instead of guessing.
This ensures conversations feel flexible for users, while remaining controlled and intentional for teams.
Configuration & Context
One assistant, many situations
The chatbot is designed to adapt to different contexts without requiring separate setups.
Teams can control where the chatbot appears, who it interacts with, and how it behaves across channels. The same assistant can respond differently depending on page, audience, or use case, while still following a consistent conversational logic.
Tone, behavior, and activation are configurable at a high level, allowing teams to align the experience with their brand and goals without rebuilding flows from scratch.
This makes the chatbot flexible enough to support multiple scenarios, while remaining simple to manage inside everyday workflows.
Configuration & Context
One assistant, many situations
The chatbot is designed to adapt to different contexts without requiring separate setups.
Teams can control where the chatbot appears, who it interacts with, and how it behaves across channels. The same assistant can respond differently depending on page, audience, or use case, while still following a consistent conversational logic.
Tone, behavior, and activation are configurable at a high level, allowing teams to align the experience with their brand and goals without rebuilding flows from scratch.
This makes the chatbot flexible enough to support multiple scenarios, while remaining simple to manage inside everyday workflows.
Configuration & Context
One assistant, many situations
The chatbot is designed to adapt to different contexts without requiring separate setups.
Teams can control where the chatbot appears, who it interacts with, and how it behaves across channels. The same assistant can respond differently depending on page, audience, or use case, while still following a consistent conversational logic.
Tone, behavior, and activation are configurable at a high level, allowing teams to align the experience with their brand and goals without rebuilding flows from scratch.
This makes the chatbot flexible enough to support multiple scenarios, while remaining simple to manage inside everyday workflows.
Outcome
From setup friction to everyday usage
Faster setup: Teams could build, test, and launch chatbots in minutes instead of days, without relying on technical support.
Lower friction: Clear visual flows and real-time previews reduced trial and error and gave teams confidence before going live.
Broader adoption: Non-technical roles, from marketing to customer support, were able to own and manage chatbot experiences independently.
More consistent conversations: Combining structured flows with flexible responses helped maintain a human tone across automated interactions.
Stronger CRM value: The chatbot builder evolved into a core capability of the CRM, supporting everyday workflows rather than feeling like an add-on.
Outcome
From setup friction to everyday usage
Faster setup: Teams could build, test, and launch chatbots in minutes instead of days, without relying on technical support.
Lower friction: Clear visual flows and real-time previews reduced trial and error and gave teams confidence before going live.
Broader adoption: Non-technical roles, from marketing to customer support, were able to own and manage chatbot experiences independently.
More consistent conversations: Combining structured flows with flexible responses helped maintain a human tone across automated interactions.
Stronger CRM value: The chatbot builder evolved into a core capability of the CRM, supporting everyday workflows rather than feeling like an add-on.
Outcome
From setup friction to everyday usage
Faster setup: Teams could build, test, and launch chatbots in minutes instead of days, without relying on technical support.
Lower friction: Clear visual flows and real-time previews reduced trial and error and gave teams confidence before going live.
Broader adoption: Non-technical roles, from marketing to customer support, were able to own and manage chatbot experiences independently.
More consistent conversations: Combining structured flows with flexible responses helped maintain a human tone across automated interactions.
Stronger CRM value: The chatbot builder evolved into a core capability of the CRM, supporting everyday workflows rather than feeling like an add-on.
Reflection
What this project reinforced
Empathy drives clarity: Understanding how non-technical teams work helped shape an experience that feels simple without sacrificing capability.
Usability scales adoption: Clear visual structure and predictable behavior made the tool approachable across roles and use cases.
Iteration prevents complexity: Testing early and refining flows as they evolved helped avoid unnecessary features and friction.
Power doesn’t need exposure: Advanced capabilities can stay behind the scenes while users focus on outcomes, not configuration.
Reflection
What this project reinforced
Empathy drives clarity: Understanding how non-technical teams work helped shape an experience that feels simple without sacrificing capability.
Usability scales adoption: Clear visual structure and predictable behavior made the tool approachable across roles and use cases.
Iteration prevents complexity: Testing early and refining flows as they evolved helped avoid unnecessary features and friction.
Power doesn’t need exposure: Advanced capabilities can stay behind the scenes while users focus on outcomes, not configuration.
Reflection
What this project reinforced
Empathy drives clarity: Understanding how non-technical teams work helped shape an experience that feels simple without sacrificing capability.
Usability scales adoption: Clear visual structure and predictable behavior made the tool approachable across roles and use cases.
Iteration prevents complexity: Testing early and refining flows as they evolved helped avoid unnecessary features and friction.
Power doesn’t need exposure: Advanced capabilities can stay behind the scenes while users focus on outcomes, not configuration.
