Exploring the value of AI-Powered Insights in DirtMate
Propeller's platform allows teams to measure and manage their sites using 3D mapping and visual tools.
One of these tools is DirtMate, a real-time progress tracking system that gives teams live visibility of material movement, helping them stay on schedule and reduce rework.
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The Problem
DirtMate collects large amounts of valuable operational data, but users often struggle to turn that raw data into meaningful, actionable insights. Many spend significant time manually digging through reports or datasets to identify opportunities for improving efficiency.
The Goal
Explore how AI could make DirtMate data more accessible and useful by:
Surfacing relevant, personalised insights proactively.
Helping users identify improvements faster.
Delivering insights through an intuitive, conversational experience
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My Role: Product Designer
Over 8 weeks, and working with product managers, engineers, and sales I:
Led discovery, including customer and stakeholder interviews
Planned and designed an experimental approach to validate AI-powered insights
Created wireframes and prototypes to test design concepts
Aligned the design with technical feasibility
Defined success criteria and risks for each experiment
Research
Customer Interviews
Customer interviews with site managers, supervisors and operators revealed:
Users want quick, easy ways to spot inefficiencies.
Many aren’t confident interpreting raw data.
They lack quick ways to investigate issues
Operator performance (e.g. idle time, productivity) is a key focus.
There is strong interest in personalised, contextual insights.
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Stakeholder interviews and workshops
Internal stakeholder interviews with sales, product and customer support revealed:
DirtMate provides valuable data, but extracting insights is time-consuming for customers
There’s an opportunity to surface key observations proactively and improve the product experience
Caution around ensuring AI insights have the right context and accuracy
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Design Process
AI Suggestion Bot Concept
We designed a lightweight, AI-powered bot within DirtMate to proactively analyse site data and provide clear, actionable insights. Users can engage with the bot to ask questions and explore their data further, all within a simple, conversational interface.
To validate the concept and de-risk development, we planned three lightweight experiments with 60+ active DirtMate users, delivered using LaunchDarkly and Claude AI.
Experiment 1: Initial Insight Card
Test simple, contextual insights within the DirtMate interface
Measure discoverability and engagement (hover rates, thumbs up/down)
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Experiment 2: AI-Generated Insights Carousel
Deliver multiple AI-generated insights focused on efficiency
Collect qualitative feedback and measure interaction with the carousel
Experiment 3: Single Query Prompt
Introduce a conversational prompt to encourage deeper data exploration
Understand what questions users naturally want to ask
Measure engagement and types of follow-up questions
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Impact and Reflection
Outcomes
This project is currently in the build phase. Once deployed, we will:
Track adoption and engagement across experiments
Collect qualitative and quantitative feedback
Identify which types of insights are most valuable to different user personas
Refine the AI models and conversational experience based on real user behaviour
If the experiments are successful, it will:
Make DirtMate data more accessible and actionable
Boost user engagement and platform value
Help users improve efficiency on site
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Key skills Demonstrated
Discovery and user research
Collaborative, cross-functional design process
AI-informed product design
Prototyping and rapid experimentation
Stakeholder alignment and communication
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