Case Study
UT Intake app
UT Intake app
Time-to-Approval
-75%
-75%
Context
Create a functional tool to help Design/UX/Product delivery teams keep outcome-based user testing from becoming a bottleneck.
The app enables users to rapidly cue and submit shell (candidate) user tests, adds human approval with spend controls, calibrates each test with agentic-inferred AARRR, test scenario, screeners and KPI metrics.
00:00 The Problem 00:55 Shell test submission 02:11 Creating an Approved test 02:37 Taskflow optimisation
No business metrics tie-in
A point of friction for users was the cognitive overhead of defining which AARRR metric and KPI should be used. Users were skipping those inputs and I didn't want to force them as being mandatory.
Iterated to generate the metrics using agentic inference from the 'What are you trying to learn' field data.
Before
[ Prototype URL ] → [ Test Shell Context ] → [ AARRR and KPI Manual inputs, Optional]
After
[ Prototype URL ] → [ Test Shell Context ] → [ AARRR and KPI agentic inference ] → [ Optional user manual adjust/override ]
Before
[ Prototype URL ] → [ Test Shell Context ] → [ AARRR and KPI Manual inputs, Optional]
After
[ Prototype URL ] → [ Test Shell Context ] → [ AARRR and KPI agentic inference ] → [ Optional user manual adjust/override ]
Spend visibility hesitation
Early feedback showed that effective test prioritisation was being held back by an absence of spend visibility. With fixed budgets for testing, Approvers needed controls to manage total spend and test coverage.
Before
[ Macro scorecards ] → [ Test Shells Table ]
After (Reduced time-to-test)
[ Macro scorecards ] → [ Budget indicator bar ] → [ Available credits and allocation usage ] → [ Test Shells Table ]
Before
[ Macro scorecards ] → [ Test Shells Table ]
After (Reduced time-to-test)
[ Macro scorecards ] → [ Budget indicator bar ] → [ Available credits and allocation usage ] → [ Test Shells Table ]
