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Devsupport.ai – Automate API integrations

Hacker News

Devsupport.ai – Automate API integrations

Hi All, API integration often cycles in repetitive use cases, API specs, Libraries/SDKs, developer context & tech support. We are building a desktop app that lets SaaS API sellers design their API integration flow and lets their customer integrate APIs in seconds. SaaS API companies (including our existing customers) resort to: 1. For API documentation - readme.io, Postman and Swagger (few use PDFs too.) 2. For Chat/Support/FAQ - Freshdesk/Zendesk/Calendly/Skype/Teamviewer 3. For Developer Context - IDEs for Android/iOS/JavaScript On top of these tools, they hire developer success manager and support staff. We combine all these facets and offer them through Devsupport AI. Also, we offer manual support as a back up when automation fails to work. We have launched a public beta and are seeing a good traction from few payment gateways in South East Asia and wanted to get some constructive feedback from the community. Please take a look at https://devsupport.ai and let us know your comments/feedback.

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7points
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Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: context, apis · Missing: mac, agents, macos
90%90% 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 · Strong signals: ios, including · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
55%55% 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
28%28% 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
25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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