Enrichify – Data Enrichment Aggregation and Orchestration

Hacker News

Hey HN, While working on a solution to lead scoring and qualification in my current company Kohomai, we were considering making as a stand-alone product a feature that we've built for Kohomai. We're at a stage where we really need some outside perspective. We've noticed that data enrichment providers have various coverage that is quite different from region to region and based on enrichment type. The testing is limited and the pricing is not always transparent. That's how Enrichify was born. Enrichify is designed to provide B2B companies with detailed profiles of their leads, giving them a comprehensive understanding of whom they're engaging with. This isn't just about knowing the profile of a person, the company size or location - it's about understanding their funding status, technology stack, market position, business model, info from websites and more. Here's where we are now: Right now, Enrichify is being used as part of Kohomai’s product, where we're processing tens of thousands of records. Customers have different enrichment needs and this is what we set up in seconds in our product to answer those needs and sometimes multi-step enrichment. What we're Looking For: I'm at the point where I've validated the idea of Enrichify as a stand-alone app with a few close connections, and the response has been promising. But I know there's a ton of wisdom in this community, and your input could be incredibly valuable. 1) How do you currently enrich your leads? 2) What challenges do you face with data enrichment? 3) Would deeper company insights be valuable in your lead enrichment process? 4) Any features you'd love to see that could make your life easier? I’m all ears for any constructive criticism, ideas, or just your two cents on the concept. Feel free to check out what I’ve built at EnrichifyApp.com and let me know your thoughts! Thanks for taking the time to read this and for any feedback you provide. Looking forward to learning from you all! Best, Aleksa

Visit product View on Hacker NewsNov 14, 2023

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

97%97% 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 · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% 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: lua, ide, io · Missing: https docs, excited, just released

21%21% 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

15%15% 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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