Integrations

Your tools finally talk to each other

UX Connection pulls insights from every layer of your design operation—then synthesizes them into patterns no single tool can see.

FigmaGA4AmplitudeHotjarGitHubJiraMixpanelLinear+ 10 more
The Difference

Individual tools vs. connected intelligence

FIGMA ALONE
Before

“Button component used 847 times”

With UX Connection

“Button component used 847 times, correlates with 23% faster task completion, and teams using it ship 2.1 days faster than teams going custom”

GA4 ALONE
Before

“Conversion rate: 3.2%”

With UX Connection

“Conversion rate: 3.2%, up from 2.4% baseline, attributable to checkout redesign with 94% confidence, representing $2.3M annual revenue lift”

HOTJAR ALONE
Before

“Rage clicks detected on checkout page”

With UX Connection

“Rage clicks on checkout traced to specific Figma component (card-payment-v2), affecting 12% of sessions, estimated $47K monthly revenue impact”

GITHUB ALONE
Before

“142 commits this sprint”

With UX Connection

“142 commits, 67% touched design system components, average PR cycle 2.1 days (vs 4.7 for custom code), correlating with 34% fewer production visual bugs”

The insight isn't in any single tool.
It's in the connections between them.

Design Tools

Where your designs live

Figma

✓ Live
What we pull:
  • •Component usage across files and teams
  • •Version history and iteration velocity
  • •Design-to-handoff time
  • •Comment sentiment and resolution rates
  • •Style guide adoption
  • + 2 more
Insight example:

“Teams using your button component ship 2.3 days faster than teams going custom.”

Sketch

✓ Live
What we pull:
  • •Library symbol usage
  • •Document version frequency
  • •Shared style adoption
  • •Plugin usage patterns
  • •Export frequency by format
Insight example:

“Symbol detachment increased 34% this quarter—design system drift detected.”

Adobe XD

✓ Live
What we pull:
  • •Component state tracking
  • •Prototype interaction paths
  • •Share link engagement
  • •Design spec views by engineering
  • •Asset export patterns
Insight example:

“Engineers viewed checkout specs 12x more than other flows—complexity signal detected.”

Analytics Platforms

Where your metrics live

Google Analytics 4

✓ Live
What we pull:
  • •Conversion events and funnels
  • •User flow paths
  • •Engagement metrics (time, depth, frequency)
  • •Audience segments
  • •E-commerce transactions
  • + 2 more
Insight example:

“Users who encountered your new onboarding flow convert at 23% higher rate with 94% confidence.”

Amplitude

✓ Live
What we pull:
  • •Behavioral cohorts
  • •Retention curves
  • •Feature adoption rates
  • •Funnel conversion
  • •Path analysis
  • + 2 more
Insight example:

“Feature adoption correlates with 3.2x higher 12-month retention for users in the redesigned segment.”

Mixpanel

✓ Live
What we pull:
  • •Event streams
  • •Funnel performance
  • •Cohort retention
  • •User property distributions
  • •Signal detection
  • + 1 more
Insight example:

“Rage click events dropped 67% after the form redesign shipped—friction eliminated.”

Heap

✓ Live
What we pull:
  • •Auto-captured events
  • •Session recordings metadata
  • •Conversion paths
  • •Effort score calculations
  • •Retroactive funnel analysis
Insight example:

“Average clicks-to-checkout dropped from 8.3 to 4.1 post-redesign.”

Behavior & Research

Where your insights live

Hotjar

✓ Live
What we pull:
  • •Heatmap density data
  • •Scroll depth percentages
  • •Click maps
  • •Rage click locations
  • •Dead click detection
  • + 2 more
Insight example:

“47% of users never scroll past the pricing table—content hierarchy issue detected.”

FullStory

✓ Live
What we pull:
  • •Frustration signals (rage clicks, error clicks)
  • •Session metrics
  • •Conversion funnels
  • •Page performance impact
  • •Search behavior
  • + 1 more
Insight example:

“Error click rate on mobile checkout is 4.2x higher than desktop—responsive issue flagged.”

Maze

✓ Live
What we pull:
  • •Usability test completion rates
  • •Task success metrics
  • •Misclick rates
  • •Time on task
  • •Path analysis
  • + 1 more
Insight example:

“Prototype B had 89% direct success vs 54% for A—clear winner before development.”

Dovetail

✓ Live
What we pull:
  • •Interview insight tags
  • •Theme frequency
  • •Sentiment analysis
  • •Highlight reels metadata
  • •Research project velocity
  • + 1 more
Insight example:

“'Confusing navigation' tagged in 34 of 50 interviews—top user pain point confirmed.”

Development & Code

Where your designs ship

GitHub

✓ Live
What we pull:
  • •Commit frequency by component
  • •PR review time
  • •Design file references in commits
  • •Component import statements
  • •Breaking change detection
  • + 1 more
Insight example:

“Design system components average 2.1 day PR cycle vs 4.7 days for custom implementations.”

GitLab

✓ Live
What we pull:
  • •Merge request metrics
  • •Pipeline success rates
  • •Component usage in codebase
  • •Design token adoption
  • •Issue-to-merge velocity
Insight example:

“Teams using design tokens ship 34% fewer visual bugs to production.”

Bitbucket

✓ Live
What we pull:
  • •Pull request analytics
  • •Branch patterns
  • •Code review cycles
  • •Deployment frequency
  • •Component traceability
Insight example:

“Frontend deploys increased 23% after design system v2 adoption.”

Project Management

Where your work is tracked

Jira

✓ Live
What we pull:
  • •Issue cycle time
  • •Story point velocity
  • •Bug density by component
  • •Design-tagged ticket flow
  • •Sprint completion rates
  • + 1 more
Insight example:

“UX-tagged tickets resolve 1.8 days faster when design specs are attached.”

Linear

✓ Live
What we pull:
  • •Cycle metrics
  • •Project progress
  • •Issue relationships
  • •Label analytics
  • •Team velocity
  • + 1 more
Insight example:

“Design QA issues dropped 45% after component library standardization.”

Asana

✓ Live
What we pull:
  • •Task completion rates
  • •Project timeline adherence
  • •Workload distribution
  • •Custom field analytics
  • •Goal progress tracking
Insight example:

“Design review bottleneck identified—average 3.2 day wait for approval.”

Data Warehouses

Connect to your source of truth

Snowflake

✓ Live
What we pull:
  • •Custom metric queries
  • •Revenue attribution data
  • •Customer lifetime value
  • •Cohort definitions
  • •Any structured business data
Insight example:

“Direct connection to your source of truth—no data discrepancies between tools.”

BigQuery

✓ Live
What we pull:
  • •Custom SQL queries
  • •Event stream data
  • •Cross-platform user matching
  • •Revenue and transaction data
  • •Custom attribution models
Insight example:

“Query your entire data warehouse with attribution context automatically applied.”

“We could build this ourselves”

You could. Here's what it takes:

18
API integrations to build and maintain
47
Distinct data points to normalize
6-9 mo
Estimated build time
$180K+
Estimated engineering cost

Or $299/month. Connected in 15 minutes.

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