Spark.
PHOTO-TO-VIDEO REDESIGN
From twenty photos to one continuous scan
Capturing a vehicle for AI damage detection meant taking 15-20 precisely angled photos — miss one angle, and the estimate wouldn't be accurate. I redesigned this into a single continuous video scan, built for dealers running it as part of their daily workload and consumers who might only trade in a car once or twice in their life.
COMPANY
ACV Auctions
ROLE
Lead Designer
SERVICES
UX/UI, Cross-team Collaboration, White-label Framework
TEAM
Me, 1 PM, 1 EM
YEAR
2023 Q2
A shot list, one angle at a time
Fifteen to twenty photos meant fifteen to twenty chances to get it wrong. Each angle had to be precise enough for the AI to read — a slightly off shot didn't just look sloppy, it directly skewed the damage estimate underneath it. And the two groups shooting these photos couldn't have been more different: dealers doing this dozens of times a day as part of their job, consumers doing it once or twice in their life, usually with no idea what "the right angle" even meant.
My Role
I was the lead designer responsible for the end-to-end capture experience, from tutorial to review flow, working alongside the team building the underlying scan technology, internal teams, and external clients deploying this on their own platforms.
Building the workflow with the businesses closest to their users
Before I touched a single screen, I worked with the internal team and external clients who would deploy this to their own dealers and consumers — each one understood their end users' constraints, I understood the capture experience, and the workflow got built together from day one instead of designed in isolation and handed off.
Designing once, for many businesses
This wasn't shipping to a single product. I built a white-labeled framework where the core interaction logic stayed consistent, but visual identity and workflow steps could be customized per client.
Owning the guidance, not just the capture
Real-time feedback — progress, angle, distance — needed to work without slowing anyone down or getting in the way of the shot itself. Teaching a completely new capture method meant the guidance system had to start before the camera even opened. I designed the tutorial flow from the ground up to get someone comfortable with an unfamiliar interaction before asking them to trust it.
The Challenge
How might we redesign vehicle capture to be faster and more forgiving than a 20-photo checklist, without breaking down in a driveway that's too tight or a lot that's too big — for two users who couldn't be more different in how often they do this?
Video loosened the angle requirement, but it didn't remove distance from the equation. Users still needed enough space between themselves and the car. On top of that, this would ship across multiple white-labeled clients, each with their own workflow expectations, and to two user groups with almost opposite needs: dealers doing this daily, consumers doing it once or twice in their life.
Discovery
I started by meeting with the PM and engineering manager building the underlying scan technology — what we were building, what "working" would actually mean, and how we'd realistically test it once it existed.
From there, I brought in the team that owned OEM tooling — the dealer-facing flow where a vehicle gets photographed for valuation right after a trade-in, before it goes up as a listing. They wanted video specifically to cut down the time that process took. We built the user workflow and journey together before any screens existed. External clients came through the same process afterward — each one had their own consumer-facing flow for photographing a vehicle's condition at trade-in or lease-end, and each wanted video for the same reasons: efficiency, ease of use, less time spent. Rather than starting from scratch with every client, I took the workflow built with OEM tooling and adapted it with each one.
Testing this kind of tool the way we normally would wasn't an option, either. AI-based video capture can't be validated online. The only way to know if it works is to point a phone at an actual car in an actual driveway. That shaped the approach from the start: design early, get it into a small group's hands, and keep iterating in the field once it's live.
Once I had an initial design, I ran it through review with the AI tool team and OEM team, and through feedback sessions with each external client, iterating the design with all of them before moving quickly into build — so the teams who'd be testing it could get their hands on it as soon as possible.
Design
Most inspections happen outdoors — glare, harsh light, reflections on the car's surface — so accessibility came first: multiple aspect ratios to adapt to different framing conditions, and clear, unmistakable CTAs for record, pause, and stop. A circular indicator showed scanning progress and camera angle in real time, so users could see the system was actively tracking their movement; if someone moved too fast or held the wrong distance, a message appeared at the top of the screen — visible, but never blocking their view of the car.
Dealers use this daily; consumers might do it once or twice in their life. Rather than building two separate experiences, guidance density adjusted to context — visible enough for a first-timer, unobtrusive enough not to slow down someone who's done this a hundred times.
Outcome
Qualitatively, the tool shipped across multiple businesses under a single white-labeled framework, and the real-time guidance system became a reference point for other capture-based features on the team.
[TBD]
Scan time reduction vs. photo capture
[TBD]
Improvement in damage estimate accuracy
[TBD]
White-labeled clients using the framework
Summary
Video solved the two things that were actually solvable in this release: speed and angle precision. The bigger vision that provides freeform, hands-free capture experience which already agreed on as the next phase. That's how I think about scope: ship what's real now, but design with the next step already in view so it's not a surprise later.
This was also the first white-labeled project I led end to end, and it changed how I think about designing for people I'll never talk to directly. Internal teams and external clients each came in with different workflows and expectations — the job wasn't picking whose was "right," it was building something flexible enough to hold all of them without becoming generic.
Spark.
work
cv
PHOTO-TO-VIDEO REDESIGN
From twenty photos to one continuous scan
Capturing a vehicle for AI damage detection meant taking 15-20 precisely angled photos — miss one angle, and the estimate wouldn't be accurate. I redesigned this into a single continuous video scan, built for dealers running it as part of their daily workload and consumers who might only trade in a car once or twice in their life.
COMPANY
ACV Auctions
ROLE
Lead Designer
SERVICES
UX/UI, Cross-team Collaboration, White-label Framework
TEAM
Me, 1 PM, 1 EM
YEAR
2023 Q2
A shot list, one angle at a time
Fifteen to twenty photos meant fifteen to twenty chances to get it wrong. Each angle had to be precise enough for the AI to read — a slightly off shot didn't just look sloppy, it directly skewed the damage estimate underneath it. And the two groups shooting these photos couldn't have been more different: dealers doing this dozens of times a day as part of their job, consumers doing it once or twice in their life, usually with no idea what "the right angle" even meant.
My Role
I was the lead designer responsible for the end-to-end capture experience, from tutorial to review flow, working alongside the team building the underlying scan technology, internal teams, and external clients deploying this on their own platforms.
Building the workflow with the businesses closest to their users
Before I touched a single screen, I worked with the internal team and external clients who would deploy this to their own dealers and consumers — each one understood their end users' constraints, I understood the capture experience, and the workflow got built together from day one instead of designed in isolation and handed off.
Designing once, for many businesses
This wasn't shipping to a single product. I built a white-labeled framework where the core interaction logic stayed consistent, but visual identity and workflow steps could be customized per client.
Owning the guidance, not just the capture
Real-time feedback — progress, angle, distance — needed to work without slowing anyone down or getting in the way of the shot itself. Teaching a completely new capture method meant the guidance system had to start before the camera even opened. I designed the tutorial flow from the ground up to get someone comfortable with an unfamiliar interaction before asking them to trust it.
The Challenge
How might we redesign vehicle capture to be faster and more forgiving than a 20-photo checklist, without breaking down in a driveway that's too tight or a lot that's too big — for two users who couldn't be more different in how often they do this?
Video loosened the angle requirement, but it didn't remove distance from the equation. Users still needed enough space between themselves and the car. On top of that, this would ship across multiple white-labeled clients, each with their own workflow expectations, and to two user groups with almost opposite needs: dealers doing this daily, consumers doing it once or twice in their life.
Discovery
I started by meeting with the PM and engineering manager building the underlying scan technology — what we were building, what "working" would actually mean, and how we'd realistically test it once it existed.
From there, I brought in the team that owned OEM tooling — the dealer-facing flow where a vehicle gets photographed for valuation right after a trade-in, before it goes up as a listing. They wanted video specifically to cut down the time that process took. We built the user workflow and journey together before any screens existed. External clients came through the same process afterward — each one had their own consumer-facing flow for photographing a vehicle's condition at trade-in or lease-end, and each wanted video for the same reasons: efficiency, ease of use, less time spent. Rather than starting from scratch with every client, I took the workflow built with OEM tooling and adapted it with each one.
Testing this kind of tool the way we normally would wasn't an option, either. AI-based video capture can't be validated online. The only way to know if it works is to point a phone at an actual car in an actual driveway. That shaped the approach from the start: design early, get it into a small group's hands, and keep iterating in the field once it's live.
Once I had an initial design, I ran it through review with the AI tool team and OEM team, and through feedback sessions with each external client, iterating the design with all of them before moving quickly into build — so the teams who'd be testing it could get their hands on it as soon as possible.
Design
Most inspections happen outdoors — glare, harsh light, reflections on the car's surface — so accessibility came first: multiple aspect ratios to adapt to different framing conditions, and clear, unmistakable CTAs for record, pause, and stop. A circular indicator showed scanning progress and camera angle in real time, so users could see the system was actively tracking their movement; if someone moved too fast or held the wrong distance, a message appeared at the top of the screen — visible, but never blocking their view of the car.
Dealers use this daily; consumers might do it once or twice in their life. Rather than building two separate experiences, guidance density adjusted to context — visible enough for a first-timer, unobtrusive enough not to slow down someone who's done this a hundred times.
Outcome
Qualitatively, the tool shipped across multiple businesses under a single white-labeled framework, and the real-time guidance system became a reference point for other capture-based features on the team.
[TBD]
Scan time reduction vs. photo capture
[TBD]
Improvement in damage estimate accuracy
[TBD]
White-labeled clients using the framework
Summary
Video solved the two things that were actually solvable in this release: speed and angle precision. The bigger vision that provides freeform, hands-free capture experience which already agreed on as the next phase. That's how I think about scope: ship what's real now, but design with the next step already in view so it's not a surprise later.
This was also the first white-labeled project I led end to end, and it changed how I think about designing for people I'll never talk to directly. Internal teams and external clients each came in with different workflows and expectations — the job wasn't picking whose was "right," it was building something flexible enough to hold all of them without becoming generic.
Spark.
cv
PHOTO-TO-VIDEO REDESIGN
From twenty photos to one continuous scan
Capturing a vehicle for AI damage detection meant taking 15-20 precisely angled photos — miss one angle, and the estimate wouldn't be accurate. I redesigned this into a single continuous video scan, built for dealers running it as part of their daily workload and consumers who might only trade in a car once or twice in their life.
COMPANY
ACV Auctions
ROLE
Lead Designer
SERVICES
UX/UI, Cross-team Collaboration, White-label Framework
TEAM
Me, 1 PM, 1 EM
YEAR
2023 Q2
A shot list, one angle at a time
Fifteen to twenty photos meant fifteen to twenty chances to get it wrong. Each angle had to be precise enough for the AI to read — a slightly off shot didn't just look sloppy, it directly skewed the damage estimate underneath it. And the two groups shooting these photos couldn't have been more different: dealers doing this dozens of times a day as part of their job, consumers doing it once or twice in their life, usually with no idea what "the right angle" even meant.
My Role
I was the lead designer responsible for the end-to-end capture experience, from tutorial to review flow, working alongside the team building the underlying scan technology, internal teams, and external clients deploying this on their own platforms.
Building the workflow with the businesses closest to their users
Before I touched a single screen, I worked with the internal team and external clients who would deploy this to their own dealers and consumers — each one understood their end users' constraints, I understood the capture experience, and the workflow got built together from day one instead of designed in isolation and handed off.
Designing once, for many businesses
This wasn't shipping to a single product. I built a white-labeled framework where the core interaction logic stayed consistent, but visual identity and workflow steps could be customized per client.
Owning the guidance, not just the capture
Real-time feedback — progress, angle, distance — needed to work without slowing anyone down or getting in the way of the shot itself. Teaching a completely new capture method meant the guidance system had to start before the camera even opened. I designed the tutorial flow from the ground up to get someone comfortable with an unfamiliar interaction before asking them to trust it.
The Challenge
How might we redesign vehicle capture to be faster and more forgiving than a 20-photo checklist, without breaking down in a driveway that's too tight or a lot that's too big — for two users who couldn't be more different in how often they do this?
Video loosened the angle requirement, but it didn't remove distance from the equation. Users still needed enough space between themselves and the car. On top of that, this would ship across multiple white-labeled clients, each with their own workflow expectations, and to two user groups with almost opposite needs: dealers doing this daily, consumers doing it once or twice in their life.
Discovery
I started by meeting with the PM and engineering manager building the underlying scan technology — what we were building, what "working" would actually mean, and how we'd realistically test it once it existed.
From there, I brought in the team that owned OEM tooling — the dealer-facing flow where a vehicle gets photographed for valuation right after a trade-in, before it goes up as a listing. They wanted video specifically to cut down the time that process took. We built the user workflow and journey together before any screens existed. External clients came through the same process afterward — each one had their own consumer-facing flow for photographing a vehicle's condition at trade-in or lease-end, and each wanted video for the same reasons: efficiency, ease of use, less time spent. Rather than starting from scratch with every client, I took the workflow built with OEM tooling and adapted it with each one.
Testing this kind of tool the way we normally would wasn't an option, either. AI-based video capture can't be validated online. The only way to know if it works is to point a phone at an actual car in an actual driveway. That shaped the approach from the start: design early, get it into a small group's hands, and keep iterating in the field once it's live.
Once I had an initial design, I ran it through review with the AI tool team and OEM team, and through feedback sessions with each external client, iterating the design with all of them before moving quickly into build — so the teams who'd be testing it could get their hands on it as soon as possible.
Design
Most inspections happen outdoors — glare, harsh light, reflections on the car's surface — so accessibility came first: multiple aspect ratios to adapt to different framing conditions, and clear, unmistakable CTAs for record, pause, and stop. A circular indicator showed scanning progress and camera angle in real time, so users could see the system was actively tracking their movement; if someone moved too fast or held the wrong distance, a message appeared at the top of the screen — visible, but never blocking their view of the car.
Dealers use this daily; consumers might do it once or twice in their life. Rather than building two separate experiences, guidance density adjusted to context — visible enough for a first-timer, unobtrusive enough not to slow down someone who's done this a hundred times.
Outcome
Qualitatively, the tool shipped across multiple businesses under a single white-labeled framework, and the real-time guidance system became a reference point for other capture-based features on the team.
[TBD]
Scan time reduction vs. photo capture
[TBD]
Improvement in damage estimate accuracy
[TBD]
White-labeled clients using the framework
Summary
Video solved the two things that were actually solvable in this release: speed and angle precision. The bigger vision that provides freeform, hands-free capture experience which already agreed on as the next phase. That's how I think about scope: ship what's real now, but design with the next step already in view so it's not a surprise later.
This was also the first white-labeled project I led end to end, and it changed how I think about designing for people I'll never talk to directly. Internal teams and external clients each came in with different workflows and expectations — the job wasn't picking whose was "right," it was building something flexible enough to hold all of them without becoming generic.