AI

AI that builds travel itineraries from booking confirmations

Hacker News

AI that builds travel itineraries from booking confirmations

We’ve developed an AI-powered feature for our travel software that automatically builds structured itineraries from raw booking confirmations. Travel agencies and tour operators often receive dozens of emails from multiple suppliers — each with different formats. Our AI model parses these confirmations, detects flights, hotels, activities, and creates a clean digital itinerary with day-by-day structure, ready to share with travelers via mobile, web, or PDF. It’s built on AWS Lambda, OpenAI Responses API, and multi-step function calling with custom JSON schemas. We had to overcome challenges like handling inconsistent data formats, time zones, and merging multi-source bookings without duplication.

Visit product View on Hacker NewsOct 31, 2025

Actual performance

1points

Did not reach leaderboard

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

80%80% 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: model, email, openai · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

16%16% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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