02
Overview
A manageable way to work with AI.
ChatSquare is designed to help SMEs create, train and manage an AI chatbot without requiring specialist technical knowledge.
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Selected Work 04
AI Chatbot and Training CMS
ChatSquare
Designing a clearer way for SMEs to train and manage AI.
- Role
- Product Strategy / UX/UI Design / Prototyping / Product Website
- Platforms
- Website Chatbot / CMS Platform / Responsive Product Website
- Target users
- SMEs / Non-technical users unfamiliar with AI training
- Design output
- One coherent journey for setup, training, review and product communication.
Available on
01
Project snapshot
The product, people and scope—at a glance.
- Project
- ChatSquare
- Product Type
- AI Chatbot and Training CMS
- Target Users
- SMEs / Non-technical users / Teams unfamiliar with AI training
- Platforms
- Website Chatbot / CMS Platform / Responsive Product Website
- My Role
- Product StrategyEnd-to-end UX/UI DesignPrototypingProduct Website
03
The challenge
Make complex training feel clear.
AI training can feel complex and inaccessible to non-technical users. The challenge was to turn chatbot setup, knowledge management and response refinement into a clear, manageable workflow.
04
My role & process
One continuous product-design journey.
I defined the product direction and connected the chatbot, training CMS and product website from system flow through developer handoff.
Product Strategy + System Flow + Information Architecture + UX/UI Design + Prototyping
01Product Strategy
02System Flow
03Information Architecture
04Wireframes
05UI Design
06Interactive Prototype
07Developer Handoff
05
Product strategy
Design principles for clarity, control and a lower barrier to entry.
Three principles kept the experience focused on what non-technical teams need to understand and do.
Simplify Setup
Guide users without technical language.
Centralise Training
Use the CMS as the main knowledge-management workspace.
Keep Users in Control
Make content, training status and responses easy to review and refine.
06
Product flow
From business content to continuous improvement.
The loop keeps knowledge, publishing and response refinement connected instead of treating AI training as a one-off setup task.
01Add Business Content
02Organise Knowledge
03Train Chatbot
04Publish Website Widget
05Review Conversations
06Refine Responses
Update Business Content
Refined responses feed back into the source knowledge.
07
Promotion website & chatbot demo
ChatSquare Promotion Website & Chatbot Demo
The promotional website introduces ChatSquare’s core features and links to its live chatbot demo so prospective users can explore the product directly.
The live demo opens on ChatSquare's website and is subject to its availability and privacy practices.
Live experience hosted by chatsquare.aiOpen Live ChatSquare Demo ↗
08
CMS user flow
A clear path from review to chatbot training.
The CMS connects chat review, Q&A training, reporting and settings from one dashboard, with the main training journey highlighted throughout the map.
Rectangle: ScreenRounded rectangle: ActionDark node: Status outcomeBlue route: Primary training journey
Focused CMS map
Review, train and resolve.
The primary route connects conversation review to Q&A training, while reporting and settings remain available from Home.
- The primary journey moves from login and Home through chat records, conversation review, Q&A creation and training status.
- Custom Q&A offers a direct route to create or edit a training record.
- Reports lead to report details and export, while Settings controls email notice frequency.
09
CMS UI showcase
The operational centre of the product.
The CMS frames AI training as familiar workspace tasks: organise knowledge, check progress and improve responses from real conversations.
10
Design output & reflection
Make AI feel like a manageable product task.
Design output
The final design connects chatbot setup, knowledge training, response review and product communication into one coherent journey intended for teams without specialist AI knowledge.
This case study documents the product structure and design rationale. It does not claim measured usability or business impact because validated outcome data is not available for publication.
Reflection
The project reinforced the importance of translating AI concepts into familiar tasks, visible feedback and clear user control.