Yo

Yolodex – real-time customer enrichment API

Hacker News

Yolodex – real-time customer enrichment API

hey hn, i’ve been working on an api to make it easy to know who your customers are, i would love your feedback. what it does send an email address, the api returns a json profile built from public data, things like: name, country, age, occupation, company, social handles and interests. It’s a single endpoint (you can hit this endpoint without auth to get a demo of what it looks like): curl https://api.yolodex.ai/api/v1/email-enrichment \ --request POST \ --header 'Content-Type: application/json' \ --data '{"email": "john.smith@example.com"}' everyone gets 100 free, pricing is per _enriched profile_: 1 email ~ $0.03, but if i don’t find anything i wont charge you. why i built it / what’s different i once built open source intelligence tooling to investigate financial crime but for a recent project i needed to find out more about some customers, i tried apollo, clearbit, lusha, clay, etc but i found: 1. outdated data - the data about was out-of-date and misleading, emails didn’t work, etc 2. dubious data - i found lots of data like personal mobile numbers that i’m pretty sure no-one shared publicly or knowingly opted into being sold on 3. aggressive pricing - monthly/annual commitments, large gaps between plans, pay the same for empty profiles 4. painful setup - hard to find the right api, set it up, test it out etc i used knowledge from criminal investigations to build an api that uses some of the same research patterns and entity resolution to find standardized information about people that is: 1. real-time 2. public info only (osint) 3. transparent simple pricing 4. 1 min to setup what i’d love feedback on * speed : are responses fast enough? would you trade-off speed for better data coverage? * coverage : which fields will you use (or others you need)? * pricing : is the pricing model sane? * use-cases : what you need this type data for (i.e. example use cases)? * accuracy : any examples where i got it badly wrong? happy to answer technical questions in the thread and give more free credits to help anyone test

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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, email, single · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
56%56% 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: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs
50%50% 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
42%42% 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
18%18% 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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