Octopus.AI - Generative AI
Octopus.AI -
Generative AI
Octopus.AI - Generative AI
Octopus.AI (Dalfin AI) leverages cutting-edge generative AI to help anyone, whether you're a developer or non-technical user, create groundbreaking software solutions with ease. By simplifying complex coding tasks, automating repetitive processes, and enabling rapid prototyping, Dalfin AI accelerates the development of custom applications, AI driven solutions, and innovative software.
With no code and low code capabilities, collaborative tools, and seamless integrations, Dalfin AI breaks down traditional barriers to software development, empowering users to transform their ideas into reality faster than ever before.
Octopus.AI (Dalfin AI) leverages cutting edge generative AI to help anyone, whether you're a developer or non-technical user, create groundbreaking software solutions with ease. By simplifying complex coding tasks, automating repetitive processes, and enabling rapid prototyping, Dalfin AI accelerates the development of custom applications, AI driven solutions, and innovative software.
With no code and low code capabilities, collaborative tools, and seamless integrations, Dalfin AI breaks down traditional barriers to software development, empowering users to transform their ideas into reality faster than ever before.
Client
Dalfin
Services
Gen. AI Experience Design
User Interface Design
Roles
Rahul Choudhary - Creative Tech Lead
Jeff Davis - Gen. AI Experience Designer
Twisha Gupta - UX Designer
Date
June 2024
Project Understanding & Goals
Conducted detailed stakeholder interviews and workshops to deeply understand project vision, business goals, and user expectations.
Objectives defined -
Empower users to effortlessly generate highly intuitive and functional software UIs.
Develop flexible and scalable UI templates that AI can effectively use as reference points.
Define success criteria: ease of use, precision, adaptability, and user satisfaction.
Conducted stakeholder interviews and workshops to align on project vision, business goals, and user expectations.
Engaged stakeholders through interviews and workshops to align on the project vision and user needs. Key goals include enabling effortless creation of intuitive UIs, developing scalable templates for AI use, and ensuring success through ease of use, accuracy, and adaptability.
Engaged stakeholders through interviews and workshops to align on the project vision and user needs. Key goals include enabling effortless creation of intuitive UIs, developing scalable templates for AI use, and ensuring success through ease of use, accuracy, and adaptability.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Competitive Analysis
Our review of existing AI UI tools revealed gaps in flexibility, customization, and usability. These insights guided us to build a more adaptive and user-friendly system, where AI supports creative control and delivers high-quality, scalable design outputs.
Our review of existing AI UI tools revealed gaps in flexibility, customization, and usability. These insights guided us to build a more adaptive and user-friendly system, where AI supports creative control and delivers high-quality, scalable design outputs.
Competitor
UX Strengths
UX Weaknesses
Uizard
Rapid prototyping, user friendly
Limited advanced customization features
Figma
Competitor
Powerful design and collaboration tools
Strengths
No direct AI generated UI components
Weaknesses
Zoho Books
Extensive integrations, customizable
Complex interface, steep learning curve
Sketch
Rich component libraries, good user control
Lack of built in AI automation
Adobe XD
Strong prototyping and UI design
Limited integration with generative AI
Canva
Easy to use, extensive template library
Limited for software UI prototyping
What we found
Insights
The analysis highlighted a clear gap in AI driven UI generation among leading tools. While platforms like Uizard and Canva offer ease of use and speed, they lack advanced customisation and are not ideal for software UI prototyping. Figma, Sketch, and Adobe XD provide powerful design capabilities but don’t natively support AI generated components or workflows. These insights underscored the opportunity to create a tool that combines intuitive design with AI automation, bridging the gap between usability, customisation, and intelligent UI generation.
Extended Design Synthesis
Extended Design Synthesis
Extended Design Synthesis
We conducted affinity diagramming sessions using insights from stakeholder interviews, workshops, and competitor benchmarking. Key insights were grouped under major themes such as:
User Expectations from AI generated UIs.
Challenges in Trusting Automated Design Outputs.
Desired Control and Customisation Levels.
Developer Pain Points in Integrating AI Outputs.
UI/UX Best Practices Relevant to AI.
These were then prioritized based on frequency, impact, and feasibility, which informed the next phases of feature ideation and user flow development.
We developed core and extended use cases across user personas:
Core Use Cases
Generate a new UI layout from scratch using AI suggestions.
Customise AI generated templates based on domain-specific needs.
Provide real-time feedback to fine-tune future AI outputs.
Collaborate with team members in real time on generated UIs.
Extended Use Cases
Override AI generated designs manually.
Analyse usage patterns and UI performance using analytics.
Learn to use the platform via guided interactive tutorials.
Each use case was mapped with its entry point, AI interaction layer, and desired outcome, forming the foundation of user flow diagrams.
Card Sorting for Navigation
Card Sorting for Navigation
Card Sorting for Navigation
To validate the platform's navigation structure and terminology by understanding how users naturally categorize and interpret features and sections of the product.
Type of Card Sorting -
We conducted both open card sorting (where participants grouped features freely and labeled categories themselves) and closed card sorting (where they sorted predefined cards into existing category names).
Participants:
8 Product Designers
5 Frontend Developers
3 Project Managers
4 Tech-savvy End Users
(Total : 20 participants)
Tool Used - Miro for remote sessions, physical cards for in-person workshops
Each card represented a distinct feature, task, or screen on the platform. Examples included -
Generate UI from prompt
Select a template
Edit generated layout
View analytics
Access tutorial walkthrough
Change AI preferences
Give feedback on UI
Access documentation
Customise colour scheme
View component library
Share design with team
Check accessibility compliance
Key Patterns Identified from Open Card Sorting
Groupings by Task Flow - Most users grouped cards by sequential workflows :
Creation (e.g., Generate UI, Choose Template, Customise)
Learning (e.g., Tutorials, Documentation)
Review & Feedback (e.g., Feedback Portal, Accessibility Checker, Analytics)
Overlap Between Customisation & Settings -
Users frequently placed “Change AI preferences,” “Customise colour scheme,” and “Accessibility settings” in both “Settings” and “Customisation.”
Decision : Split settings into General Settings and Design Customisation.
Navigation Language Insights -
Users preferred intuitive, task-based terms:
“UI Builder” or “Design Studio” over “UI Generator”
“Design Settings” over “AI Preferences”
“Design Library” over “Component Library”
Component vs Template Confusion -
Many grouped components and templates together.
Decision: Include contextual help text and tooltips in UI to clarify - “Templates are pre-designed full screen layouts. Components are reusable UI elements like buttons or forms.”
Feedback Loop Perception - Users associated the feedback portal with both “Support” and “AI Improvement,” suggesting its dual role as both utility and contribution tool.
Navigation Language Insights -
Users preferred intuitive, task based terms :
“UI Builder” or “Design Studio” over “UI Generator”
“Design Settings” over “AI Preferences”
“Design Library” over “Component Library”
Component vs Template Confusion -
Many grouped components and templates together.
Decision : Include contextual help text and tooltips in UI to clarify - “Templates are pre-designed full screen layouts. Components are reusable UI elements like buttons or forms.”
Feedback Loop Perception - Users associated the feedback portal with both “Support” and “AI Improvement,” suggesting its dual role as both utility and contribution tool.
Outcomes and Changes Made to IA
Insight
Action Taken
Users think in task-based flows, not features
Reorganized IA into workflow aligned clusters : Create → Customise → Collaborate → Learn → Evaluate
Confusion between components and templates
Added microcopy/tooltips for clarification + visual examples
Tutorials and documentation were expected under one label
Merged into a unified “Learn” section
Settings perceived as too broad
Split into “Platform Settings” and “Design Preferences”
Users wanted easy access to feedback and collaboration tools
Moved “Feedback” and “Collaboration” into top level navigation items
Final Navigation Structure (Post Sorting Iteration)
Home - Dashboard Overview.
Design Studio - Template Generator, Layout Customiser, AI Preview.
Design Library - UI Components, Layout Patterns, Responsive Frameworks.
Learn - Tutorials, Developer Docs.
Collaborate - Real Time Collaboration, Share Designs.
Feedback - Feedback Portal, AI Suggestions Log.
Evaluate - Analytics, Accessibility Checker.
Settings - Platform Settings.
Primary User Stories
ID
Role
User Story
Priority
Benefit
01
Product Designer
As a designer, I want to generate a UI layout using AI so I can quickly iterate on ideas.
High
Speed & ideation
02
Developer
As a developer, I want predictable and clean AI generated code so I can avoid debugging overhead.
High
Seamless handoff
03
End User
As a user, I want to control and edit the AI output so I feel confident using it.
High
Trust & usability
04
Project Manager
As a manager, I want collaboration tools to monitor and review team progress in real time.
Medium
Visibility & collaboration
05
Accessibility Advocate
As a user, I want UI designs to meet accessibility standards so our product is inclusive.
High
Compliance & inclusivity
The primary user stories highlight the diverse needs of key stakeholders interacting with Octopus.AI. Product designers seek fast, AI-assisted layout generation to streamline ideation, while developers prioritise clean and reliable code outputs to minimise debugging. End users value having control over AI outputs to build trust and confidence in the system. Project managers focus on real-time collaboration tools to oversee team workflows effectively, and accessibility advocates emphasise the importance of inclusive design that adheres to compliance standards. Together, these stories underscore the platform’s need for speed, predictability, usability, collaboration, and inclusivity.
Feature Roadmap (with Embedded AI Integration)
Priority
Feature
Description
AI Role
Timeline
01
UI Component Library
Modular and categorised elements for generation and customisation.
Content generation
Week 5-6
02
AI Layout Generator
One click UI layout generation based on user prompt or domain selection.
Generative automation
Week 5-6
03
Personalisation Layer
Allows users to fine-tune AI generated content to reflect branding.
Adaptive personalisation
Week 6-7
04
Real Time Collaboration
Live design and feedback with multi user editing.
No direct AI, but co pilot features planned
Week 7
05
AI Feedback Panel
Feedback mechanism that learns from user corrections to retrain models.
Active learning integration
Week 7
06
Template Customisation
Save and reuse user created templates; AI learns user patterns.
Pattern recognition
Week 7
07
Accessibility Compliance
Built in checkers and suggestions for WCAG 2.1 compliance.
Automated evaluation
Week 7
08
AI Tutorial Assistant
AI chatbot/assistant that walks users through features.
Conversational AI
Week 7
09
Predictive UI Suggestions
Recommends layout tweaks based on industry best practices and previous use.
Predictive modeling
Week 7
10
Analytics Dashboard
User behavior insights to measure success and optimise UX.
Pattern detection, prediction
Week 7
Seamless AI Embedding in User Flows
Seamless AI Embedding
in User Flows
We designed AI infused user flows for critical tasks, ensuring seamless integration of generative, adaptive, and predictive AI.
Key examples -
Flow : Generate New Interface
User enters prompt → AI suggests layout → User reviews → Modify via drag-and-drop or tweak suggestions → Save template.
Flow : Customisation & Override
AI generates default layout → User toggles customisation slider → Edit fonts/colours/components manually → Save preferred version.
Flow : Feedback Loop : User edits AI-generated design → System prompts user for feedback → Feedback logged and used to retrain next-gen models.
Outcomes and Changes Made to IA
Reorganised navigation into workflow-based clusters : Create → Customise → Collaborate → Learn → Evaluate.
Added tooltips and visuals to clarify the difference between components and templates.
Merged tutorials and documentation into a single “Learn” section.
Split settings into Platform Settings and Design Preferences for better clarity.
Moved Feedback and Collaboration tools to top level navigation for easier access.
Final Navigation Structure (Post Sorting Iteration)
Home - Dashboard Overview.
Design Studio - Template Generator, Layout Customiser, AI Preview.
Design Library - UI Components, Layout Patterns, Responsive Frameworks.
Learn - Tutorials, Developer Docs.
Collaborate - Real Time Collaboration, Share Designs.
Feedback - Feedback Portal, AI Suggestions Log.
Evaluate - Analytics, Accessibility Checker.
Settings - Platform Settings.
Primary User Stories
The primary user stories highlight the diverse needs of key stakeholders interacting with Octopus.AI. Product designers seek fast, AI assisted layout generation to streamline ideation, while developers prioritise clean and reliable code outputs to minimise debugging.
End users value having control over AI outputs to build trust and confidence in the system. Project managers focus on real time collaboration tools to oversee team workflows effectively, and accessibility advocates emphasise the importance of inclusive design that adheres to compliance standards.
Together, these stories underscore the platform’s need for speed, predictability, usability, collaboration, and inclusivity.
Feature Roadmap (with Embedded AI Integration)
Seamless AI Embedding
in User Flows
We designed AI infused user flows for critical tasks, ensuring seamless integration of generative, adaptive, and predictive AI.
Key examples -
Flow : Generate New Interface
User enters prompt → AI suggests layout → User reviews → Modify via drag-and-drop or tweak suggestions → Save template.
Flow : Customisation & Override
AI generates default layout → User toggles customisation slider → Edit fonts/colours/components manually → Save preferred version.
Flow : Feedback Loop : User edits AI-generated design → System prompts user for feedback → Feedback logged and used to retrain next-gen models.
UI Component Library and AI Layout Generator were prioritised early (Week 5–6) to enable modular UI creation and instant layout generation via AI.
A Personalisation Layer allows branding-specific customisations using adaptive AI (Week 6–7).
Real-Time Collaboration enables live co designing, with AI co-pilot features planned (Week 7).
The AI Feedback Panel captures user corrections to improve model performance through active learning.
Template Customisation supports reusability and pattern learning from user behavior.
Accessibility Compliance ensures designs meet WCAG 2.1 standards via automated checks.
An AI Tutorial Assistant offers step by step guidance using conversational AI.
Predictive UI Suggestions propose enhancements based on prior use and design principles.
The Analytics Dashboard delivers insights into user behavior and AI effectiveness using predictive analytics.
Developer Collaboration
& AI Predictability
We collaborated closely with developers to -
Define input output structures of AI components to ensure consistency.
Develop validation rules for AI generated UIs (e.g., spacing, alignment, responsiveness).
Create debug logs and transparency overlays to help users understand “why” the AI did what it did.
Trust, Transparency & Control in AI UX
Usability testing revealed that trust in AI increases when users are given clear explanations for its actions. A/B testing showed users preferred AI generated outputs when visual justifications were provided.
Think aloud sessions highlighted the value of transparency through “why this layout” tooltips, while task-based observations showed users expected control features like undo, toggles, and manual overrides. Overall, trust was strongest when users felt informed, empowered, and in control of the AI experience.
Implementation
Delivered comprehensive modular UI libraries fully documented for AI model training.
Integrated UI components with Octopus.AI to dynamically generate interfaces.
Conducted extensive user testing to refine AI-generated outputs based on user feedback, enhancing accuracy and usability.
Proposed Sitemap
Home - Dashboard Overview
UI Generator - Template Selection, Customisation Panel, AI Preview.
Component Library - UI Components, Layout Patterns, Responsive Frameworks.
Tutorials & Documentation - Interactive Tutorials, Developer Docs.
Collaboration & Feedback - Real Time Collaboration, User Feedback Portal.
Analytics - User Insights.
Settings - AI Preferences, Accessibility Settings.
Information Architecture
The Octopus.AI platform features a user-friendly architecture with sections for quick project access, AI powered UI generation, and a rich component library. It includes interactive tutorials, real time collaboration, user feedback tools, and analytics to track usage and AI performance. Customisable settings support AI preferences and accessibility, ensuring a flexible and inclusive design experience.
High Fidelity
Reflecting & Evolvution
Ethical and Inclusive AI Implementation
Ensured AI transparency by clearly indicating AI generated UI elements.
Implemented strict guidelines to prevent biases in UI/UX designs.
Conducted rigorous accessibility testing adhering to WCAG 2.1 standards to ensure inclusivity.
Established user control protocols, enabling manual overrides on AI-generated designs.
Setup for AI Training Handoff
Prepared extensive documentation for training and fine tuning AI models, including interaction patterns, best practices, and troubleshooting guides.
Developed interactive, step by step tutorials for developers and stakeholders.
Provided detailed guidelines on ethical considerations and inclusivity in AI-driven design.
Retrospection and Continuous Improvement
Conducted comprehensive retrospection involving stakeholders, designers, developers, and users to evaluate project effectiveness and identify improvement areas.
Analysed and documented AI performance, comparing human designed interfaces with AI generated outputs.
Created a continuous improvement roadmap highlighting ongoing enhancements in AI capabilities, user experience optimisation, and ethical AI practices.
Planned future iterations emphasising user feedback, technological advancements, and ethical standards maintenance.
Developer Collaboration & AI Predictability
Developer Collaboration
& AI Predictability
We collaborated closely with developers to -
Define input output structures of AI components to ensure consistency.
Develop validation rules for AI generated UIs (e.g., spacing, alignment, responsiveness).
Create debug logs and transparency overlays to help users understand “why” the AI did what it did.
Trust, Transparency & Control in AI UX
Area
Method
Outcome
Trust in AI Output
A/B testing - AI vs manual
Users trusted AI more when outputs were visually explained or justified.
Transparency
Think aloud sessions
Users appreciated “why this layout” tooltips that showed logic behind choices.
Control over AI
Task based observation
Users expected undo/redo, toggle AI suggestions, and manual overrides.
Key Insight
Trust was highest when users felt in control, were educated on why the AI acted a certain way, and could adjust things easily.
Implementation
Delivered comprehensive modular UI libraries fully documented for AI model training.
Integrated UI components with Octopus.AI to dynamically generate interfaces.
Conducted extensive user testing to refine AI-generated outputs based on user feedback, enhancing accuracy and usability.
Proposed Sitemap
Home - Dashboard Overview
UI Generator - Template Selection, Customisation Panel, AI Preview.
Component Library - UI Components, Layout Patterns, Responsive Frameworks.
Tutorials & Documentation - Interactive Tutorials, Developer Docs.
Collaboration & Feedback - Real Time Collaboration, User Feedback Portal.
Analytics - User Insights.
Settings - AI Preferences, Accessibility Settings.
Information Architecture
The Octopus.AI platform features a user-friendly architecture with sections for quick project access, AI powered UI generation, and a rich component library. It includes interactive tutorials, real time collaboration, user feedback tools, and analytics to track usage and AI performance. Customisable settings support AI preferences and accessibility, ensuring a flexible and inclusive design experience.
High Fidelity
High Fidelity
Reflecting & Evolvution
Reflecting & Evolvution
Ethical and Inclusive AI Implementation
Ensured AI transparency by clearly indicating AI generated UI elements.
Implemented strict guidelines to prevent biases in UI/UX designs.
Conducted rigorous accessibility testing adhering to WCAG 2.1 standards to ensure inclusivity.
Established user control protocols, enabling manual overrides on AI-generated designs.
Setup for AI Training Handoff
Prepared extensive documentation for training and fine tuning AI models, including interaction patterns, best practices, and troubleshooting guides.
Developed interactive, step by step tutorials for developers and stakeholders.
Provided detailed guidelines on ethical considerations and inclusivity in AI-driven design.
Retrospection and Continuous Improvement
Conducted comprehensive retrospection involving stakeholders, designers, developers, and users to evaluate project effectiveness and identify improvement areas.
Analysed and documented AI performance, comparing human designed interfaces with AI generated outputs.
Created a continuous improvement roadmap highlighting ongoing enhancements in AI capabilities, user experience optimisation, and ethical AI practices.
Planned future iterations emphasising user feedback, technological advancements, and ethical standards maintenance.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Octopus.AI - Generative AI
Octopus.AI -
Generative AI
Octopus.AI - Generative AI
Octopus.AI (Dalfin AI) leverages cutting-edge generative AI to help anyone, whether you're a developer or non-technical user, create groundbreaking software solutions with ease. By simplifying complex coding tasks, automating repetitive processes, and enabling rapid prototyping, Dalfin AI accelerates the development of custom applications, AI driven solutions, and innovative software.
With no code and low code capabilities, collaborative tools, and seamless integrations, Dalfin AI breaks down traditional barriers to software development, empowering users to transform their ideas into reality faster than ever before.
Octopus.AI (Dalfin AI) leverages cutting edge generative AI to help anyone, whether you're a developer or non-technical user, create groundbreaking software solutions with ease. By simplifying complex coding tasks, automating repetitive processes, and enabling rapid prototyping, Dalfin AI accelerates the development of custom applications, AI driven solutions, and innovative software.
With no code and low code capabilities, collaborative tools, and seamless integrations, Dalfin AI breaks down traditional barriers to software development, empowering users to transform their ideas into reality faster than ever before.
Client
Dalfin
Services
Gen. AI Experience Design
User Interface Design
Roles
Rahul Choudhary - Creative Tech Lead
Jeff Davis - Gen. AI Experience Designer
Twisha Gupta - UX Designer
Date
June 2024
Project Understanding & Goals
Conducted detailed stakeholder interviews and workshops to deeply understand project vision, business goals, and user expectations.
Objectives defined -
Empower users to effortlessly generate highly intuitive and functional software UIs.
Develop flexible and scalable UI templates that AI can effectively use as reference points.
Define success criteria: ease of use, precision, adaptability, and user satisfaction.
Conducted stakeholder interviews and workshops to align on project vision, business goals, and user expectations.
Engaged stakeholders through interviews and workshops to align on the project vision and user needs. Key goals include enabling effortless creation of intuitive UIs, developing scalable templates for AI use, and ensuring success through ease of use, accuracy, and adaptability.
Engaged stakeholders through interviews and workshops to align on the project vision and user needs. Key goals include enabling effortless creation of intuitive UIs, developing scalable templates for AI use, and ensuring success through ease of use, accuracy, and adaptability.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Competitive Analysis
Our review of existing AI UI tools revealed gaps in flexibility, customization, and usability. These insights guided us to build a more adaptive and user-friendly system, where AI supports creative control and delivers high-quality, scalable design outputs.
Our review of existing AI UI tools revealed gaps in flexibility, customization, and usability. These insights guided us to build a more adaptive and user-friendly system, where AI supports creative control and delivers high-quality, scalable design outputs.
Competitor
UX Strengths
UX Weaknesses
Uizard
Rapid prototyping, user friendly
Limited advanced customization features
Figma
Competitor
Powerful design and collaboration tools
Strengths
No direct AI generated UI components
Weaknesses
Zoho Books
Extensive integrations, customizable
Complex interface, steep learning curve
Sketch
Rich component libraries, good user control
Lack of built in AI automation
Adobe XD
Strong prototyping and UI design
Limited integration with generative AI
Canva
Easy to use, extensive template library
Limited for software UI prototyping
What we found
Insights
The analysis highlighted a clear gap in AI driven UI generation among leading tools. While platforms like Uizard and Canva offer ease of use and speed, they lack advanced customisation and are not ideal for software UI prototyping. Figma, Sketch, and Adobe XD provide powerful design capabilities but don’t natively support AI generated components or workflows. These insights underscored the opportunity to create a tool that combines intuitive design with AI automation, bridging the gap between usability, customisation, and intelligent UI generation.
Extended Design Synthesis
Extended Design Synthesis
Extended Design Synthesis
We conducted affinity diagramming sessions using insights from stakeholder interviews, workshops, and competitor benchmarking. Key insights were grouped under major themes such as:
User Expectations from AI generated UIs.
Challenges in Trusting Automated Design Outputs.
Desired Control and Customisation Levels.
Developer Pain Points in Integrating AI Outputs.
UI/UX Best Practices Relevant to AI.
These were then prioritized based on frequency, impact, and feasibility, which informed the next phases of feature ideation and user flow development.
We developed core and extended use cases across user personas:
Core Use Cases
Generate a new UI layout from scratch using AI suggestions.
Customise AI generated templates based on domain-specific needs.
Provide real-time feedback to fine-tune future AI outputs.
Collaborate with team members in real time on generated UIs.
Extended Use Cases
Override AI generated designs manually.
Analyse usage patterns and UI performance using analytics.
Learn to use the platform via guided interactive tutorials.
Each use case was mapped with its entry point, AI interaction layer, and desired outcome, forming the foundation of user flow diagrams.
Card Sorting for Navigation
Card Sorting for Navigation
Card Sorting for Navigation
To validate the platform's navigation structure and terminology by understanding how users naturally categorize and interpret features and sections of the product.
Type of Card Sorting -
We conducted both open card sorting (where participants grouped features freely and labeled categories themselves) and closed card sorting (where they sorted predefined cards into existing category names).
Participants:
8 Product Designers
5 Frontend Developers
3 Project Managers
4 Tech-savvy End Users
(Total : 20 participants)
Tool Used - Miro for remote sessions, physical cards for in-person workshops
Each card represented a distinct feature, task, or screen on the platform. Examples included -
Generate UI from prompt
Select a template
Edit generated layout
View analytics
Access tutorial walkthrough
Change AI preferences
Give feedback on UI
Access documentation
Customise colour scheme
View component library
Share design with team
Check accessibility compliance
Key Patterns Identified from Open Card Sorting
Groupings by Task Flow - Most users grouped cards by sequential workflows :
Creation (e.g., Generate UI, Choose Template, Customise)
Learning (e.g., Tutorials, Documentation)
Review & Feedback (e.g., Feedback Portal, Accessibility Checker, Analytics)
Overlap Between Customisation & Settings -
Users frequently placed “Change AI preferences,” “Customise colour scheme,” and “Accessibility settings” in both “Settings” and “Customisation.”
Decision : Split settings into General Settings and Design Customisation.
Navigation Language Insights -
Users preferred intuitive, task-based terms:
“UI Builder” or “Design Studio” over “UI Generator”
“Design Settings” over “AI Preferences”
“Design Library” over “Component Library”
Component vs Template Confusion -
Many grouped components and templates together.
Decision: Include contextual help text and tooltips in UI to clarify - “Templates are pre-designed full screen layouts. Components are reusable UI elements like buttons or forms.”
Feedback Loop Perception - Users associated the feedback portal with both “Support” and “AI Improvement,” suggesting its dual role as both utility and contribution tool.
Navigation Language Insights -
Users preferred intuitive, task based terms :
“UI Builder” or “Design Studio” over “UI Generator”
“Design Settings” over “AI Preferences”
“Design Library” over “Component Library”
Component vs Template Confusion -
Many grouped components and templates together.
Decision : Include contextual help text and tooltips in UI to clarify - “Templates are pre-designed full screen layouts. Components are reusable UI elements like buttons or forms.”
Feedback Loop Perception - Users associated the feedback portal with both “Support” and “AI Improvement,” suggesting its dual role as both utility and contribution tool.
Outcomes and Changes Made to IA
Insight
Action Taken
Users think in task-based flows, not features
Reorganized IA into workflow aligned clusters : Create → Customise → Collaborate → Learn → Evaluate
Confusion between components and templates
Added microcopy/tooltips for clarification + visual examples
Tutorials and documentation were expected under one label
Merged into a unified “Learn” section
Settings perceived as too broad
Split into “Platform Settings” and “Design Preferences”
Users wanted easy access to feedback and collaboration tools
Moved “Feedback” and “Collaboration” into top level navigation items
Final Navigation Structure (Post Sorting Iteration)
Home - Dashboard Overview.
Design Studio - Template Generator, Layout Customiser, AI Preview.
Design Library - UI Components, Layout Patterns, Responsive Frameworks.
Learn - Tutorials, Developer Docs.
Collaborate - Real Time Collaboration, Share Designs.
Feedback - Feedback Portal, AI Suggestions Log.
Evaluate - Analytics, Accessibility Checker.
Settings - Platform Settings.
Primary User Stories
ID
Role
User Story
Priority
Benefit
01
Product Designer
As a designer, I want to generate a UI layout using AI so I can quickly iterate on ideas.
High
Speed & ideation
02
Developer
As a developer, I want predictable and clean AI generated code so I can avoid debugging overhead.
High
Seamless handoff
03
End User
As a user, I want to control and edit the AI output so I feel confident using it.
High
Trust & usability
04
Project Manager
As a manager, I want collaboration tools to monitor and review team progress in real time.
Medium
Visibility & collaboration
05
Accessibility Advocate
As a user, I want UI designs to meet accessibility standards so our product is inclusive.
High
Compliance & inclusivity
The primary user stories highlight the diverse needs of key stakeholders interacting with Octopus.AI. Product designers seek fast, AI-assisted layout generation to streamline ideation, while developers prioritise clean and reliable code outputs to minimise debugging. End users value having control over AI outputs to build trust and confidence in the system. Project managers focus on real-time collaboration tools to oversee team workflows effectively, and accessibility advocates emphasise the importance of inclusive design that adheres to compliance standards. Together, these stories underscore the platform’s need for speed, predictability, usability, collaboration, and inclusivity.
Feature Roadmap (with Embedded AI Integration)
Priority
Feature
Description
AI Role
Timeline
01
UI Component Library
Modular and categorised elements for generation and customisation.
Content generation
Week 5-6
02
AI Layout Generator
One click UI layout generation based on user prompt or domain selection.
Generative automation
Week 5-6
03
Personalisation Layer
Allows users to fine-tune AI generated content to reflect branding.
Adaptive personalisation
Week 6-7
04
Real Time Collaboration
Live design and feedback with multi user editing.
No direct AI, but co pilot features planned
Week 7
05
AI Feedback Panel
Feedback mechanism that learns from user corrections to retrain models.
Active learning integration
Week 7
06
Template Customisation
Save and reuse user created templates; AI learns user patterns.
Pattern recognition
Week 7
07
Accessibility Compliance
Built in checkers and suggestions for WCAG 2.1 compliance.
Automated evaluation
Week 7
08
AI Tutorial Assistant
AI chatbot/assistant that walks users through features.
Conversational AI
Week 7
09
Predictive UI Suggestions
Recommends layout tweaks based on industry best practices and previous use.
Predictive modeling
Week 7
10
Analytics Dashboard
User behavior insights to measure success and optimise UX.
Pattern detection, prediction
Week 7
Seamless AI Embedding in User Flows
Seamless AI Embedding
in User Flows
We designed AI infused user flows for critical tasks, ensuring seamless integration of generative, adaptive, and predictive AI.
Key examples -
Flow : Generate New Interface
User enters prompt → AI suggests layout → User reviews → Modify via drag-and-drop or tweak suggestions → Save template.
Flow : Customisation & Override
AI generates default layout → User toggles customisation slider → Edit fonts/colours/components manually → Save preferred version.
Flow : Feedback Loop : User edits AI-generated design → System prompts user for feedback → Feedback logged and used to retrain next-gen models.
Outcomes and Changes Made to IA
Reorganised navigation into workflow-based clusters : Create → Customise → Collaborate → Learn → Evaluate.
Added tooltips and visuals to clarify the difference between components and templates.
Merged tutorials and documentation into a single “Learn” section.
Split settings into Platform Settings and Design Preferences for better clarity.
Moved Feedback and Collaboration tools to top level navigation for easier access.
Final Navigation Structure (Post Sorting Iteration)
Home - Dashboard Overview.
Design Studio - Template Generator, Layout Customiser, AI Preview.
Design Library - UI Components, Layout Patterns, Responsive Frameworks.
Learn - Tutorials, Developer Docs.
Collaborate - Real Time Collaboration, Share Designs.
Feedback - Feedback Portal, AI Suggestions Log.
Evaluate - Analytics, Accessibility Checker.
Settings - Platform Settings.
Primary User Stories
The primary user stories highlight the diverse needs of key stakeholders interacting with Octopus.AI. Product designers seek fast, AI assisted layout generation to streamline ideation, while developers prioritise clean and reliable code outputs to minimise debugging.
End users value having control over AI outputs to build trust and confidence in the system. Project managers focus on real time collaboration tools to oversee team workflows effectively, and accessibility advocates emphasise the importance of inclusive design that adheres to compliance standards.
Together, these stories underscore the platform’s need for speed, predictability, usability, collaboration, and inclusivity.
Feature Roadmap (with Embedded AI Integration)
Seamless AI Embedding
in User Flows
We designed AI infused user flows for critical tasks, ensuring seamless integration of generative, adaptive, and predictive AI.
Key examples -
Flow : Generate New Interface
User enters prompt → AI suggests layout → User reviews → Modify via drag-and-drop or tweak suggestions → Save template.
Flow : Customisation & Override
AI generates default layout → User toggles customisation slider → Edit fonts/colours/components manually → Save preferred version.
Flow : Feedback Loop : User edits AI-generated design → System prompts user for feedback → Feedback logged and used to retrain next-gen models.
UI Component Library and AI Layout Generator were prioritised early (Week 5–6) to enable modular UI creation and instant layout generation via AI.
A Personalisation Layer allows branding-specific customisations using adaptive AI (Week 6–7).
Real-Time Collaboration enables live co designing, with AI co-pilot features planned (Week 7).
The AI Feedback Panel captures user corrections to improve model performance through active learning.
Template Customisation supports reusability and pattern learning from user behavior.
Accessibility Compliance ensures designs meet WCAG 2.1 standards via automated checks.
An AI Tutorial Assistant offers step by step guidance using conversational AI.
Predictive UI Suggestions propose enhancements based on prior use and design principles.
The Analytics Dashboard delivers insights into user behavior and AI effectiveness using predictive analytics.
Developer Collaboration
& AI Predictability
We collaborated closely with developers to -
Define input output structures of AI components to ensure consistency.
Develop validation rules for AI generated UIs (e.g., spacing, alignment, responsiveness).
Create debug logs and transparency overlays to help users understand “why” the AI did what it did.
Trust, Transparency & Control in AI UX
Usability testing revealed that trust in AI increases when users are given clear explanations for its actions. A/B testing showed users preferred AI generated outputs when visual justifications were provided.
Think aloud sessions highlighted the value of transparency through “why this layout” tooltips, while task-based observations showed users expected control features like undo, toggles, and manual overrides. Overall, trust was strongest when users felt informed, empowered, and in control of the AI experience.
Implementation
Delivered comprehensive modular UI libraries fully documented for AI model training.
Integrated UI components with Octopus.AI to dynamically generate interfaces.
Conducted extensive user testing to refine AI-generated outputs based on user feedback, enhancing accuracy and usability.
Proposed Sitemap
Home - Dashboard Overview
UI Generator - Template Selection, Customisation Panel, AI Preview.
Component Library - UI Components, Layout Patterns, Responsive Frameworks.
Tutorials & Documentation - Interactive Tutorials, Developer Docs.
Collaboration & Feedback - Real Time Collaboration, User Feedback Portal.
Analytics - User Insights.
Settings - AI Preferences, Accessibility Settings.
Information Architecture
The Octopus.AI platform features a user-friendly architecture with sections for quick project access, AI powered UI generation, and a rich component library. It includes interactive tutorials, real time collaboration, user feedback tools, and analytics to track usage and AI performance. Customisable settings support AI preferences and accessibility, ensuring a flexible and inclusive design experience.
High Fidelity
Reflecting & Evolvution
Ethical and Inclusive AI Implementation
Ensured AI transparency by clearly indicating AI generated UI elements.
Implemented strict guidelines to prevent biases in UI/UX designs.
Conducted rigorous accessibility testing adhering to WCAG 2.1 standards to ensure inclusivity.
Established user control protocols, enabling manual overrides on AI-generated designs.
Setup for AI Training Handoff
Prepared extensive documentation for training and fine tuning AI models, including interaction patterns, best practices, and troubleshooting guides.
Developed interactive, step by step tutorials for developers and stakeholders.
Provided detailed guidelines on ethical considerations and inclusivity in AI-driven design.
Retrospection and Continuous Improvement
Conducted comprehensive retrospection involving stakeholders, designers, developers, and users to evaluate project effectiveness and identify improvement areas.
Analysed and documented AI performance, comparing human designed interfaces with AI generated outputs.
Created a continuous improvement roadmap highlighting ongoing enhancements in AI capabilities, user experience optimisation, and ethical AI practices.
Planned future iterations emphasising user feedback, technological advancements, and ethical standards maintenance.
Developer Collaboration & AI Predictability
Developer Collaboration
& AI Predictability
We collaborated closely with developers to -
Define input output structures of AI components to ensure consistency.
Develop validation rules for AI generated UIs (e.g., spacing, alignment, responsiveness).
Create debug logs and transparency overlays to help users understand “why” the AI did what it did.
Trust, Transparency & Control in AI UX
Area
Method
Outcome
Trust in AI Output
A/B testing - AI vs manual
Users trusted AI more when outputs were visually explained or justified.
Transparency
Think aloud sessions
Users appreciated “why this layout” tooltips that showed logic behind choices.
Control over AI
Task based observation
Users expected undo/redo, toggle AI suggestions, and manual overrides.
Key Insight
Trust was highest when users felt in control, were educated on why the AI acted a certain way, and could adjust things easily.
Implementation
Delivered comprehensive modular UI libraries fully documented for AI model training.
Integrated UI components with Octopus.AI to dynamically generate interfaces.
Conducted extensive user testing to refine AI-generated outputs based on user feedback, enhancing accuracy and usability.
Proposed Sitemap
Home - Dashboard Overview
UI Generator - Template Selection, Customisation Panel, AI Preview.
Component Library - UI Components, Layout Patterns, Responsive Frameworks.
Tutorials & Documentation - Interactive Tutorials, Developer Docs.
Collaboration & Feedback - Real Time Collaboration, User Feedback Portal.
Analytics - User Insights.
Settings - AI Preferences, Accessibility Settings.
Information Architecture
The Octopus.AI platform features a user-friendly architecture with sections for quick project access, AI powered UI generation, and a rich component library. It includes interactive tutorials, real time collaboration, user feedback tools, and analytics to track usage and AI performance. Customisable settings support AI preferences and accessibility, ensuring a flexible and inclusive design experience.
High Fidelity
High Fidelity
Reflecting & Evolvution
Reflecting & Evolvution
Ethical and Inclusive AI Implementation
Ensured AI transparency by clearly indicating AI generated UI elements.
Implemented strict guidelines to prevent biases in UI/UX designs.
Conducted rigorous accessibility testing adhering to WCAG 2.1 standards to ensure inclusivity.
Established user control protocols, enabling manual overrides on AI-generated designs.
Setup for AI Training Handoff
Prepared extensive documentation for training and fine tuning AI models, including interaction patterns, best practices, and troubleshooting guides.
Developed interactive, step by step tutorials for developers and stakeholders.
Provided detailed guidelines on ethical considerations and inclusivity in AI-driven design.
Retrospection and Continuous Improvement
Conducted comprehensive retrospection involving stakeholders, designers, developers, and users to evaluate project effectiveness and identify improvement areas.
Analysed and documented AI performance, comparing human designed interfaces with AI generated outputs.
Created a continuous improvement roadmap highlighting ongoing enhancements in AI capabilities, user experience optimisation, and ethical AI practices.
Planned future iterations emphasising user feedback, technological advancements, and ethical standards maintenance.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Octopus.AI - Generative AI
Octopus.AI -
Generative AI
Octopus.AI - Generative AI
Octopus.AI (Dalfin AI) leverages cutting-edge generative AI to help anyone, whether you're a developer or non-technical user, create groundbreaking software solutions with ease. By simplifying complex coding tasks, automating repetitive processes, and enabling rapid prototyping, Dalfin AI accelerates the development of custom applications, AI driven solutions, and innovative software.
With no code and low code capabilities, collaborative tools, and seamless integrations, Dalfin AI breaks down traditional barriers to software development, empowering users to transform their ideas into reality faster than ever before.
Octopus.AI (Dalfin AI) leverages cutting edge generative AI to help anyone, whether you're a developer or non-technical user, create groundbreaking software solutions with ease. By simplifying complex coding tasks, automating repetitive processes, and enabling rapid prototyping, Dalfin AI accelerates the development of custom applications, AI driven solutions, and innovative software.
With no code and low code capabilities, collaborative tools, and seamless integrations, Dalfin AI breaks down traditional barriers to software development, empowering users to transform their ideas into reality faster than ever before.
Client
Dalfin
Services
Gen. AI Experience Design
User Interface Design
Roles
Rahul Choudhary - Creative Tech Lead
Jeff Davis - Gen. AI Experience Designer
Twisha Gupta - UX Designer
Date
June 2024
Project Understanding & Goals
Conducted detailed stakeholder interviews and workshops to deeply understand project vision, business goals, and user expectations.
Objectives defined -
Empower users to effortlessly generate highly intuitive and functional software UIs.
Develop flexible and scalable UI templates that AI can effectively use as reference points.
Define success criteria: ease of use, precision, adaptability, and user satisfaction.
Conducted stakeholder interviews and workshops to align on project vision, business goals, and user expectations.
Engaged stakeholders through interviews and workshops to align on the project vision and user needs. Key goals include enabling effortless creation of intuitive UIs, developing scalable templates for AI use, and ensuring success through ease of use, accuracy, and adaptability.
Engaged stakeholders through interviews and workshops to align on the project vision and user needs. Key goals include enabling effortless creation of intuitive UIs, developing scalable templates for AI use, and ensuring success through ease of use, accuracy, and adaptability.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Challenges
Inconsistencies in AI generated user interfaces. AI limitations in understanding UX/UI principles. Need for personalized user customizations.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Solution
Created diverse and comprehensive UI model libraries. Developed detailed UI/UX guidelines and frameworks. Built highly customizable and adaptive UI templates.
Competitive Analysis
Our review of existing AI UI tools revealed gaps in flexibility, customization, and usability. These insights guided us to build a more adaptive and user-friendly system, where AI supports creative control and delivers high-quality, scalable design outputs.
Our review of existing AI UI tools revealed gaps in flexibility, customization, and usability. These insights guided us to build a more adaptive and user-friendly system, where AI supports creative control and delivers high-quality, scalable design outputs.
Competitor
UX Strengths
UX Weaknesses
Uizard
Rapid prototyping, user friendly
Limited advanced customization features
Figma
Competitor
Powerful design and collaboration tools
Strengths
No direct AI generated UI components
Weaknesses
Zoho Books
Extensive integrations, customizable
Complex interface, steep learning curve
Sketch
Rich component libraries, good user control
Lack of built in AI automation
Adobe XD
Strong prototyping and UI design
Limited integration with generative AI
Canva
Easy to use, extensive template library
Limited for software UI prototyping
What we found
Insights
The analysis highlighted a clear gap in AI driven UI generation among leading tools. While platforms like Uizard and Canva offer ease of use and speed, they lack advanced customisation and are not ideal for software UI prototyping. Figma, Sketch, and Adobe XD provide powerful design capabilities but don’t natively support AI generated components or workflows. These insights underscored the opportunity to create a tool that combines intuitive design with AI automation, bridging the gap between usability, customisation, and intelligent UI generation.
Extended Design Synthesis
Extended Design Synthesis
Extended Design Synthesis
We conducted affinity diagramming sessions using insights from stakeholder interviews, workshops, and competitor benchmarking. Key insights were grouped under major themes such as:
User Expectations from AI generated UIs.
Challenges in Trusting Automated Design Outputs.
Desired Control and Customisation Levels.
Developer Pain Points in Integrating AI Outputs.
UI/UX Best Practices Relevant to AI.
These were then prioritized based on frequency, impact, and feasibility, which informed the next phases of feature ideation and user flow development.
We developed core and extended use cases across user personas:
Core Use Cases
Generate a new UI layout from scratch using AI suggestions.
Customise AI generated templates based on domain-specific needs.
Provide real-time feedback to fine-tune future AI outputs.
Collaborate with team members in real time on generated UIs.
Extended Use Cases
Override AI generated designs manually.
Analyse usage patterns and UI performance using analytics.
Learn to use the platform via guided interactive tutorials.
Each use case was mapped with its entry point, AI interaction layer, and desired outcome, forming the foundation of user flow diagrams.
Card Sorting for Navigation
Card Sorting for Navigation
Card Sorting for Navigation
To validate the platform's navigation structure and terminology by understanding how users naturally categorize and interpret features and sections of the product.
Type of Card Sorting -
We conducted both open card sorting (where participants grouped features freely and labeled categories themselves) and closed card sorting (where they sorted predefined cards into existing category names).
Participants:
8 Product Designers
5 Frontend Developers
3 Project Managers
4 Tech-savvy End Users
(Total : 20 participants)
Tool Used - Miro for remote sessions, physical cards for in-person workshops
Each card represented a distinct feature, task, or screen on the platform. Examples included -
Generate UI from prompt
Select a template
Edit generated layout
View analytics
Access tutorial walkthrough
Change AI preferences
Give feedback on UI
Access documentation
Customise colour scheme
View component library
Share design with team
Check accessibility compliance
Key Patterns Identified from Open Card Sorting
Groupings by Task Flow - Most users grouped cards by sequential workflows :
Creation (e.g., Generate UI, Choose Template, Customise)
Learning (e.g., Tutorials, Documentation)
Review & Feedback (e.g., Feedback Portal, Accessibility Checker, Analytics)
Overlap Between Customisation & Settings -
Users frequently placed “Change AI preferences,” “Customise colour scheme,” and “Accessibility settings” in both “Settings” and “Customisation.”
Decision : Split settings into General Settings and Design Customisation.
Navigation Language Insights -
Users preferred intuitive, task-based terms:
“UI Builder” or “Design Studio” over “UI Generator”
“Design Settings” over “AI Preferences”
“Design Library” over “Component Library”
Component vs Template Confusion -
Many grouped components and templates together.
Decision: Include contextual help text and tooltips in UI to clarify - “Templates are pre-designed full screen layouts. Components are reusable UI elements like buttons or forms.”
Feedback Loop Perception - Users associated the feedback portal with both “Support” and “AI Improvement,” suggesting its dual role as both utility and contribution tool.
Navigation Language Insights -
Users preferred intuitive, task based terms :
“UI Builder” or “Design Studio” over “UI Generator”
“Design Settings” over “AI Preferences”
“Design Library” over “Component Library”
Component vs Template Confusion -
Many grouped components and templates together.
Decision : Include contextual help text and tooltips in UI to clarify - “Templates are pre-designed full screen layouts. Components are reusable UI elements like buttons or forms.”
Feedback Loop Perception - Users associated the feedback portal with both “Support” and “AI Improvement,” suggesting its dual role as both utility and contribution tool.
Outcomes and Changes Made to IA
Insight
Action Taken
Users think in task-based flows, not features
Reorganized IA into workflow aligned clusters : Create → Customise → Collaborate → Learn → Evaluate
Confusion between components and templates
Added microcopy/tooltips for clarification + visual examples
Tutorials and documentation were expected under one label
Merged into a unified “Learn” section
Settings perceived as too broad
Split into “Platform Settings” and “Design Preferences”
Users wanted easy access to feedback and collaboration tools
Moved “Feedback” and “Collaboration” into top level navigation items
Final Navigation Structure (Post Sorting Iteration)
Home - Dashboard Overview.
Design Studio - Template Generator, Layout Customiser, AI Preview.
Design Library - UI Components, Layout Patterns, Responsive Frameworks.
Learn - Tutorials, Developer Docs.
Collaborate - Real Time Collaboration, Share Designs.
Feedback - Feedback Portal, AI Suggestions Log.
Evaluate - Analytics, Accessibility Checker.
Settings - Platform Settings.
Primary User Stories
ID
Role
User Story
Priority
Benefit
01
Product Designer
As a designer, I want to generate a UI layout using AI so I can quickly iterate on ideas.
High
Speed & ideation
02
Developer
As a developer, I want predictable and clean AI generated code so I can avoid debugging overhead.
High
Seamless handoff
03
End User
As a user, I want to control and edit the AI output so I feel confident using it.
High
Trust & usability
04
Project Manager
As a manager, I want collaboration tools to monitor and review team progress in real time.
Medium
Visibility & collaboration
05
Accessibility Advocate
As a user, I want UI designs to meet accessibility standards so our product is inclusive.
High
Compliance & inclusivity
The primary user stories highlight the diverse needs of key stakeholders interacting with Octopus.AI. Product designers seek fast, AI-assisted layout generation to streamline ideation, while developers prioritise clean and reliable code outputs to minimise debugging. End users value having control over AI outputs to build trust and confidence in the system. Project managers focus on real-time collaboration tools to oversee team workflows effectively, and accessibility advocates emphasise the importance of inclusive design that adheres to compliance standards. Together, these stories underscore the platform’s need for speed, predictability, usability, collaboration, and inclusivity.
Feature Roadmap (with Embedded AI Integration)
Priority
Feature
Description
AI Role
Timeline
01
UI Component Library
Modular and categorised elements for generation and customisation.
Content generation
Week 5-6
02
AI Layout Generator
One click UI layout generation based on user prompt or domain selection.
Generative automation
Week 5-6
03
Personalisation Layer
Allows users to fine-tune AI generated content to reflect branding.
Adaptive personalisation
Week 6-7
04
Real Time Collaboration
Live design and feedback with multi user editing.
No direct AI, but co pilot features planned
Week 7
05
AI Feedback Panel
Feedback mechanism that learns from user corrections to retrain models.
Active learning integration
Week 7
06
Template Customisation
Save and reuse user created templates; AI learns user patterns.
Pattern recognition
Week 7
07
Accessibility Compliance
Built in checkers and suggestions for WCAG 2.1 compliance.
Automated evaluation
Week 7
08
AI Tutorial Assistant
AI chatbot/assistant that walks users through features.
Conversational AI
Week 7
09
Predictive UI Suggestions
Recommends layout tweaks based on industry best practices and previous use.
Predictive modeling
Week 7
10
Analytics Dashboard
User behavior insights to measure success and optimise UX.
Pattern detection, prediction
Week 7
Seamless AI Embedding in User Flows
Seamless AI Embedding
in User Flows
We designed AI infused user flows for critical tasks, ensuring seamless integration of generative, adaptive, and predictive AI.
Key examples -
Flow : Generate New Interface
User enters prompt → AI suggests layout → User reviews → Modify via drag-and-drop or tweak suggestions → Save template.
Flow : Customisation & Override
AI generates default layout → User toggles customisation slider → Edit fonts/colours/components manually → Save preferred version.
Flow : Feedback Loop : User edits AI-generated design → System prompts user for feedback → Feedback logged and used to retrain next-gen models.
Outcomes and Changes Made to IA
Reorganised navigation into workflow-based clusters : Create → Customise → Collaborate → Learn → Evaluate.
Added tooltips and visuals to clarify the difference between components and templates.
Merged tutorials and documentation into a single “Learn” section.
Split settings into Platform Settings and Design Preferences for better clarity.
Moved Feedback and Collaboration tools to top level navigation for easier access.
Final Navigation Structure (Post Sorting Iteration)
Home - Dashboard Overview.
Design Studio - Template Generator, Layout Customiser, AI Preview.
Design Library - UI Components, Layout Patterns, Responsive Frameworks.
Learn - Tutorials, Developer Docs.
Collaborate - Real Time Collaboration, Share Designs.
Feedback - Feedback Portal, AI Suggestions Log.
Evaluate - Analytics, Accessibility Checker.
Settings - Platform Settings.
Primary User Stories
The primary user stories highlight the diverse needs of key stakeholders interacting with Octopus.AI. Product designers seek fast, AI assisted layout generation to streamline ideation, while developers prioritise clean and reliable code outputs to minimise debugging.
End users value having control over AI outputs to build trust and confidence in the system. Project managers focus on real time collaboration tools to oversee team workflows effectively, and accessibility advocates emphasise the importance of inclusive design that adheres to compliance standards.
Together, these stories underscore the platform’s need for speed, predictability, usability, collaboration, and inclusivity.
Feature Roadmap (with Embedded AI Integration)
Seamless AI Embedding
in User Flows
We designed AI infused user flows for critical tasks, ensuring seamless integration of generative, adaptive, and predictive AI.
Key examples -
Flow : Generate New Interface
User enters prompt → AI suggests layout → User reviews → Modify via drag-and-drop or tweak suggestions → Save template.
Flow : Customisation & Override
AI generates default layout → User toggles customisation slider → Edit fonts/colours/components manually → Save preferred version.
Flow : Feedback Loop : User edits AI-generated design → System prompts user for feedback → Feedback logged and used to retrain next-gen models.
UI Component Library and AI Layout Generator were prioritised early (Week 5–6) to enable modular UI creation and instant layout generation via AI.
A Personalisation Layer allows branding-specific customisations using adaptive AI (Week 6–7).
Real-Time Collaboration enables live co designing, with AI co-pilot features planned (Week 7).
The AI Feedback Panel captures user corrections to improve model performance through active learning.
Template Customisation supports reusability and pattern learning from user behavior.
Accessibility Compliance ensures designs meet WCAG 2.1 standards via automated checks.
An AI Tutorial Assistant offers step by step guidance using conversational AI.
Predictive UI Suggestions propose enhancements based on prior use and design principles.
The Analytics Dashboard delivers insights into user behavior and AI effectiveness using predictive analytics.
Developer Collaboration
& AI Predictability
We collaborated closely with developers to -
Define input output structures of AI components to ensure consistency.
Develop validation rules for AI generated UIs (e.g., spacing, alignment, responsiveness).
Create debug logs and transparency overlays to help users understand “why” the AI did what it did.
Trust, Transparency & Control in AI UX
Usability testing revealed that trust in AI increases when users are given clear explanations for its actions. A/B testing showed users preferred AI generated outputs when visual justifications were provided.
Think aloud sessions highlighted the value of transparency through “why this layout” tooltips, while task-based observations showed users expected control features like undo, toggles, and manual overrides. Overall, trust was strongest when users felt informed, empowered, and in control of the AI experience.
Implementation
Delivered comprehensive modular UI libraries fully documented for AI model training.
Integrated UI components with Octopus.AI to dynamically generate interfaces.
Conducted extensive user testing to refine AI-generated outputs based on user feedback, enhancing accuracy and usability.
Proposed Sitemap
Home - Dashboard Overview
UI Generator - Template Selection, Customisation Panel, AI Preview.
Component Library - UI Components, Layout Patterns, Responsive Frameworks.
Tutorials & Documentation - Interactive Tutorials, Developer Docs.
Collaboration & Feedback - Real Time Collaboration, User Feedback Portal.
Analytics - User Insights.
Settings - AI Preferences, Accessibility Settings.
Information Architecture
The Octopus.AI platform features a user-friendly architecture with sections for quick project access, AI powered UI generation, and a rich component library. It includes interactive tutorials, real time collaboration, user feedback tools, and analytics to track usage and AI performance. Customisable settings support AI preferences and accessibility, ensuring a flexible and inclusive design experience.
High Fidelity
Reflecting & Evolvution
Ethical and Inclusive AI Implementation
Ensured AI transparency by clearly indicating AI generated UI elements.
Implemented strict guidelines to prevent biases in UI/UX designs.
Conducted rigorous accessibility testing adhering to WCAG 2.1 standards to ensure inclusivity.
Established user control protocols, enabling manual overrides on AI-generated designs.
Setup for AI Training Handoff
Prepared extensive documentation for training and fine tuning AI models, including interaction patterns, best practices, and troubleshooting guides.
Developed interactive, step by step tutorials for developers and stakeholders.
Provided detailed guidelines on ethical considerations and inclusivity in AI-driven design.
Retrospection and Continuous Improvement
Conducted comprehensive retrospection involving stakeholders, designers, developers, and users to evaluate project effectiveness and identify improvement areas.
Analysed and documented AI performance, comparing human designed interfaces with AI generated outputs.
Created a continuous improvement roadmap highlighting ongoing enhancements in AI capabilities, user experience optimisation, and ethical AI practices.
Planned future iterations emphasising user feedback, technological advancements, and ethical standards maintenance.
Developer Collaboration & AI Predictability
Developer Collaboration
& AI Predictability
We collaborated closely with developers to -
Define input output structures of AI components to ensure consistency.
Develop validation rules for AI generated UIs (e.g., spacing, alignment, responsiveness).
Create debug logs and transparency overlays to help users understand “why” the AI did what it did.
Trust, Transparency & Control in AI UX
Area
Method
Outcome
Trust in AI Output
A/B testing - AI vs manual
Users trusted AI more when outputs were visually explained or justified.
Transparency
Think aloud sessions
Users appreciated “why this layout” tooltips that showed logic behind choices.
Control over AI
Task based observation
Users expected undo/redo, toggle AI suggestions, and manual overrides.
Key Insight
Trust was highest when users felt in control, were educated on why the AI acted a certain way, and could adjust things easily.
Implementation
Delivered comprehensive modular UI libraries fully documented for AI model training.
Integrated UI components with Octopus.AI to dynamically generate interfaces.
Conducted extensive user testing to refine AI-generated outputs based on user feedback, enhancing accuracy and usability.
Proposed Sitemap
Home - Dashboard Overview
UI Generator - Template Selection, Customisation Panel, AI Preview.
Component Library - UI Components, Layout Patterns, Responsive Frameworks.
Tutorials & Documentation - Interactive Tutorials, Developer Docs.
Collaboration & Feedback - Real Time Collaboration, User Feedback Portal.
Analytics - User Insights.
Settings - AI Preferences, Accessibility Settings.
Information Architecture
The Octopus.AI platform features a user-friendly architecture with sections for quick project access, AI powered UI generation, and a rich component library. It includes interactive tutorials, real time collaboration, user feedback tools, and analytics to track usage and AI performance. Customisable settings support AI preferences and accessibility, ensuring a flexible and inclusive design experience.
High Fidelity
High Fidelity
Reflecting & Evolvution
Reflecting & Evolvution
Ethical and Inclusive AI Implementation
Ensured AI transparency by clearly indicating AI generated UI elements.
Implemented strict guidelines to prevent biases in UI/UX designs.
Conducted rigorous accessibility testing adhering to WCAG 2.1 standards to ensure inclusivity.
Established user control protocols, enabling manual overrides on AI-generated designs.
Setup for AI Training Handoff
Prepared extensive documentation for training and fine tuning AI models, including interaction patterns, best practices, and troubleshooting guides.
Developed interactive, step by step tutorials for developers and stakeholders.
Provided detailed guidelines on ethical considerations and inclusivity in AI-driven design.
Retrospection and Continuous Improvement
Conducted comprehensive retrospection involving stakeholders, designers, developers, and users to evaluate project effectiveness and identify improvement areas.
Analysed and documented AI performance, comparing human designed interfaces with AI generated outputs.
Created a continuous improvement roadmap highlighting ongoing enhancements in AI capabilities, user experience optimisation, and ethical AI practices.
Planned future iterations emphasising user feedback, technological advancements, and ethical standards maintenance.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.
Your Next Step Starts Here
Got a bold idea or a tricky problem? We’re here to help. We work with individuals, startups, and businesses to design solutions that matter. Let’s team up and build something great together.