AI-powered Email Assistant and Product Discovery for fast growing teams

Hacker News

We built Neferdata (www.neferdata.com) out of a personal need to eliminate email distractions while staying open to emerging trends and solutions. On one hand the cold sales emails are getting completely out of control. On the other, if you are anything like me, you are always looking for cool tools that others are excited about. These concepts may not seem directly related but our belief is that we can provide a free utility of a better email experience while learning about what our users actually get value from. Then we can use that knowledge to drive more transparency around SaaS solutions that are worth trying out. This is why the product has 2 main features: Email Assistant and Product Discovery. We use large language models (LLMs) to summarize and infer depersonalized insights from the always-free email filter side, allowing us to better understand interactions between different company types. These insights focus on the nature of the relationship between communicating parties rather than their individual identities. Because of that the insights are depersonalized by default. The insights power our graph of SaaS providers and users. In many ways, this works similarly to how TikTok knows what you *will* like and hides the stuff you won’t, just applied to tech products. You can try our AI product recommendations without the need to create an account at www.neferdata.com/discover. If you’d like to get recommendations that are more tailored to the industry you are in you’ll need to sign in with Google or Microsoft. And with the free Email Assistant we can even further personalize the recommendations, while providing you with tools to contain the cold sales email frenzy. The product is functional but still evolving, and your input will help make it even better. Try it out and let us know what you think. Your time and thoughts are greatly appreciated!

Visit product View on Hacker NewsAug 8, 2023

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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, google, user · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

49%49% 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, google, users · Missing: mobile apps, ios, entrepreneurs

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

40%40% 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

10%10% 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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