Pizza Ordering AI - 1(234)222-9290 (demo line)

Hacker News

Hey HN, would love to get some feedback on our new automated phone ordering system for Pizza Inn (a mid-size 150 franchise chain). We're currently live in one location. The demo number below will not get you any actual pizza (sorry). Nor will it actually charge you if you accidentally click the apple pay link in the confirmation text message. 1 (234) 222-9290 You should be able order most things from this menu: http://www.pizzainn.com/menu/ Here are some things to try (but feel free to give it a real test drive w/the menu) "can i have a large pepperoni pizza on thin crust and a large coke" "i'd like to order chicken wings" "can i order 2 large buffalo pizzas and 1 half gallon of sweet tea" Let me know especially if you can figure out how to have your order repeated back to you from the top, are able to successfully end your order, and what breaks the system. Some "coming soon" features: try saying "abracadabra" or "abracadabra pta" .. we're thinking "magic words" ala "coupon codes". Thanks for any feedback!

Visit product View on Hacker NewsAug 26, 2018

Share card

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: apple, new, code · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

RTree demo41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

RTree demo

Hacker News7

WebGL viewshed demo60%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

WebGL viewshed demo

Hacker News1

CashEth – demo of the system41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

CashEth – demo of the system

Hacker News4

GrannyAuth Demo41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

GrannyAuth Demo

Hacker News6

What Are You Syncing About by Ninjadev (WebGL Demo)51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

What Are You Syncing About by Ninjadev (WebGL Demo)

Hacker News2

“W

“What Are You Syncing About?” [WebGL Demo]51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

“What Are You Syncing About?” [WebGL Demo]

Hacker News1

Demo of word2vec on GoogleNews41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Demo of word2vec on GoogleNews

Hacker News3

A WebGL demo of the Observer Effect51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A WebGL demo of the Observer Effect

Hacker News15

Force12 – demo of container autoscaling40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Force12 – demo of container autoscaling

Hacker News17

Polymer 1.0 Demo (From CodePicnic)41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Polymer 1.0 Demo (From CodePicnic)

Hacker News2

Browse all products Analyze your launch