Role
Product designer
Owned
End-to-end UX + UI
Company
Brivo (Eagle Eye Networks)
Timeline
Sep 2024 – Dec 2024
Challenge
Brivo (prev. Eagle Eye Networks) is a global cloud video security platform where enterprise security teams monitor thousands of cameras across hundreds of locations. Its AI-powered Video Search feature could detect people in footage, but only by clothing color and gender, so tracing a specific person still meant scrubbing clip by clip. Leadership wanted to launch Face Match, a paid add-on, as its own section, separate from Video Search. That would have made teams search for a person’s face in one place and for what that person did in another.
My role: I led the end-to-end design and helped shape strategy for scaling the feature, aligning stakeholders, and securing cross-functional support.
The goal: Enable security teams to search footage and see who was there and what they did, all in one place.
The solution
Face Match lives inside Video Search, not separate
Based on their mental model, users are typically searching for a person, not a face. Name and label became person attributes next to gender and clothing color. Managing these identities, like naming and tagging, moved to a separate “People” section.
Each result shows who, when, and where
Grouping results by person wasn’t possible in eight weeks, because faces and events sat in separate databases. Instead, each card pairs the scene with a face, a name or four-digit ID, and labels. Clicking a face opens a sidebar with first seen, last seen, cameras, and locations, without leaving search.
Making syntax search more guided and flexible
Video search moved from natural language to key-value queries like person:upperwear:green. Autocomplete suggests keys and values as you type, partial queries still run, and invalid ones are caught before they return empty results.
Outcome
Multi-location operations were accumulating hundreds of identified people within the first few months
Face Match became the top-selling add-on feature
No longer a friction point in sales conversations
Positive feedback from resellers during the annual summit
How this got built
I started by talking to customer-facing teams and key customers and reviewing how competitors handle people search, then turned the case for integration into a design proposal. The PM signed off right away, and two weeks later the CEO approved building it. From there, I worked through feasibility with engineering in design reviews, which shaped the result cards and merge flow, and we built a working prototype showing the same search could scale to cameras, locations, and tags.