An AI assistant for workplace audit readiness
An AI assistant for workplace audit readiness
Role
Experience Designer
Client
Multinational Food Company
Services
UX Strategy, AI Workflow Design, Product UI, Prototyping
Role
Experience Designer
Client
Multinational Food Company
Services
UX Strategy, UX Research, Onsite Workshop Facilitation, ServiceNow
Role
Experience Designer
Client
Multinational Food Company
Services
UX Strategy, AI Workflow Design, Product UI, Prototyping
We designed an AI-powered audit readiness workspace to help safety and quality teams find the right documents faster, review incident evidence, and prepare more confidently for audits and inspections.
We designed an AI-powered audit readiness workspace to help safety and quality teams find the right documents faster, review incident evidence, and prepare more confidently for audits and inspections.
We designed an AI-powered audit readiness workspace to help safety and quality teams find the right documents faster, review incident evidence, and prepare more confidently for audits and inspections.
We designed an AI-powered audit readiness workspace to help safety and quality teams find the right documents faster, review incident evidence, and prepare more confidently for audits and inspections.
The Story
For Safety managers, preparing for an audit or inspection is usually stressful and time-consuming. They have to go searching for documents, verify incident logs, and piece together what happened if things weren't documented perfectly from the beginning.
By leveraging generative AI, we wanted to build a product that could instantly parse through their dense documentation and logged data, pulling up the exact information and specific details needed to be audit-ready on demand.
Key Goals
Decrease the financial cost of audits by helping employees to be audit-ready with minimal external support
Minimise nonconformities reported by anticipating and addressing potential issues prior to the audit
Shorten the length of audits by reducing the retrieval time of information requested mid-audit
The Story
For Safety managers, preparing for an audit or inspection is usually stressful and time-consuming. They have to go searching for documents, verify incident logs, and piece together what happened if things weren't documented perfectly from the beginning.
By leveraging generative AI, we wanted to build a product that could instantly parse through their dense documentation and logged data, pulling up the exact information and specific details needed to be audit-ready on demand.
Key Goals
Decrease the financial cost of audits by helping employees to be audit-ready with minimal external support
Minimise nonconformities reported by anticipating and addressing potential issues prior to the audit
Shorten the length of audits by reducing the retrieval time of information requested mid-audit
The Story
For Safety managers, preparing for an audit or inspection is usually stressful and time-consuming. They have to go searching for documents, verify incident logs, and piece together what happened if things weren't documented perfectly from the beginning.
By leveraging generative AI, we wanted to build a product that could instantly parse through their dense documentation and logged data, pulling up the exact information and specific details needed to be audit-ready on demand.
Key Goals
2025
2025
2025
From use case to reality
AI Model
Using HybridRAG technology for unstructured, multimodal data
The source material would come in different formats, so the proposed model used HybridRAG to combine semantic search, keyword matching, OCR, and relationship mapping.
The system could find the right evidence, understand how it connected, and return information that users could review before using it in an audit response.
Connection model showing how related safety information can be linked to give AI more context.
What is HybridRAG
HybridRAG combines different retrieval methods, like semantic search, keyword matching, OCR, metadata, and relationship mapping, so AI can find evidence that is both relevant and traceable before generating a response.
AI Model
Using HybridRAG technology for unstructured, multimodal data
The source material would come in different formats, so the proposed model used HybridRAG to combine semantic search, keyword matching, OCR, and relationship mapping.
The system could find the right evidence, understand how it connected, and return information that users could review before using it in an audit response.
Connection model showing how related safety information can be linked to give AI more context.
What is HybridRAG
HybridRAG combines different retrieval methods, like semantic search, keyword matching, OCR, metadata, and relationship mapping, so AI can find evidence that is both relevant and traceable before generating a response.
AI Model
Using HybridRAG technology for unstructured, multimodal data
The source material would come in different formats, so the proposed model used HybridRAG to combine semantic search, keyword matching, OCR, and relationship mapping.
The system could find the right evidence, understand how it connected, and return information that users could review before using it in an audit response.
Connection model showing how related safety information can be linked to give AI more context.
What is HybridRAG
2025
System Workflow
From request to review-ready response
Once the workflow is initiated, the AI would help gather the right records and connect the available evidence. The safety managers could then review, ask follow-up questions, and build a clearer picture of what happened before preparing the final response to auditors.
Connection model showing how related safety information can be linked to give AI more context.
System Workflow
From request to review-ready response
Once the workflow is initiated, the AI would help gather the right records and connect the available evidence. The safety managers could then review, ask follow-up questions, and build a clearer picture of what happened before preparing the final response to auditors.
Connection model showing how related safety information can be linked to give AI more context.
The Solution
The final experience gave safety managers a single place to access the documents and information needed for an audit. They could share what they were looking for, add any relevant incident details, and let the system pull together the supporting records and evidence.
The final experience gave safety managers a single place to access the documents and information needed for an audit. They could share what they were looking for, add any relevant incident details, and let the system pull together the supporting records and evidence.
The Solution
We designed an AI-powered audit readiness workspace to help safety and quality teams find the right documents faster, review incident evidence, and prepare more confidently for audits and inspections.
The Solution
The final experience gave safety managers a single place to access the documents and information needed for an audit. They could share what they were looking for, add any relevant incident details, and let the system pull together the supporting records and evidence.
Step 1
Tell the AI agent what you need
The safety manager enters the information they are trying to gather, and the system begins finding the records and evidence that could support it.
Step 1
Tell the AI agent what you need
The safety manager enters the information they are trying to gather, and the system begins finding the records and evidence that could support it.
Step 1
Tell the AI agent what you need
The safety manager enters the information they are trying to gather, and the system begins finding the records and evidence that could support it.
Step 2
Capturing case details
The intake form captures the core case details so the system has enough context to retrieve relevant details and build a case.
Step 3
Building the case from available records
Once the case details are submitted, the system begins parsing available records and collecting the documents connected to the incident.
Step 3
Building the case from available records
Once the case details are submitted, the system begins parsing available records and collecting the documents connected to the incident.
Step 3
Building the case from available records
Once the case details are submitted, the system begins parsing available records and collecting the documents connected to the incident.
Step 4
Collected evidence summary
The AI parses through all the available records and presents the case details, collected documents, and an initial summary in one place, with all the sources available in the side panel.
Step 5
Asking follow-up questions
Any area can be investigated further by asking a more specific question within the same workspace, with follow-up questions building on what has already been found, rather than sending the user back to a new search.
Step 5
Asking follow-up questions
Any area can be investigated further by asking a more specific question within the same workspace, with follow-up questions building on what has already been found, rather than sending the user back to a new search.
Step 5
Asking follow-up questions
Any area can be investigated further by asking a more specific question within the same workspace, with follow-up questions building on what has already been found, rather than sending the user back to a new search.
Step 6
Digging deeper into specific evidence
Once a follow-up question is submitted, the AI re-parses the relevant documents and provides a more focused summary, with the available evidence presented upfront.
Reflection
Turning the AI model into a usable review flow
This project showed me that AI works best when it supports the person doing the work, rather than trying to replace their judgment. When the records already exist but are spread across documents and resources, AI is most useful as an assistant that can help gather and organise that information.
For future iterations, I would explore clearer ways to show when information is missing and when a response needs further checking.
Future considerations
Source confidence
Show why a record was retrieved and how strongly it supports the answer.
State missing evidence
Make it clear when the system cannot find enough information.
Reflection
Turning the AI model into a usable review flow
This project showed me that AI works best when it supports the person doing the work, rather than trying to replace their judgment. When the records already exist but are spread across documents and resources, AI is most useful as an assistant that can help gather and organise that information.
For future iterations, I would explore clearer ways to show when information is missing and when a response needs further checking.
Future considerations
Source confidence
Show why a record was retrieved and how strongly it supports the answer.
State missing evidence
Make it clear when the system cannot find enough information.
Reflection
Turning the AI model into a usable review flow
This project showed me that AI works best when it supports the person doing the work, rather than trying to replace their judgment. When the records already exist but are spread across documents and resources, AI is most useful as an assistant that can help gather and organise that information.
For future iterations, I would explore clearer ways to show when information is missing and when a response needs further checking.
Future considerations
Source confidence
2025
State missing evidence
2025
Reflection
Turning the AI model into a usable review flow
This project showed me that AI works best when it supports the person doing the work, rather than trying to replace their judgment. When the records already exist but are spread across documents and resources, AI is most useful as an assistant that can help gather and organise that information.
For future iterations, I would explore clearer ways to show when information is missing and when a response needs further checking.
Future Considerations
Source confidence
Show why a record was retrieved and how strongly it supports the answer.
State missing evidence
Make it clear when the system cannot find enough information.
Digital handoff
A packaged summary of the collected evidence to digitally share with auditors.
Reflection
Turning the AI model into a usable review flow
This project showed me that AI works best when it supports the person doing the work, rather than trying to replace their judgment. When the records already exist but are spread across documents and resources, AI is most useful as an assistant that can help gather and organise that information.
For future iterations, I would explore clearer ways to show when information is missing and when a response needs further checking.
Future Considerations
Source confidence
Show why a record was retrieved and how strongly it supports the answer.
State missing evidence
Make it clear when the system cannot find enough information.
Digital handoff
A packaged summary of the collected evidence to digitally share with auditors.