Designing a modular dashboard for marketers who need to monitor, interpret, and present AI visibility data.
Role
Product Designer
Team
1 Designer
4 Developers
Timeline
5 Weeks
Tools
Figma
Claude Code
Before
The original dashboard felt dense before users even knew where to look
The problem was not a lack of data. It was that everything arrived at once: weak hierarchy, too many competing modules, and no clear path from monitoring to reporting.
Problem 01
No clear hierarchy. Every module competes for attention.
Problem 02
Too many chart types and tables stacked at once.
Problem 03
No role-based path for CMOs, SEOs, or analysts.
Problem 04
Hard to turn what you see into a report teams can reuse.
Scanning
Users had to decode the layout before they could understand the data.
Investigation
The same dashboard tried to serve high-level monitoring and deep analysis at once.
Reporting
There was no clean way to turn insight into an artifact for decks or recurring updates.
Context
Different stakeholders, different needs, one dashboard
Athena had strong AI visibility data, but one fixed dashboard was forcing very different users into the same workflow. As the product matured, the real challenge was not getting more data on screen. It was helping each role get to the job they came to do faster.
What users needed
The same product had to support fast executive monitoring, deeper investigation, and repeatable reporting.
CMOs
Needed a fast pulse on brand momentum without wading through the full system.
SEOs
Needed prompt-level visibility so they could investigate what changed and why.
Analysts
Needed reusable outputs they could bring into recurring leadership updates.
Signal from research
One interview line captured the gap between having data and actually being able to use it.
“I've got all the instruments that I need. But I gotta put together a symphony now.”
CMO, Coinbase Canada
Design goals
I reframed the opportunity around the three jobs the dashboard actually needed to support: quick monitoring, deeper investigation, and easier reporting.
How might we
Turn Athena's AI visibility data into a dashboard that supports quick monitoring, deeper investigation, and easier reporting?
Compare the right slices of data
Make it easy to shift between brand terms, products, markets, and growth signals.
Create a simple reporting rhythm
Support a repeatable flow from data point to insight to next action.
Export cleanly into decks and docs
Help teams move useful evidence out of the dashboard without reformatting it by hand.
Create a shared source of truth
Give different roles one system they can trust even if they use it in different ways.
Solution
A strong default that works out of the box
Olympus opens with a structured overview: key metrics first, deeper modules below. Users can get oriented immediately without configuring anything.
One system for different views and roles
Rather than creating separate dashboards for CMOs, SEOs, and PMMs, the system lets teams select, reorder, and remove widgets to match the questions they care about most.
From dashboard to presentation in one click
The key feature was not the export button itself. It was the artifact users got from it: clean widget graphics they could drop straight into decks and recurring updates. Reset kept that workflow low-risk.
Inside Athena
then
What export creates
Share of voice widget
Brand traits widget
Slide deck
Exploration
Full report generation was too heavy
We explored a multi-step flow for generating PDF reports from dashboard data: pick a goal, select focus areas, and export a formatted document. But the workflow was too tedious. Users didn't want a new artifact. They wanted to pull specific widgets into existing decks and docs. That insight pushed export toward the simpler widget-level action instead.
Choose a report goal
Precedent
This pattern already works at scale
Stripe and Shopify both use modular, customizable dashboards. Strong defaults with the ability to add, remove, and rearrange widgets.
Stripe
Default overview with + Add and Edit controls. Edit mode lets users remove widgets. + Add opens a picker to pull in new ones.
Default dashboard
Shopify
Users can drag, reposition, and resize displayed analytics to their liking.
Analytics dashboard customization
Key Decisions
Useful by default, customizable when needed
The dashboard has to feel useful before anyone touches settings. Customization is additive, not required.
Each widget helps tell the story
Each module plays a role: summarize, compare, track momentum, or investigate. The point was not just to show data, but to make it easier to communicate.
Communication over analysis
The biggest unmet need was getting insights out of the product and into decks, docs, and recurring updates.
Safe to experiment
Reset returns users to a known baseline. Flexibility should feel approachable, not risky.
Outcome
From vision to roadmap
The Olympus concept became the foundation for AthenaHQ's Q1 dashboard redesign. Configurable widgets and the export workflow shipped as the default architecture, replacing the original fixed layout.
2x
Peak daily active users after the modular Olympus dashboard shipped.
25%
Increase in feature adoption from the Proactive Insights Engine, which translated complex AI signals into actionable guidance.
~2 months
Of engineering time saved by pivoting through user research before building a low-impact feature.
Reflection
A dashboard is rarely just a dashboard
Dashboards sit inside workflows.
The hardest part was not choosing which charts to show. It was figuring out how one system could stay coherent while serving different stakeholders and supporting the stories they need to tell.
AI accelerated direction, not decisions.
Claude Code helped me move faster through interface directions. But the real work was defining product logic, user needs, and the kind of experience Athena needed to become.