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Inspec – Specification scheduling software for interior designers

Hacker News

Inspec – Specification scheduling software for interior designers

Hi HN, I'm Nick and I built Inspec with my partner Caitlin (she's the interior designer, I'm the developer). Inspec lets interior designers create and manage FF&E schedules - basically spec sheets listing everything that goes in a room before the furniture: flooring, paint, countertops, lighting, fixtures, etc. This process is currently exclusively done using Excel, and while this works to some degree, it comes with a lot of manual work and clunkiness. When Caitlin started working remotely, I watched her deal with these issues daily. So I spent the past 9 months building a better solution. Key Features: - Realtime collaboration - Revision control (versioning) - Professional PDF exports - QR codes to print out on-site for contractors, builders etc. to always have the latest version - Completely customisable fields and a familiar workflow to Excel to keep learning curve small When we first started building this we thought it was completely unique, but turns out there are 2 competitors. I think our positioning is quite different though as we seek to only replace this document creation, rather than overhaul the entire project management and try to be a complete solution. So that’s helped to guide our focus, but also validated that interior designers are looking to modernise their software. Tech stack: I used my SaaS boilerplate which is just a modified T3 stack template with everything set up just how I like it. So Next.js with the pages router, TS, Trpc, Prisma + PostgreSQL, and TailwindCSS. I’ve also got Pusher for realtime collaboration, Redis and BullMQ for keeping a seperate background worker for PDF and web scraping jobs. I can’t imagine many interior designers are on HN, but figured I would share this for anyone who wants to take a look :) feedback always welcome! The landing page is at https://inspec.design

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
90%90% 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: project management, using, code · Missing: mac, agents, macos
62%62% 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
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive, builder · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
13%13% 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.

Correct prediction on native model

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