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Drowning in customer feedback but starved of real insight?
Most product and CX teams face the same challenge: endless tickets, app reviews, and survey responses, but no clear, data-backed story they can take to leadership.
That’s why I’m fascinated by tools like Unwrap’s AI-powered customer intelligence platform, which turns raw feedback into patterns you can act on.
For example, it can surface issues like a 3X YoY rise in a specific bug, or highlight that 7.3% of Android users can’t complete a key action, the kind of evidence you need to justify roadmap or process changes with confidence.
This is where customer-centric teams will win: not just collecting feedback, but transforming it into measurable, defensible insights that drive product and CX decisions.
Would love to hear what’s working (or not) for you in the comments.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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This video is not sped up.
Crazy 8’s used to take a full sprint room: timer, sketches, cleanup, clustering, critique, and then translating rough ideas into screens.
For the #GoogleStitchChallenge, I wanted to test what happens when Stitch becomes part of that workflow.
I started with one polished Harbor Market checkout screen and used Stitch to generate 8 redesign directions in about 2 minutes: trust badges, delivery confidence, produce guarantees, substitution clarity, cost transparency, shopper support, risk-free first order, and speed-with-safety.
The magic is not just speed. It is how quickly the team can move from one idea into divergent options, then critique, compare, and choose what to refine.
Stitch turns Crazy 8’s from a sketching exercise into an interactive product design loop.
Generate → explore → compare → critique → refine
One of the most expensive AI mistakes isn't choosing the wrong model.
It's building without a way to measure success.
I've seen teams spend months building AI features only to discover:
❌ Users don't trust the outputs
❌ Accuracy isn't improving
❌ Costs keep increasing
❌ Nobody knows what's working
The best AI teams think about evaluation before implementation.
They define:
📊 Success metrics
🎯 Quality benchmarks
📈 Monitoring & observability
🔄 Continuous improvement loops
Because AI isn't a "set it and forget it" system.
It's a product that needs measurement, feedback, and optimization.
The companies winning with AI aren't just building faster.
They're learning faster.
And that starts with measuring what matters. 🚀
How does your team evaluate AI quality today? 👇
#AI #GenerativeAI #AIEngineering #LLMAgents #RAG #MLOps #ArtificialIntelligence #ProductDevelopment #FullStackDevelopment #SaaS #Contra
We explored a modern CRM dashboard, focused on helping teams manage customer data, track performance, and monitor business growth in one unified interface.
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