Premier Plus LLC
Premier Plus LLC
How I Streamlined The AI Drone Damage Inspections & Report Generation
How I Streamlined The AI Drone Damage Inspections & Report Generation
How I Streamlined The AI Drone Damage Inspections & Report Generation
35% faster
Flight job completion
20% increase
Daily inspections booked
50% faster
Claim report generation
88% accuracy
AI damage detection
My Role
Product Designer
The Team
1 x Product Designer
1 x Product Manager
1 x Tech Lead
2 x AI/ML Engineers
2 x Developers
Year
2023
Problem
Problem
Problem
Imagine Emma, a Claims and Damage Specialist, tasked with analyzing drone-captured images. She has to sift through hundreds of photos, evaluating roof and siding damage across multiple homes.
Emma's primary goal is to assess and label each image, marking which ones indicate damage and which ones do not.
However, the process is cumbersome. She has to switch between multiple third-party apps to review the pilot's notes, navigate through poorly organized image galleries, and manually tag each relevant image.
Emma’s pain points reflect broader issues:
📃 Cumbersome report generation
Too many steps to generate reports, with a disconnected flow between reviewing images and compiling findings.
🗃️ Inefficient image organization
The current system does not group or label images in a way that makes it easy to review damage quickly and accurately.
🤔 Trust in potential AI solution
Emma feels new solution will lack the ability to accurately detect complex or subtle damage types, requiring her to spend extra time checking the images
Imagine Emma, a Claims and Damage Specialist, tasked with analyzing drone-captured images. She has to sift through hundreds of photos, evaluating roof and siding damage across multiple homes.
Emma's primary goal is to assess and label each image, marking which ones indicate damage and which ones do not.
However, the process is cumbersome. She has to switch between multiple third-party apps to review the pilot's notes, navigate through poorly organized image galleries, and manually tag each relevant image.
Emma’s pain points reflect broader issues:
📃 Cumbersome report generation
Too many steps to generate reports, with a disconnected flow between reviewing images and compiling findings.
🗃️ Inefficient image organization
The current system does not group or label images in a way that makes it easy to review damage quickly and accurately.
🤔 Trust in potential AI solution
Emma feels new solution will lack the ability to accurately detect complex or subtle damage types, requiring her to spend extra time checking the images
In the field, drone pilots faced different issues…
In the field, drone pilots faced different issues…
In the field, drone pilots faced different issues…
Imagine Jake, a Drone Inspection Pilot working for Premier Plus, tasked with surveying a neighborhood hit hard by a recent storm. He arrives at the scene, where roofs are damaged, trees are down, and his team needs immediate data to assess the situation further in order to create an accurate insurance claim report.
As Jake prepares to launch the drone, he realizes that the drone battery is low and that the backup battery is probably not fully charged yet. He also worries if the wind might be too dangerous for the drone's safe flight.
This situation highlights the major challenges in the current drone damage inspection process:
🎮 Slow on-site job creation and drone setup
Usage of numerous third-party apps and manual flight setup hurts the efficiency potential and more daily scans to be performed.
🚧 Lack of real-time updates on flight safety and approvals
Pilots had to visit the FAA's website to verify flight approval status and separately monitor weather apps for sudden wind changes.
🔋 Lengthy jobs drained drone's battery beyond acceptable rate
Unnecessarily long flights forced the pilots to juggle between checking the battery level and making sure that the backup battery is charged enough.
Imagine Jake, a Drone Inspection Pilot working for Premier Plus, tasked with surveying a neighborhood hit hard by a recent storm. He arrives at the scene, where roofs are damaged, trees are down, and his team needs immediate data to assess the situation further in order to create an accurate insurance claim report.
As Jake prepares to launch the drone, he realizes that the drone battery is low and that the backup battery is probably not fully charged yet. He also worries if the wind might be too dangerous for the drone's safe flight.
This situation highlights the major challenges in the current drone damage inspection process:
🎮 Slow on-site job creation and drone setup
Usage of numerous third-party apps and manual flight setup hurts the efficiency potential and more daily scans to be performed.
🚧 Lack of real-time updates on flight safety and approvals
Pilots had to visit the FAA's website to verify flight approval status and separately monitor weather apps for sudden wind changes.
🔋 Lengthy jobs drained drone's battery beyond acceptable rate
Unnecessarily long flights forced the pilots to juggle between checking the battery level and making sure that the backup battery is charged enough.
Our research insights helped us develop the user personas and create the customer journey maps of their current workflow.
Our research insights helped us develop the user personas and create the customer journey maps of their current workflow.
Our research insights helped us develop the user personas and create the customer journey maps of their current workflow.
Goals
Goals
Goals
Our primary objectives were to:
⚙️ Automate job creation and footage upload
Reduce manual steps so pilots can focus on data collection & D2D sales.
✔️ Make flight clearance approvals faster
Integrate FAA checks directly for quicker approvals without leaving the app.
✨ Have a reliable AI damage detection
Improve AI accuracy, reducing manual review and false positives for quicker assessments.
⚡ Real-time alerts on sudden weather changes
Send instant notifications about weather changes to avoid risky flights.
📋 Generate cleaner reports for the insurance companies
Streamline images, notes, and damage data into well-organized, faster-to-process reports.
Our primary objectives were to:
⚙️ Automate job creation and footage upload
Reduce manual steps so pilots can focus on data collection & D2D sales.
✔️ Make flight clearance approvals faster
Integrate FAA checks directly for quicker approvals without leaving the app.
✨ Have a reliable AI damage detection
Improve AI accuracy, reducing manual review and false positives for quicker assessments.
⚡ Real-time alerts on sudden weather changes
Send instant notifications about weather changes to avoid risky flights.
📋 Generate cleaner reports for the insurance companies
Streamline images, notes, and damage data into well-organized, faster-to-process reports.
Solutions & Impact
Solutions & Impact
Solutions & Impact
The improved drone inspection workflow introduced a range of optimizations that streamlined both on-site operations and post-flight analysis.
By enhancing job creation, flight safety, and AI-assisted damage detection, we aimed to boost efficiency across the board.
35% faster
Job completion
35% faster
Job completion
35% faster
Job completion
20% increase
Daily inspections booked
20% increase
Daily inspections booked
20% increase
Daily inspections booked
50% faster
report generation
50% faster
report generation
50% faster
report generation
88% accuracy
AI damage detection
88% accuracy
AI damage detection
88% accuracy
AI damage detection
