Case Study — Agentic AI · UX Intelligence System
From customer feedback to a revenue-ranked fix list, with no one in the loop
A working multi-agent system that reads raw feedback, finds the patterns that matter, and tells you which one is costing you money first.
42
pieces of feedback read, all at once
3
issues caught that were actually costing money
60s
from complaint to a ticket ready to build
0
hours spent sorting through it by hand
The problem
Most companies don't lose revenue to one big failure. They lose it to small friction someone already reported, sitting in a feedback tool, unread, for weeks.
By the time a product or design team manually reads through feedback, clusters the patterns, and writes a spec engineering can act on, the cycle has already cost a sprint — sometimes a quarter. The information to prevent it was there the whole time. Nobody had the hours to find it.
Before vs after
What changed for one real issue
A customer mentioned a broken promo code field. Here's what used to happen next, and what happens now.
3–7 days
The complaint sits in a spreadsheet. A designer finds out by accident, days later, after 12 more customers hit the same wall.
Feedback comes in → Designer finds out, by chance → Sits in a spreadsheet
60 seconds
The same complaint gets checked against the live product, confirmed as real, and turned into a ticket an engineer can start today.
Feedback comes in → Checked live → Ticket ready to build
Time to reach an engineer, drawn to scale
How it works
Four steps, no human sorting in between
Not a dashboard you have to read. A system that reads for you, and only tells you what's worth your time.
01
Feedback in
Raw customer feedback — support tickets, reviews, survey text — feeds in as-is. No formatting, no cleanup.
02
Related complaints get grouped together
Not keyword-matching — it reads what people actually meant and puts the same underlying issue in one pile.
03
Sorted by what it's costing you
Checkout drop-off, churn risk, onboarding blockers — ranked by business impact, not just how many people complained.
04
Spec, ready to ship
The top issue is written up as an engineering-ready spec automatically. No second meeting to clarify scope.
Three issues, ranked the way a CFO would rank them
Not a list of UX nitpicks. Each finding is tied to what it actually costs the business.
- Revenue risk
A hidden promo code field was quietly killing checkout conversion
Customers were abandoning carts to search for a discount code they couldn't find on the page, then not coming back. The system flagged this as the highest-priority fix based on direct revenue exposure, not just complaint frequency.
Priority: Ship first — direct checkout revenue impact
- Expansion risk
A broken password reset flow was quietly blocking new seats
New users invited by existing customers were getting stuck at password reset, a silent tax on every expansion sale, invisible unless someone cross-referenced onboarding drop-off with support tickets.
Priority: Next sprint — blocks seat expansion revenue
- Retention risk
Mobile navigation was burying account and billing settings
Lower urgency than the other two, but a slow drag on self-serve retention. Customers who couldn't find billing settings were more likely to contact support or churn quietly instead of resolving it themselves.
Priority: Queued — retention and support-cost drag
What this means for your team
The same system, pointed at your feedback
Time back
What used to take a PM a week of reading and sorting now happens on its own — freeing that time for decisions only a person can make.
Risk caught earlier
Problems that cost real money get caught before they compound across a quarter, not after they show up in churn numbers.
No new tools to manage
Built to connect to what your team already uses — nothing new to roll out, train on, or maintain.
Want to see it run on your feedback?
15 minutes, live. Bring your own feedback data or use a sample set. No slides, no pitch deck, just the system working.
Send me your workflow