Altas
Overview
Atlas didn’t just reimagine search—it reframed how we interact with our digital past. By focusing on moments over items, and context over content, Atlas offered a differentiated solution for the modern knowledge economy—paving the way for a new category of intelligent, semantic productivity tools.
Personal search index combining users’ activity and behavioral patterns to provide a semantic search, using images and events, as well as insights and automated recommendations. Altas captured each user’s individual behaviors—including both the content and action on a comprehensive timeline. This index could then be searched to recreate moments from your past.
Atlas delivered strategic business value by reducing cognitive load for knowledge workers through a memory-like retrieval system that spanned devices and formats. Its personalized, context-aware insights encouraged daily use, positioning it as a behavioral layer on top of the OS. While initially targeting consumers, early traction revealed strong enterprise potential in knowledge-intensive industries.
Responsibilities
Co-founder, responsible for design, user research and product management.
Directed iterative design process to develop search interface, visualizations and index management tools
Defined and drove product vision and detailed roadmap, use cases, wireframes and detailed requirements.
Co-led fundraising for our second round.
Established design language for the product
Deliverables
Responsible for all Product Strategy, Prioritized Roadmap, Product Design, Branding
Drove Product-Market Fit alpha & beta adoption among knowledge workers and exec assistantsDifferentiated
Created proprietary visual & Mad-Lib interfaces, strengthening IP defensibilityMarket Expansion
Led the pivot from B2C to B2B focusing on enterprise collaboration & KM tools
Validated pricing model & monetization strategies
Co-led second fundraising round
Mad-Lib personal search UI
Comprehensive Design System
Established biweekly User Research cadence
Drove customer engagement: identified key “trust” metrics
GTM strategy, collateral, sales material
Refined & validated business model
Capturing the moment
Given Atlas captured running index of all content on your device and actions you took, then stored that information on a timeline. Traditional searches find an item—a message or webpage, they search for needles in a haystack; Atlas was a time machine.
Altas allowed you to easily move from that one message to recreating the moment you first read it—opening that excel file, your browser tabs, related emails, even the music you were listening to.
Altas integrated with OS and internet search tools as well. Since Altas’ index was driven by what the user had done or seen, rather than a general public index driven by popularity or promotional algorithms, Altas’ results were truly personalized and they could remind you of things you may have already read, done, or seen related to that Google or Apple search.
Visual Search
Rather than relying on a list format of results, Atlas employed a visual search, displaying images from your activities grouped together by time, topic, people, locations, etc. Atlas created semantic connections based on behavioral patterns, activity loops, topics, locations, and routines to capture the moment rather individual items.
By indexing everything you were working at any moment, Atlas captured interactions between applications and websites (i.e., copy/paste, flipping between windows, etc.). Opening a link from an email or message Atlas would track the sender, the other recipients, your reply, and what you did next. Allowing Atlas to return you to that moment, what you were doing, and who you were interacting with, augmenting your memory and recommending next steps.
Mad-Lib Search Interface
Another design innovation for Atlas was the use of a mad-lib style search interface. The user could type or select from a set of words or short phrases to focus or expand their search. This allowed the user to search independent of keywords. Once they had returned to the moment in time they were looking for, they could then share the moment with friends and colleagues, or simply pick-up where they left off.
The Mad-Lib style interface allowed users to have discrete control over their search. The Mad-Lib set could also be tailored to spend content types, locations, or different contexts.
Reliving a moment to plan the next
Traditional search engines index single items—a message, a webpage, a file; allowing you search for needles in a haystack; Atlas was a time machine.
Altas allowed you to easily move back in time starting with a key memory—a message, a person, that website you found, and from there recreate the moment you first read it—opening that excel file, your browser tabs, related emails, even the music you were listening to.
Since Altas’ index was driven by what the user had done or seen, rather than a general public index driven by popularity or promotional algorithms, Altas’ results were truly personalized. Unlike other search services, Atlas would know that you not only looked at those ski boots, but that your purchased them, while chatting with your friends about when and where you could skiing, and even what you had planned for dinner the first night. Then it could build out recommendations, pulling together information from the internet, your past likes, etc.