Power Growth Campaigns from Your BI Dashboards

Hacker News

Hello HN! I'm Hoshang, Co-founder of Airbook. We're a team of former data and ops leaders who often struggled with a fragmented analytics landscape—various specialized tools for data pipelines, SQL explorers, BI dashboards, and ad-hoc automation scripts. These tools, tailored for distinct skill sets, unfortunately failed to integrate effectively, leaving valuable insights isolated within dashboards and disconnected from the operational tools teams use daily to act on these insights. That’s why we created Airbook. Our platform is as intuitive as 'Notion' but fully equipped to handle everything from data ingestion and BI to activation. It’s designed to break down silos and bring all your data workflows into a single, cohesive environment. We're inviting 20 tech startups (typically Series A+ or >$100K MRR) to join our no-risk, 2-week pilot. We'll set everything up and demonstrate how Airbook can streamline your data operations. Ideal for: - Heads of Data or Ops (RevOps, BizOps etc) in growing tech startups. This is perfect if you’re: - Looking to set up your first data stack or consolidate multiple tools into one platform. - Keen on building cross-functional insights across tools like GA, HubSpot, SFDC, PostgreSQL, and Snowflake—whether you code or not. - Aiming to automate customer segments or engage audiences more effectively with tools like CustomerIO, Clay, HubSpot and more If this resonates, let’s chat! We’re happy to have a 15-minute call to discuss your needs and see if we’re a good fit. Here's my Calendly link - https://calendly.com/meet-airbook/interview-with-airbook Your feedback would mean a lot! Thanks in advance!

Visit product View on Hacker NewsJan 21, 2025

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3points

Did not reach leaderboard

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

82%82% 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: single, code · Missing: mac, agents, macos

73%73% 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: lua, ide, pipe · 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.

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: mrr, growth · Missing: arr, revenue, profit

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: 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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