Product
Product
Research
Research
UI
UI
AI-powered systems designed to streamline complex workflows, enhance and ease decision-making, and deliver intuitive, cross-platform and cross screen size experiences.
AI-powered systems designed to streamline complex workflows, enhance and ease decision-making, and deliver intuitive, cross-platform and cross screen size experiences.
AI-powered systems designed to streamline complex workflows, enhance and ease decision-making, and deliver intuitive, cross-platform and cross screen size experiences.
AI-powered systems designed to streamline complex workflows, enhance and ease decision-making, and deliver intuitive, cross-platform and cross screen size experiences.
AI-powered systems designed to streamline complex workflows, enhance and ease decision-making, and deliver intuitive, cross-platform and cross screen size experiences.
10x Quality Coverage
75% Drop in Time on Relative Tasks
3 New Features
10x Quality Coverage
75% Drop in Time on Relative Tasks
3 New Features
This case study is a snapshot of a larger journey—five phases of integrating AI into 4insite
This case study is a snapshot of a larger journey—five phases of integrating AI into 4insite
This case study is a snapshot of a larger journey—five phases of integrating AI into 4insite
This case study is a snapshot of a larger journey—five phases of integrating AI into 4insite
This case study is a snapshot of a larger journey—five phases of integrating AI into 4insite
Act 1
Act 1
Act 1
The Vision: AI Powered QC
The Vision: AI Powered QC
The Vision: AI Powered QC
The Vision: AI Powered QC
The Vision: AI Powered QC
Reimagining quality control with AI to enhance manager productivity, expand site coverage, and position SBM’s services as innovative and market-leading.
Reimagining quality control with AI to enhance manager productivity, expand site coverage, and position SBM’s services as innovative and market-leading.
Reimagining quality control with AI to enhance manager productivity, expand site coverage, and position SBM’s services as innovative and market-leading.
Reimagining quality control with AI to enhance manager productivity, expand site coverage, and position SBM’s services as innovative and market-leading.
Reimagining quality control with AI to enhance manager productivity, expand site coverage, and position SBM’s services as innovative and market-leading.
We asked "How do we integrate AI into 4insite?"
We asked "How do we integrate AI into 4insite?"
We asked "How do we integrate AI into 4insite?"
We asked "How do we integrate AI into 4insite?"
We asked "How do we integrate AI into 4insite?"
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Ai Adoption
Ai Adoption
Optimizing Manager Workflows
Optimizing Manager Workflows
The challenge wasn’t just about introducing AI—it required designing a solution that scaled, integrated seamlessly with existing workflows, and added value to managers' time. The software structure was not built to support AI features and that was a challenge as well.
The challenge wasn’t just about introducing AI—it required designing a solution that scaled, integrated seamlessly with existing workflows, and added value to managers' time. The software structure was not built to support AI features and that was a challenge as well.
Then we planned and executed a mix of frameworks to solve the puzzle..
Then we planned and executed a mix of frameworks to solve the puzzle..
Then we planned and executed a mix of frameworks to solve the puzzle..
Then we planned and executed a mix of frameworks to solve the puzzle..
Then we planned and executed a mix of frameworks to solve the puzzle..
Key Findings:
Key Findings:
Key Findings:
Key Findings:
Key Findings:
Redundancy Issues
Redundancy Issues
Planning Challenges
Planning Challenges
To uncover inefficiencies, we combined multiple research methods—flowchart analysis to break down existing workflows, contextual inquiry to observe real challenges in action, pain point mapping to visualize friction areas, and a prioritization matrix to rank issues by impact. This layered approach ensured we weren’t just identifying problems, but also understanding their root causes and prioritizing the right solutions. By structuring our research this way, we created a clear path forward, focusing on changes that would drive the most meaningful improvements.
To uncover inefficiencies, we combined multiple research methods—flowchart analysis to break down existing workflows, contextual inquiry to observe real challenges in action, pain point mapping to visualize friction areas, and a prioritization matrix to rank issues by impact. This layered approach ensured we weren’t just identifying problems, but also understanding their root causes and prioritizing the right solutions. By structuring our research this way, we created a clear path forward, focusing on changes that would drive the most meaningful improvements.
To uncover inefficiencies, we combined multiple research methods—flowchart analysis to break down existing workflows, contextual inquiry to observe real challenges in action, pain point mapping to visualize friction areas, and a prioritization matrix to rank issues by impact. This layered approach ensured we weren’t just identifying problems, but also understanding their root causes and prioritizing the right solutions. By structuring our research this way, we created a clear path forward, focusing on changes that would drive the most meaningful improvements.
To uncover inefficiencies, we combined multiple research methods—flowchart analysis to break down existing workflows, contextual inquiry to observe real challenges in action, pain point mapping to visualize friction areas, and a prioritization matrix to rank issues by impact. This layered approach ensured we weren’t just identifying problems, but also understanding their root causes and prioritizing the right solutions. By structuring our research this way, we created a clear path forward, focusing on changes that would drive the most meaningful improvements.
To uncover inefficiencies, we combined multiple research methods—flowchart analysis to break down existing workflows, contextual inquiry to observe real challenges in action, pain point mapping to visualize friction areas, and a prioritization matrix to rank issues by impact. This layered approach ensured we weren’t just identifying problems, but also understanding their root causes and prioritizing the right solutions. By structuring our research this way, we created a clear path forward, focusing on changes that would drive the most meaningful improvements.
As a solution, we added a dynamic routing system, eliminating redundancies and boosting coverage
As a solution, we added a dynamic routing system, eliminating redundancies and boosting coverage
As a solution, we added a dynamic routing system, eliminating redundancies and boosting coverage
As a solution, we added a dynamic routing system, eliminating redundancies and boosting coverage
As a solution, we added a dynamic routing system, eliminating redundancies and boosting coverage
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Audit Routes
Audit Routes
Coverage Planning & Trends
Coverage Planning & Trends
Process engineers predicted site coverage would increase by 20%–60% depending on site size, with audit redundancy eliminated by up to 70%. However, scaling the system revealed technical constraints tied to site mapping regulations.
Process engineers predicted site coverage would increase by 20%–60% depending on site size, with audit redundancy eliminated by up to 70%. However, scaling the system revealed technical constraints tied to site mapping regulations.
Process engineers predicted site coverage would increase by 20%–60% depending on site size, with audit redundancy eliminated by up to 70%. However, scaling the system revealed technical constraints tied to site mapping regulations.
Process engineers predicted site coverage would increase by 20%–60% depending on site size, with audit redundancy eliminated by up to 70%. However, scaling the system revealed technical constraints tied to site mapping regulations.
Process engineers predicted site coverage would increase by 20%–60% depending on site size, with audit redundancy eliminated by up to 70%. However, scaling the system revealed technical constraints tied to site mapping regulations.
Act 2
Act 2
Act 2
Rethinking the Approach to Scale AI
Rethinking the Approach to Scale AI
Rethinking the Approach to Scale AI
Rethinking the Approach to Scale AI
Rethinking the Approach to Scale AI
Technical limitations prevented us from implementing the original prioritization tool. Instead of optimizing audit routing, we had to pivot toward embedding AI into the audit process itself.
Technical limitations prevented us from implementing the original prioritization tool. Instead of optimizing audit routing, we had to pivot toward embedding AI into the audit process itself.
Technical limitations prevented us from implementing the original prioritization tool. Instead of optimizing audit routing, we had to pivot toward embedding AI into the audit process itself.
Technical limitations prevented us from implementing the original prioritization tool. Instead of optimizing audit routing, we had to pivot toward embedding AI into the audit process itself.
Technical limitations prevented us from implementing the original prioritization tool. Instead of optimizing audit routing, we had to pivot toward embedding AI into the audit process itself.
Our first solution couldn’t scale—so we pivoted
Our first solution couldn’t scale—so we pivoted
Our first solution couldn’t scale—so we pivoted
Our first solution couldn’t scale—so we pivoted
Our first solution couldn’t scale—so we pivoted
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Pivoting & Timelines
Pivoting & Timelines
Resources Limitations
Resources Limitations
Our initial approach relied on site mapping data, but system limitations made predictive routing infeasible. Additionally, the lack of AI infrastructure meant we couldn’t implement automation at scale. Instead of prioritization, we had to focus on eliminating manual tasks that slowed managers down.
Our initial approach relied on site mapping data, but system limitations made predictive routing infeasible. Additionally, the lack of AI infrastructure meant we couldn’t implement automation at scale. Instead of prioritization, we had to focus on eliminating manual tasks that slowed managers down.
Our initial approach relied on site mapping data, but system limitations made predictive routing infeasible. Additionally, the lack of AI infrastructure meant we couldn’t implement automation at scale. Instead of prioritization, we had to focus on eliminating manual tasks that slowed managers down.
Our initial approach relied on site mapping data, but system limitations made predictive routing infeasible. Additionally, the lack of AI infrastructure meant we couldn’t implement automation at scale. Instead of prioritization, we had to focus on eliminating manual tasks that slowed managers down.
Our initial approach relied on site mapping data, but system limitations made predictive routing infeasible. Additionally, the lack of AI infrastructure meant we couldn’t implement automation at scale. Instead of prioritization, we had to focus on eliminating manual tasks that slowed managers down.
We mapped a new path using insights from our first attempt
We mapped a new path using insights from our first attempt
We mapped a new path using insights from our first attempt
We mapped a new path using insights from our first attempt
We mapped a new path using insights from our first attempt
Key Findings:
Key Findings:
Key Findings:
Key Findings:
Key Findings:
Manual Processes Limits Productivity
Manual Processes Limits Productivity
Scoring and commenting were the most time-consuming tasks, so redesigned the audit flow to prioritize pass/fail scoring on mobile screens. Using progressive disclosure and multi-touch gestures, we created an intuitive interface.
Scoring and commenting were the most time-consuming tasks, so redesigned the audit flow to prioritize pass/fail scoring on mobile screens. Using progressive disclosure and multi-touch gestures, we created an intuitive interface.
Scoring and commenting were the most time-consuming tasks, so redesigned the audit flow to prioritize pass/fail scoring on mobile screens. Using progressive disclosure and multi-touch gestures, we created an intuitive interface.
Scoring and commenting were the most time-consuming tasks, so redesigned the audit flow to prioritize pass/fail scoring on mobile screens. Using progressive disclosure and multi-touch gestures, we created an intuitive interface.
Scoring and commenting were the most time-consuming tasks, so redesigned the audit flow to prioritize pass/fail scoring on mobile screens. Using progressive disclosure and multi-touch gestures, we created an intuitive interface.
A redesigned workflow that tripled audits without extra effort
Key Solutions:
Automating Key Steps
The new system tripled the number of audits managers could complete per month while reducing workload and maintaining quality standards. This shift transformed AI from a passive tool into an active assistant that scaled audits efficiently.
Redesigned audit flows cut 75% of time on task by eliminating manual entries—tripling productivity
Redesigned audit flows cut 75% of time on task by eliminating manual entries—tripling productivity
Redesigned audit flows cut 75% of time on task by eliminating manual entries—tripling productivity
Redesigned audit flows cut 75% of time on task by eliminating manual entries—tripling productivity
Redesigned audit flows cut 75% of time on task by eliminating manual entries—tripling productivity
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Automating Key Steps
Automating Key Steps
The new system tripled the number of audits managers could complete per month while reducing workload and maintaining quality standards. This shift transformed AI from a passive tool into an active assistant that scaled audits efficiently.
The new system tripled the number of audits managers could complete per month while reducing workload and maintaining quality standards. This shift transformed AI from a passive tool into an active assistant that scaled audits efficiently.
The new system tripled the number of audits managers could complete per month while reducing workload and maintaining quality standards. This shift transformed AI from a passive tool into an active assistant that scaled audits efficiently.
The new system tripled the number of audits managers could complete per month while reducing workload and maintaining quality standards. This shift transformed AI from a passive tool into an active assistant that scaled audits efficiently.
The new system tripled the number of audits managers could complete per month while reducing workload and maintaining quality standards. This shift transformed AI from a passive tool into an active assistant that scaled audits efficiently.
Act 3
Act 3
Act 3
Act 3
Overcoming AI Limitations – Expanding Verification Beyond Managers
Overcoming AI Limitations – Expanding Verification Beyond Managers
Overcoming AI Limitations – Expanding Verification Beyond Managers
Overcoming AI Limitations – Expanding Verification Beyond Managers
Overcoming AI Limitations – Expanding Verification Beyond Managers
As we tested AI-powered audits, we faced a major roadblock—the AI required significantly more training data than expected to achieve reliable accuracy. Scaling was at risk, and we needed an alternative way to accelerate AI training while maintaining audit integrity.
As we tested AI-powered audits, we faced a major roadblock—the AI required significantly more training data than expected to achieve reliable accuracy. Scaling was at risk, and we needed an alternative way to accelerate AI training while maintaining audit integrity.
As we tested AI-powered audits, we faced a major roadblock—the AI required significantly more training data than expected to achieve reliable accuracy. Scaling was at risk, and we needed an alternative way to accelerate AI training while maintaining audit integrity.
As we tested AI-powered audits, we faced a major roadblock—the AI required significantly more training data than expected to achieve reliable accuracy. Scaling was at risk, and we needed an alternative way to accelerate AI training while maintaining audit integrity.
As we tested AI-powered audits, we faced a major roadblock—the AI required significantly more training data than expected to achieve reliable accuracy. Scaling was at risk, and we needed an alternative way to accelerate AI training while maintaining audit integrity.
AI-powered audits struggled with accuracy
AI-powered audits struggled with accuracy
AI-powered audits struggled with accuracy
AI-powered audits struggled with accuracy
AI-powered audits struggled with accuracy
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Insufficient Data
Insufficient Data
Review Necessity
Review Necessity
The AI model had difficulty distinguishing between visually similar elements (e.g., floor vs. reflections). It needed a large dataset of labeled photos, but collecting enough training data was taking too long to scale effectively.
The AI model had difficulty distinguishing between visually similar elements (e.g., floor vs. reflections). It needed a large dataset of labeled photos, but collecting enough training data was taking too long to scale effectively.
The AI model had difficulty distinguishing between visually similar elements (e.g., floor vs. reflections). It needed a large dataset of labeled photos, but collecting enough training data was taking too long to scale effectively.
The AI model had difficulty distinguishing between visually similar elements (e.g., floor vs. reflections). It needed a large dataset of labeled photos, but collecting enough training data was taking too long to scale effectively.
The AI model had difficulty distinguishing between visually similar elements (e.g., floor vs. reflections). It needed a large dataset of labeled photos, but collecting enough training data was taking too long to scale effectively.
We turned associates into AI trainers—solving two problems at once
We turned associates into AI trainers—solving two problems at once
We turned associates into AI trainers—solving two problems at once
We turned associates into AI trainers—solving two problems at once
We turned associates into AI trainers—solving two problems at once
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Managers Review
Managers Review
Associates Collect
Associates Collect
To accelerate AI learning, we restructured the verification process:
Associate-Powered Verifications – Associates verified five areas daily, dramatically increasing the volume of training data.
Managerial Validation System – Managers retained oversight by reviewing and correcting flagged inaccuracies.
Incentive-Based Participation – A leaderboard and daily incentives encouraged engagement while maintaining integrity.
To accelerate AI learning, we restructured the verification process:
Associate-Powered Verifications – Associates verified five areas daily, dramatically increasing the volume of training data.
Managerial Validation System – Managers retained oversight by reviewing and correcting flagged inaccuracies.
Incentive-Based Participation – A leaderboard and daily incentives encouraged engagement while maintaining integrity.
To accelerate AI learning, we restructured the verification process:
Associate-Powered Verifications – Associates verified five areas daily, dramatically increasing the volume of training data.
Managerial Validation System – Managers retained oversight by reviewing and correcting flagged inaccuracies.
Incentive-Based Participation – A leaderboard and daily incentives encouraged engagement while maintaining integrity.
To accelerate AI learning, we restructured the verification process:
Associate-Powered Verifications – Associates verified five areas daily, dramatically increasing the volume of training data.
Managerial Validation System – Managers retained oversight by reviewing and correcting flagged inaccuracies.
Incentive-Based Participation – A leaderboard and daily incentives encouraged engagement while maintaining integrity.
To accelerate AI learning, we restructured the verification process:
Associate-Powered Verifications – Associates verified five areas daily, dramatically increasing the volume of training data.
Managerial Validation System – Managers retained oversight by reviewing and correcting flagged inaccuracies.
Incentive-Based Participation – A leaderboard and daily incentives encouraged engagement while maintaining integrity.
AI verification scaled 10x—solving training, coverage, and audit integrity at once
AI verification scaled 10x—solving training, coverage, and audit integrity at once
AI verification scaled 10x—solving training, coverage, and audit integrity at once
AI verification scaled 10x—solving training, coverage, and audit integrity at once
AI verification scaled 10x—solving training, coverage, and audit integrity at once
The solution increased daily photo uploads significantly, providing the dataset needed to improve AI accuracy while maintaining audit integrity. It allowed the managers to take control quality through the app.
The solution increased daily photo uploads significantly, providing the dataset needed to improve AI accuracy while maintaining audit integrity. It allowed the managers to take control quality through the app.
The solution increased daily photo uploads significantly, providing the dataset needed to improve AI accuracy while maintaining audit integrity. It allowed the managers to take control quality through the app.
The solution increased daily photo uploads significantly, providing the dataset needed to improve AI accuracy while maintaining audit integrity. It allowed the managers to take control quality through the app.
The solution increased daily photo uploads significantly, providing the dataset needed to improve AI accuracy while maintaining audit integrity. It allowed the managers to take control quality through the app.
Act 4
Act 4
Advancing the Vision – AI-Powered Managerial Summaries
Advancing the Vision – AI-Powered Managerial Summaries
Advancing the Vision – AI-Powered Managerial Summaries
Advancing the Vision – AI-Powered Managerial Summaries
Advancing the Vision – AI-Powered Managerial Summaries
As AI-powered audits matured, the focus shifted from data collection to making the data actionable for managers. The goal was to transform scattered insights into clear, high-level summaries that supported decision-making and positioned SBM’s AI capabilities as a market differentiator.
As AI-powered audits matured, the focus shifted from data collection to making the data actionable for managers. The goal was to transform scattered insights into clear, high-level summaries that supported decision-making and positioned SBM’s AI capabilities as a market differentiator.
As AI-powered audits matured, the focus shifted from data collection to making the data actionable for managers. The goal was to transform scattered insights into clear, high-level summaries that supported decision-making and positioned SBM’s AI capabilities as a market differentiator.
As AI-powered audits matured, the focus shifted from data collection to making the data actionable for managers. The goal was to transform scattered insights into clear, high-level summaries that supported decision-making and positioned SBM’s AI capabilities as a market differentiator.
As AI-powered audits matured, the focus shifted from data collection to making the data actionable for managers. The goal was to transform scattered insights into clear, high-level summaries that supported decision-making and positioned SBM’s AI capabilities as a market differentiator.
The module was generating data—but managers needed clear insights
The module was generating data—but managers needed clear insights
The module was generating data—but managers needed clear insights
The module was generating data—but managers needed clear insights
The module was generating data—but managers needed clear insights
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Key Challenges:
Data-to-Story
Data-to-Story
Insufficient Supporting Data
Insufficient Supporting Data
The system was collecting a wealth of data across multiple features, but insights weren’t structured in a way that managers could quickly understand or act on. We needed to create a system that grouped, correlated, and surfaced the right information at the right time.
The system was collecting a wealth of data across multiple features, but insights weren’t structured in a way that managers could quickly understand or act on. We needed to create a system that grouped, correlated, and surfaced the right information at the right time.
The system was collecting a wealth of data across multiple features, but insights weren’t structured in a way that managers could quickly understand or act on. We needed to create a system that grouped, correlated, and surfaced the right information at the right time.
The system was collecting a wealth of data across multiple features, but insights weren’t structured in a way that managers could quickly understand or act on. We needed to create a system that grouped, correlated, and surfaced the right information at the right time.
The system was collecting a wealth of data across multiple features, but insights weren’t structured in a way that managers could quickly understand or act on. We needed to create a system that grouped, correlated, and surfaced the right information at the right time.
We built structured summaries to surface what mattered most
We built structured summaries to surface what mattered most
We built structured summaries to surface what mattered most
We built structured summaries to surface what mattered most
We built structured summaries to surface what mattered most
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Key Solutions:
Data Correlations
Data Correlations
3 Informative Styles
3 Informative Styles
To make the data actionable, we developed a system that:
Feature Correlations – Connected data points across multiple tools to surface trends and relationships.
Three Summary Styles – Designed call-to-action items, performance summaries, and achievement highlights to give managers clear next steps instead of raw data.
Responsive Storytelling UI – Created a flexible layout that adapted across three screen sizes, ensuring accessibility at all levels of management.
To make the data actionable, we developed a system that:
Feature Correlations – Connected data points across multiple tools to surface trends and relationships.
Three Summary Styles – Designed call-to-action items, performance summaries, and achievement highlights to give managers clear next steps instead of raw data.
Responsive Storytelling UI – Created a flexible layout that adapted across three screen sizes, ensuring accessibility at all levels of management.
To make the data actionable, we developed a system that:
Feature Correlations – Connected data points across multiple tools to surface trends and relationships.
Three Summary Styles – Designed call-to-action items, performance summaries, and achievement highlights to give managers clear next steps instead of raw data.
Responsive Storytelling UI – Created a flexible layout that adapted across three screen sizes, ensuring accessibility at all levels of management.
To make the data actionable, we developed a system that:
Feature Correlations – Connected data points across multiple tools to surface trends and relationships.
Three Summary Styles – Designed call-to-action items, performance summaries, and achievement highlights to give managers clear next steps instead of raw data.
Responsive Storytelling UI – Created a flexible layout that adapted across three screen sizes, ensuring accessibility at all levels of management.
To make the data actionable, we developed a system that:
Feature Correlations – Connected data points across multiple tools to surface trends and relationships.
Three Summary Styles – Designed call-to-action items, performance summaries, and achievement highlights to give managers clear next steps instead of raw data.
Responsive Storytelling UI – Created a flexible layout that adapted across three screen sizes, ensuring accessibility at all levels of management.
Site Pulse delivered 4x more insights—turning AI data into action, strategy, and success metrics
Site Pulse delivered 4x more insights—turning AI data into action, strategy, and success metrics
Site Pulse delivered 4x more insights—turning AI data into action, strategy, and success metrics
Site Pulse delivered 4x more insights—turning AI data into action, strategy, and success metrics
A feature that transformed raw data into contextual narratives. It provided three types of stories:
Actionable Insights: Highlighted tasks requiring immediate attention, like flagged areas or overdue verifications.
Strategic Summaries: Delivered high-level overviews of site performance to support resource allocation and planning.
Success Metrics: Showcased key achievements, such as improvements in cleanliness scores or associate engagement, to inform and motivate stakeholders.
A feature that transformed raw data into contextual narratives. It provided three types of stories:
Actionable Insights: Highlighted tasks requiring immediate attention, like flagged areas or overdue verifications.
Strategic Summaries: Delivered high-level overviews of site performance to support resource allocation and planning.
Success Metrics: Showcased key achievements, such as improvements in cleanliness scores or associate engagement, to inform and motivate stakeholders.
A feature that transformed raw data into contextual narratives. It provided three types of stories:
Actionable Insights: Highlighted tasks requiring immediate attention, like flagged areas or overdue verifications.
Strategic Summaries: Delivered high-level overviews of site performance to support resource allocation and planning.
Success Metrics: Showcased key achievements, such as improvements in cleanliness scores or associate engagement, to inform and motivate stakeholders.
A feature that transformed raw data into contextual narratives. It provided three types of stories:
Actionable Insights: Highlighted tasks requiring immediate attention, like flagged areas or overdue verifications.
Strategic Summaries: Delivered high-level overviews of site performance to support resource allocation and planning.
Success Metrics: Showcased key achievements, such as improvements in cleanliness scores or associate engagement, to inform and motivate stakeholders.
Delivery Manager
Ontrac
Optimized workflows across 3 screen sizes to reduce complexity, time on task, and boost efficiency and productivity, boosting Productivity by 19.56%
Research
UI
32% Task Completion Rate Increase
18% Less Errors
23% Less Time on Task
Case Study
UI Snippets
Delivery Manager
Ontrac
Optimized workflows across 3 screen sizes to reduce complexity, time on task, and boost efficiency and productivity, boosting Productivity by 19.56%
Research
UI
32% Task Completion Rate Increase
18% Less Errors
23% Less Time on Task
Case Study
UI Snippets
4I Enhancements
4Insite
A collection of enhancements in several modules to bridge workflow gaps, streamline tasks, and enhance user experience.
Product
Research
UI
Process Established
28% Entry Increase
30% Less Time on Task
Under Construction
UI Snippets
4I Enhancements
4Insite
A collection of enhancements in several modules to bridge workflow gaps, streamline tasks, and enhance user experience.
Product
Research
UI
Process Established
28% Entry Increase
30% Less Time on Task
Under Construction
UI Snippets
4I Enhancements
4Insite
A collection of enhancements in several modules to bridge workflow gaps, streamline tasks, and enhance user experience.
Product
Research
UI
Process Established
28% Entry Increase
30% Less Time on Task
Under Construction
UI Snippets
Delivery Manager
Ontrac
Optimized workflows across 3 screen sizes to reduce complexity, time on task, and boost efficiency and productivity, boosting Productivity by 19.56%
Research
UI
32% Task Completion Rate Increase
18% Less Errors
23% Less Time on Task
Case Study
UI Snippets
4I Enhancements
4Insite
A collection of enhancements in several modules to bridge workflow gaps, streamline tasks, and enhance user experience.
Product
Research
UI
Process Established
28% Entry Increase
30% Less Time on Task
Under Construction
UI Snippets
Delivery Manager
Ontrac
Optimized workflows across 3 screen sizes to reduce complexity, time on task, and boost efficiency and productivity, boosting Productivity by 19.56%
Research
UI
32% Task Completion Rate Increase
18% Less Errors
23% Less Time on Task
Case Study
UI Snippets
4I Enhancements
4Insite
A collection of enhancements in several modules to bridge workflow gaps, streamline tasks, and enhance user experience.
Product
Research
UI
Process Established
28% Entry Increase
30% Less Time on Task
Under Construction
UI Snippets
Delivery Manager
Ontrac
Optimized workflows across 3 screen sizes to reduce complexity, time on task, and boost efficiency and productivity, boosting Productivity by 19.56%
Research
UI
32% Task Completion Rate Increase
18% Less Errors
23% Less Time on Task
Case Study
UI Snippets
4I Enhancements
4Insite
A collection of enhancements in several modules to bridge workflow gaps, streamline tasks, and enhance user experience.
Product
Research
UI
Process Established
28% Entry Increase
30% Less Time on Task
Under Construction
UI Snippets
Delivery Manager
Ontrac
Optimized workflows across 3 screen sizes to reduce complexity, time on task, and boost efficiency and productivity, boosting Productivity by 19.56%
Research
UI
32% Task Completion Rate Increase
18% Less Errors
23% Less Time on Task
Case Study
UI Snippets
4I Enhancements
4Insite
A collection of enhancements in several modules to bridge workflow gaps, streamline tasks, and enhance user experience.
Product
Research
UI
Process Established
28% Entry Increase
30% Less Time on Task
Under Construction
UI Snippets
Delivery Manager
Ontrac
Optimized workflows across 3 screen sizes to reduce complexity, time on task, and boost efficiency and productivity, boosting Productivity by 19.56%
Research
UI
32% Task Completion Rate Increase
18% Less Errors
23% Less Time on Task
Case Study
UI Snippets