Acai — Analytics built for how designers think
Most analytics tools weren't built for designers. Acai changes that surfacing insights that are visual, contextual, and ready to act on.
Project background
Acai is a data analytics tool built specifically for designers. It pulls in data from tools like Mixpanel, Hotjar, and Google Analytics , and instead of presenting raw numbers, it takes a screenshot of your actual product and gives you insights based on what it sees in that visual.
The goal: give designers the clarity they need to act, without requiring them to become analytics experts.
The challenge
Every product team has at least one designer. But for a designer's work to land, it needs to be backed by both qualitative and quantitative data — not just one or the other.
The problem is that most designers don't have direct access to the numbers. They rely on project managers or data analysts to pull insights, which either slows down the process or means decisions get made without the full picture.
And as the role of the designer expands, expected to contribute to strategy, not just craft, that gap becomes harder to ignore.
With Acai, designers can:
Make data-driven decisions independently
Contribute to product strategy with confidence
Move faster without waiting on analysts
Take full ownership of the product experience
Role
Founding Product Designer
Timeline
Q4-Q2
Team
2 designers
2 Developers
2 Project manager
Tools
Figma, Storybook, code connect
-Product desinger
THE APPROACH
Most designers are designing blind.
Before building anything, we needed to hear directly from the people who would use it. We interviewed designers across levels — from junior to staff — to understand their current pain points around data, how they work today, and what we might have missed in our initial internal brainstorm.
Key take aways:
1
Trends & iterations
Designers need to see how changes perform over time, and quickly decide whether to revert or move forward
2
Data curated for designers
Not every data point matters to a designer. They need a focused set of metrics relevant to design decisions, not a full analyst dashboard
3
Actionable insights
Raw data isn't enough. Designers need interpretation — clear next steps attached to what the numbers are saying
USERFLOWS
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Tom is a designer trying to understand why users are dropping off before adding anything to their cart. We used his scenario to pressure-test our MVP — mapping his end-to-end flow to figure out exactly what Acai needed to support on day one.
From account creation to exporting an insight, Tom's flow defined the core features we needed to build.
A pivotal moment
Acai was originally designed as a Figma plugin. It wasn't until we mapped the flows in full that we realized a plugin is an extension of an existing product — it lives in someone else's house. For the depth of data, the AI engine, and the insight experience we wanted to give designers, a plugin would have been too constrained. That's when we made the call to build Acai as a standalone product.
What mapping the flows uncovered
Multiple streams in a project
If a user has more than one stream, how do they select which one to analyze? A decision that would have been missed without mapping the flow end to end.
Disconnected integrations
If the connection between a site and a partner breaks, how does the user know? How do they reconnect without losing their work?
AI vs. manual input
Where Acai generates the insight automatically versus where the designer needs to stay in control of the decision
Empty state
What a designer sees when there's not enough data yet — a moment that needed its own solution, not just a blank screen
MVP DEFINITION
Meet tom!
Tom is a designer trying to understand why users are dropping off before adding anything to their cart. We used his scenario to pressure-test our MVP — mapping his end-to-end flow to figure out exactly what Acai needed to support on day one.
From account creation to exporting an insight, Tom's flow defined the core features we needed to build.
Based on Tom's needs, Five things had to exist on day one
Pages
View insights tied to each page of your product
Flows
Track how users move through your product end to end
Data source
Set up and manage your workspace independently
AI chatbot
Ask questions and get contextual design suggestions
Project management
Organize insights across multiple projects and clients
What we built for Tom.
Before touching a single wireframe, we mapped the full vision of Acai — everything it could eventually become. From there, we cut ruthlessly, prioritizing only what Tom needed to complete his task end to end. The diagram below shows that full picture — blue for MVP, grey for what comes next.
WIREFRAMES
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Tom is a designer trying to understand why users are dropping off before adding anything to their cart. We used his scenario to pressure-test our MVP — mapping his end-to-end flow to figure out exactly what Acai needed to support on day one.
Old
Empty states dominated the view for new users
"Try sample data" was the most prominent action — not project creation
A growing project list would quickly become overwhelming
New
Empty states dominated the view for new users
"Try sample data" was the most prominent action — not project creation
A growing project list would quickly become overwhelming
What we built for Tom.
One of the earlier navigation decisions we had to revisit was how users move through the product. Initially, we relied on a combination of main navigation and contextual sub-navigation within pages — but it quickly became cluttered and hard to predict. We simplified to a single navigation system: the main nav handles all movement throughout the site, and each page is self-contained. No competing layers, no confusion about where you are.
Old
Main navigation, sub-navigation, and AI chat panel all competing for space
Three layers of navigation made the interface feel cluttered and overwhelming
The AI chat took up permanent screen real estate regardless of whether the user needed it
New
AI chat collapsed into a floating action button (FAB) — out of the way until needed
One click expands the chatbot in context, without disrupting the current view
Option to go full screen for deeper conversations when needed
Cleaner interface — navigation hierarchy reduced from three layers to two
DESIGN
Do content for design
Tom is a designer trying to understand why users are dropping off before adding anything to their cart. We used his scenario to pressure-test our MVP — mapping his end-to-end flow to figure out exactly what Acai needed to support on day one.
