We

We're building a better future for gig work starting with onboarding

Hacker News

We're building a better future for gig work starting with onboarding

Today we’re announcing a new product we’ve had in the works for the past few months! Corridor is an embeddable no-code solution for HR and Ops teams to onboard their flexible workers at speed and scale. Create, launch, and optimize your onboarding flow, but faster. Improve candidate conversion, automate tasks, collaborate with your team on design, and seamlessly plug-in your favorite HR tools. We’re building this to help our customers and their candidates have a better all-around onboarding experience. Along the way, we captured feedback around how difficult it is to design, manage, and track all the different paperwork and processes that go into getting a worker to their first day of work (not to mention dealing with trying to make this a good UX as apart of your product). We landed on building something that balances ease of creation and delightfulness by focusing on the candidate experience first and working backward toward designing such experiences. We’re still in early beta so we’re looking for testers, early adopters, and design partners. If interested, please feel free to schedule a demo or sign up for early access on our website. P.S. I'm giving away $30 Doordash gift cards in exchange for a 30 minute user research interview...message me at vu.francois@withcorridor if interested! :)

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, tasks · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, 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
40%40% 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 · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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