Laila Grainawi



According to the Federal Trade Commission, Americans can receive up to 40 spam texts per month and can lose an average of $1,000 per text scam.

Develop an easy to use text blocking app, that appeals to a younger demographic that are primarily concerned with annoying texts, compared to calls they just ignore. 

I designed Blockr to make spam suppression easy. Blockr uses AI & machine learning to identify spam texts before they reach you, helping make your iMessage inbox safer than ever.

Instead of other text blocking apps on the market, Blockr surfaces AI-generated suggested Keyword Packs to help make creating a “block list” and easy and fun. 

At the end, I included a few gamification explorations to encourage more text submissions to help feed the model, but we decided not to include in the MVP launch due to developer effort compared to rewards. We have every anonymized text to train Blockr so focusing on getting customers into the app and using the product was a higher priority. 

Explore the app ->

I also made some supporting App Store screenshot sets, email campaigns, landing page and website design with blog. 
This app is brand new and has a small amount of subscribers and revenue so far, but what few metrics we do have seem promising! 

Preliminary stats:

Install to Paywall view = 68% 
(Avg on App Store is 53.5%)

Paywall view to Purchase = 15%
(Avg is 7%)

Install to Trial Conversion = 12%
(Avg is 3.5%)

Role: Supporting the product design function as a team of one. 

Title: Design Director / Senior Product Designer

Wider crew:
4 Devs
1 PM
1 Marketing

Timeline: A fast one!
4-5 weeks for design/dev of the whole app

Lottie animation for welcome screen
After UX testing, we moved the phone number screen to the end as users were ‘warmed up’ to give us more information.
Gamification research
Exploration to gamify the AI model training (Not in MVP release)