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Augmented Reality Knowledge Management for Facility Maintenance Teams

Hacker News

Augmented Reality Knowledge Management for Facility Maintenance Teams

Hello friends at HN. Aircada is an AR knowledge management platform to help facility maintenance teams capture, retain, and transfer knowledge. Here is a link to a demo video showcasing the features - https://youtu.be/QXwTlNC3C7A During the pandemic, we noticed that some technical achievements from Microsoft's Spatial Anchors were finally at a point of opening the door to adoption for a magnitude of AR use cases, that in the past, weren't a viable option. And paired with the processing power and lidar capabilities of new phones, it seemed location based AR finally wasn't a pain in the a%% to use. Until now, QR codes and advanced computer based setups were required, and just were not worth the effort for most. But now, all with a mobile device - scan an area, place AR content, then scan that area again and have it show up exactly where you placed it. Awesome. However, there were still some problems with Microsoft's system. Locating AR content sometimes took 30+ seconds, and cost up to a dollar to do. So we built on top of their system and managed to cut that cost by about 10x, and increase the speed of locating content by about 10x. And by doing so, the use cases that opened up were quite vast! With our backgrounds in industrial automation, we set out to address the following problem - The growing industrial skills gap. The US Department of Labor estimated the 50% of the US workforce was set to retire over the next 5-10 years. In utilities and manufacturing, where turnover is high, this is becoming a huge issue. Imagine Bob, a senior maintenance technician with 35 years of experience, about to retire. When Bob walks out the door, so do many of his trade secrets in how to operate the facility. After speaking with several managers at these facilities, there is an eery since of panic approaching, where they are wondering how they will transfer Bob's knowledge to the younger generation before it's too late. Meanwhile, when hiring the younger generation, Bob continually has to hands on train these new hires with the same knowledge over and over again, just to watch them quit a year or so later. And this is where location based AR can show some real value. If Bob can capture his knowledge and place it next to the machines where it'll live forever, and be accessible in seconds from any mobile phone, it's as if Bob never actually left. Sort of like Obi-Wan Kenobi in the original Star Wars. New hires will be able to train themselves with the autonomy they so desire, while Bob can continue addressing the facilities larger issues. Anyway, I appreciate you reading, and I'll finish with this - we'd love to hear your thoughts, feedback, and or any questions you may have. The market and use cases for what we've built it quite vast, so narrowing it down has been one of the most challenging aspects.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% 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: mac, computer, new · Missing: agents, macos, agent
74%74% 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
65%65% 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: platform · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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