Digitalisation of Design Codes
Client: Ministry of Housing and Local Government
Role: Interaction Designer and Lead Researcher
TLDR: Design Codes are hard to write, find and apply. We built and tested an AI-assisted writing tool to improve clarity and consistency of Design codes along with a map based search to facilitate the process of finding them.
Design Codes
Challenge
Design codes are often written in vague, inconsistent language, making it difficult for developers to determine compliance with policy. This causes delays and inefficiencies in the planning process.
Currently, there is no service that allows users to easily search for or interpret design codes.
Solutions
Through research into the needs of both design code writers and users, we:
- Developed AI-assisted writing tools to standardise design codes and provide contextual guidance.
- Created low-cost image generation and annotation tools to help visualise design codes.
- Built and tested a public-facing, map-based search tool that enables users to find and understand applicable design codes, streamlining the planning process.
Outcome
We validated the concept for a service that helps Local Planning Authorities (LPAs) draft clear, actionable design requirements and publish them in an accessible digital format.
With further funding and development into a private beta, this service could improve compliance, increase submission quality, and lead to better design outcomes.
How might we enable urban planners to create and apply standardised planning codes that simplify approvals and accelerate housing development across the UK?
Individual Skills Showcased
User Research
Design System
integration
AI Prototyping
Concept testing
Usability testing
Qual Analysis
Service mapping
Jump to:
Objectives
Context
Discovery Work
Prototyping & testing assumptions
UR
MVP Service Design
Conclusion
Outcomes
Client Objectives
1
Design and test a new service for creating better Design Codes
Map user journeys, build prototype screens, and test concepts to support code writers in producing clearer, more consistent guidance.
2
Understand user groups and their needs
Identify and map the different types of users — those who write codes and those who use them — through research and prototype testing.
3
Define what the MVP could look like
Explore service and technical feasibility and outline the core features needed for an initial minimum viable product.
Context
What are Design Codes — and what’s wrong with them?
Design Codes set the rules/policy for how places are built — things like how tall buildings can be, how far apart trees should sit, or what materials are allowed. They’re published by local councils and used when making planning applications.
But they’re often difficult to use:
- Hard to find — hidden in 300-page PDFs
- There is no way to map search which codes are active and relate to your geographic area
- Inconsistent — every council does it differently
- Unclear — language is vague and not actionable for developers who abide by them
- Slow — they take a long time to create and update
Discovery Research & Mapping User Flows
Discovery research was conducted through workshops and interviews with expert user groups consisting of local authorities and architectural consultants responsible for creating design codes. Data was analysed and put together a comprehensive flow of the design coding process.
Mocking up Wireframes
High-level user needs and design coding pain points were identified during discovery and informed the creation of initial wireframes, which were developed using the GOV.UK Design System. These wireframes were validated with client stakeholders through iterative stages of refinement before being concept-tested with participants.
Testing Assumptions
I built a series of high-fidelity, interactive prototypes with v0.dev to test how users understand, write, and find design codes. Because we were exploring reactions to AI suggestions in an expert field, realistic prototypes were essential.
Swipe to view prototypes
Right Place Right time Guidance
We tested content design patterns using examples of good and bad design codes to understand how people interpret national guidance.
Insight: Contextual, “just-in-time” guidance can support users without replacing expertise — showing the value of consistent language across varied local contexts.
How to provide feedback?
Tested LLM-powered feedback on user-written requirements.Users liked the clarity prompts but stressed the need for context-sensitive feedback.Action: Refine feedback to reflect real-world scenarios and avoid generic suggestions.
Image before text or text before image?
We tested where images fit in the writing process and how easy forms of annotation could empower councils to produce images themselves rather than use consultants.
Findability for decision makers
We explored how end users — such as developers and planning officers — search for and review design code information.
This helped us identify what makes design codes easier to find, understand, and act on.
Creation of Archetypes
- Developing archetypes of design code writers
Matrix Analysis
- We plotted the archetypes on a matrix to show the service’s impact across different user groups and their needs.
- We defined the strategic direction of the research to shape the MVP and identify the user group for the private beta trial.
User research showed the design code builder delivers value through guidance for writing codes and tools for creating visuals. Its usefulness depended on each LPA’s expertise and capacity.
Mapping Service Insights and Establishing MVP Flow
We mapped the service journey to identify user breaking points and linked these to the mindsets where the service had the most impact. This helped clarify which features — within and beyond the core service — shaped the final value proposition flow.
By the end of the alpha, we identified one key user archetype that we believed would be most impactful and would benefit the most from the service. This insight informed our plans for the upcoming private beta, where we intend to focus on this group.
Through the alpha, we also defined which features should be included within the service and which should remain outside of it, shaping the direction of the MVP. We concluded by mapping how future versions of the service could evolve to support additional archetypes once the most impactful user groups have been addressed.
Conclusion
This project was a great opportunity to explore how rapid AI prototyping can reveal deeper insights into different user mindsets and behaviors.
By building tangible concept functionalities that users could actually interact with, we were able to see where the service fits naturally into their workflows. These hands-on sessions helped us confidently map out the many possible service flows that could support people creating or searching for design codes.
Testing our assumptions early gave us clarity on which user groups would be most valuable to focus on for our MVP.
Finally, experimenting with multiple functionalities and interaction sequences helped validate our service design decisions — and ultimately shaped a clear vision of what our MVP should be.
Outputs
1
Validated service concept and mapped user journeys
Delivered conclusive concept validation and mapped the service flow, defining how the new approach could work end-to-end.
2
User research insights and audience archetypes
Produced a research report outlining key user types, their needs, and strategies for engaging the most impactful groups.
3
Defined MVP and next steps
Established a clear MVP path and technical feasibility, with recommendations and next steps handed over for build.
Credits
DevOpsTess B
Data EngineerDan K
Project LeadTonya B
Client IllustratorSirdeep S
Client LeadMatt P
Service DesignSergei W
Digitalisation of Design Codes
Client: Ministry of Housing and Local Government
Role: Interaction Designer and Lead Researcher
TLDR: Design Codes are hard to write, find and apply. We built and tested an AI-assisted writing tool to improve clarity and consistency of Design codes along with a map based search to facilitate the process of finding them.
Design Codes
How might we enable urban planners to create and apply standardised planning codes that simplify approvals and accelerate housing development across the UK?
Individual Skills Showcased
User Research
Design System
integration
AI Prototyping
Concept testing
Usability testing
Qual Analysis
Service mapping
Challenge
Design codes are often written in vague, inconsistent language, making it difficult for developers to determine compliance with policy. This causes delays and inefficiencies in the planning process.
Currently, there is no service that allows users to easily search for or interpret design codes.
Solutions
Through research into the needs of both design code writers and users, we:
- Developed AI-assisted writing tools to standardise design codes and provide contextual guidance.
- Created low-cost image generation and annotation tools to help visualise design codes.
- Built and tested a public-facing, map-based search tool that enables users to find and understand applicable design codes, streamlining the planning process.
Outcome
We validated the concept for a service that helps Local Planning Authorities (LPAs) draft clear, actionable design requirements and publish them in an accessible digital format.
With further funding and development into a private beta, this service could improve compliance, increase submission quality, and lead to better design outcomes.
Jump to:
Objectives
Context
Discovery Work
Prototyping & testing assumptions
UR
MVP Service Design
Conclusion
Outcomes
Client Objectives
1
Design and test a new service for creating better Design Codes
Map user journeys, build prototype screens, and test concepts to support code writers in producing clearer, more consistent guidance.
2
Understand user groups and their needs
Identify and map the different types of users — those who write codes and those who use them — through research and prototype testing.
3
Define what the MVP could look like
Explore service and technical feasibility and outline the core features needed for an initial minimum viable product.
Context
What are Design Codes — and what’s wrong with them?
Design Codes set the rules/policy for how places are built — things like how tall buildings can be, how far apart trees should sit, or what materials are allowed. They’re published by local councils and used when making planning applications.
But they’re often difficult to use:
- Hard to find — hidden in 300-page PDFs
- There is no way to map search which codes are active and relate to your geographic area
- Inconsistent — every council does it differently
- Unclear — language is vague and not actionable for developers who abide by them
- Slow — they take a long time to create and update
Discovery Research & Mapping User Flows
Discovery research was conducted through workshops and interviews with expert user groups consisting of local authorities and architectural consultants responsible for creating design codes. Data was analysed and put together a comprehensive flow of the design coding process.
Mocking up Wireframes
High-level user needs and design coding pain points were identified during discovery and informed the creation of initial wireframes, which were developed using the GOV.UK Design System. These wireframes were validated with client stakeholders through iterative stages of refinement before being concept-tested with participants.
Testing Assumptions
I built a series of high-fidelity, interactive prototypes with v0.dev to test how users understand, write, and find design codes. Because we were exploring reactions to AI suggestions in an expert field, realistic prototypes were essential.
Right Place Right time Guidance
We tested content design patterns using examples of good and bad design codes to understand how people interpret national guidance.
Insight: Contextual, “just-in-time” guidance can support users without replacing expertise — showing the value of consistent language across varied local contexts.
How to provide feedback?
Tested LLM-powered feedback on user-written requirements.
Users liked the clarity prompts but stressed the need for context-sensitive feedback.Action: Refine feedback to reflect real-world scenarios and avoid generic suggestions.
Image before text or text before image?
We tested where images fit in the writing process and how easy forms of annotation could empower councils to produce images themselves rather than use consultants.
Findability for decision makers
We explored how end users — such as developers and planning officers — search for and review design code information.
This helped us identify what makes design codes easier to find, understand, and act on.
Creation of Archetypes
- Developing archetypes of design code writers
Matrix Analysis
- We plotted the archetypes on a matrix to show the service’s impact across different user groups and their needs.
- We defined the strategic direction of the research to shape the MVP and identify the user group for the private beta trial.
User research showed the design code builder delivers value through guidance for writing codes and tools for creating visuals. Its usefulness depended on each LPA’s expertise and capacity.
Mapping Service Insights and Establishing MVP Flow
We mapped the service journey to identify user breaking points and linked these to the mindsets where the service had the most impact. This helped clarify which features — within and beyond the core service — shaped the final value proposition flow.
By the end of the alpha, we identified one key user archetype that we believed would be most impactful and would benefit the most from the service. This insight informed our plans for the upcoming private beta, where we intend to focus on this group.
Through the alpha, we also defined which features should be included within the service and which should remain outside of it, shaping the direction of the MVP. We concluded by mapping how future versions of the service could evolve to support additional archetypes once the most impactful user groups have been addressed.
Conclusion
This project was a great opportunity to explore how rapid AI prototyping can reveal deeper insights into different user mindsets and behaviors.
By building tangible concept functionalities that users could actually interact with, we were able to see where the service fits naturally into their workflows. These hands-on sessions helped us confidently map out the many possible service flows that could support people creating or searching for design codes.
Testing our assumptions early gave us clarity on which user groups would be most valuable to focus on for our MVP.
Finally, experimenting with multiple functionalities and interaction sequences helped validate our service design decisions — and ultimately shaped a clear vision of what our MVP should be.
Outputs
1
Validated service concept and mapped user journeys
Delivered conclusive concept validation and mapped the service flow, defining how the new approach could work end-to-end.
2
User research insights and audience archetypes
Produced a research report outlining key user types, their needs, and strategies for engaging the most impactful groups.
3
Defined MVP and next steps
Established a clear MVP path and technical feasibility, with recommendations and next steps handed over for build.
Credits
DevOpsTess B
Data EngineerDan K
Project LeadTonya B
Client IllustratorSirdeep S
Client LeadMatt P
Service DesignSergei W
Digitalisation of Design Codes
Client: Ministry of Housing and Local Government
Role: Interaction Designer and Lead Researcher
TLDR: Design Codes are hard to write, find and apply. We built and tested an AI-assisted writing tool to improve clarity and consistency of Design codes along with a map based search to facilitate the process of finding them.
Design Codes
How might we enable urban planners to create and apply standardised planning codes that simplify approvals and accelerate housing development across the UK?
Individual Skills Showcased
User Research
Design System
integration
AI Prototyping
Concept testing
Usability testing
Qual Analysis
Service mapping
Challenge
Design codes are often written in vague, inconsistent language, making it difficult for developers to determine compliance with policy. This causes delays and inefficiencies in the planning process.
Currently, there is no service that allows users to easily search for or interpret design codes.
Solutions
Through research into the needs of both design code writers and users, we:
- Developed AI-assisted writing tools to standardise design codes and provide contextual guidance.
- Created low-cost image generation and annotation tools to help visualise design codes.
- Built and tested a public-facing, map-based search tool that enables users to find and understand applicable design codes, streamlining the planning process.
Outcome
We validated the concept for a service that helps Local Planning Authorities (LPAs) draft clear, actionable design requirements and publish them in an accessible digital format.
With further funding and development into a private beta, this service could improve compliance, increase submission quality, and lead to better design outcomes.
Jump to:
Objectives
Context
Discovery Work
Prototyping & testing assumptions
UR
MVP Service Design
Conclusion
Outcomes
Client Objectives
1
Design and test a new service for creating better Design Codes
Map user journeys, build prototype screens, and test concepts to support code writers in producing clearer, more consistent guidance.
2
Understand user groups and their needs
Identify and map the different types of users — those who write codes and those who use them — through research and prototype testing.
3
Define what the MVP could look like
Explore service and technical feasibility and outline the core features needed for an initial minimum viable product.
Context
What are Design Codes — and what’s wrong with them?
Design Codes set the rules/policy for how places are built — things like how tall buildings can be, how far apart trees should sit, or what materials are allowed. They’re published by local councils and used when making planning applications.
But they’re often difficult to use:
- Hard to find — hidden in 300-page PDFs
- There is no way to map search which codes are active and relate to your geographic area
- Inconsistent — every council does it differently
- Unclear — language is vague and not actionable for developers who abide by them
- Slow — they take a long time to create and update
Discovery Research & Mapping User Flows
Discovery research was conducted through workshops and interviews with expert user groups consisting of local authorities and architectural consultants responsible for creating design codes. Data was analysed and put together a comprehensive flow of the design coding process.
Mocking up Wireframes
High-level user needs and design coding pain points were identified during discovery and informed the creation of initial wireframes, which were developed using the GOV.UK Design System. These wireframes were validated with client stakeholders through iterative stages of refinement before being concept-tested with participants.
Testing Assumptions
I built a series of high-fidelity, interactive prototypes with v0.dev to test how users understand, write, and find design codes. Because we were exploring reactions to AI suggestions in an expert field, realistic prototypes were essential.
Right Place Right time Guidance
We tested content design patterns using examples of good and bad design codes to understand how people interpret national guidance.
Insight: Contextual, “just-in-time” guidance can support users without replacing expertise — showing the value of consistent language across varied local contexts.
How to provide feedback?
Tested LLM-powered feedback on user-written requirements.Users liked the clarity prompts but stressed the need for context-sensitive feedback.Action: Refine feedback to reflect real-world scenarios and avoid generic suggestions.
Image before text or text before image?
We tested where images fit in the writing process and how easy forms of annotation could empower councils to produce images themselves rather than use consultants.
Findability for decision makers
We explored how end users — such as developers and planning officers — search for and review design code information.
This helped us identify what makes design codes easier to find, understand, and act on.
Creation of Archetypes
- Developing archetypes of design code writers
Matrix Analysis
- We plotted the archetypes on a matrix to show the service’s impact across different user groups and their needs.
- We defined the strategic direction of the research to shape the MVP and identify the user group for the private beta trial.
User research showed the design code builder delivers value through guidance for writing codes and tools for creating visuals. Its usefulness depended on each LPA’s expertise and capacity.
Mapping Service Insights and Establishing MVP Flow
We mapped the service journey to identify user breaking points and linked these to the mindsets where the service had the most impact. This helped clarify which features — within and beyond the core service — shaped the final value proposition flow.
By the end of the alpha, we identified one key user archetype that we believed would be most impactful and would benefit the most from the service. This insight informed our plans for the upcoming private beta, where we intend to focus on this group.
Through the alpha, we also defined which features should be included within the service and which should remain outside of it, shaping the direction of the MVP. We concluded by mapping how future versions of the service could evolve to support additional archetypes once the most impactful user groups have been addressed.
Conclusion
This project was a great opportunity to explore how rapid AI prototyping can reveal deeper insights into different user mindsets and behaviors.
By building tangible concept functionalities that users could actually interact with, we were able to see where the service fits naturally into their workflows. These hands-on sessions helped us confidently map out the many possible service flows that could support people creating or searching for design codes.
Testing our assumptions early gave us clarity on which user groups would be most valuable to focus on for our MVP.
Finally, experimenting with multiple functionalities and interaction sequences helped validate our service design decisions — and ultimately shaped a clear vision of what our MVP should be.
Outputs
1
Validated service concept and mapped user journeys
Delivered conclusive concept validation and mapped the service flow, defining how the new approach could work end-to-end.
2
User research insights and audience archetypes
Produced a research report outlining key user types, their needs, and strategies for engaging the most impactful groups.
3
Defined MVP and next steps
Established a clear MVP path and technical feasibility, with recommendations and next steps handed over for build.
Credits
DevOpsTess B
Data EngineerDan K
Project LeadTonya B
Client IllustratorSirdeep S
Client LeadMatt P
Service DesignSergei W