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Castled Data – Customer engagement software on top of data warehouses

Hacker News

Castled Data – Customer engagement software on top of data warehouses

Hi HN, We're Frank, Abhilash, and Arun from Castled Data( https://castled.io ). Castled is a Customer Engagement platform built directly on top of cloud data warehouses like Snowflake, BigQuery, Redshift, and Postgres. In essence, Castled is identical to Braze and Iterable with a crucial difference - we don't keep a copy of your customer data. Instead, we enable marketers to create audiences directly on their cloud data warehouse and engage them across channels like Email, Sms, WhatsApp, Push, and In-app notifications - no SQL knowledge, no engineering favours required. Here is a quick demo: https://www.loom.com/share/217a31ac47de451992573b1b37c1b8e5 We started our journey by building an open-source Reverse ETL solution to make warehouse data actionable to marketers. However, after talking to 100s of marketers, we realised that modern B2C marketers needed to leverage billions of customer data points from the data warehouse to run personalised marketing campaigns. While Reverse ETL could sync all this data to marketing tools like Braze, Iterable, etc., the traditional marketing solutions were fundamentally not designed to store and process so much data. That's when we changed course to build a warehouse-first customer engagement solution. With Castled, marketers could finally use "all" the data from their data warehouse to engage their customers without any tradeoffs. Customer Engagement platforms have historically been built on top of transactional databases. So, solving the same use cases on top of a data warehouse optimised for analytical workloads was a significant challenge. But fortunately, B2C marketing use cases primarily warranted bulk data access, which data warehouses were uniquely designed to solve. The architectural change also enabled us to offer capabilities that were impossible with traditional solutions. As Castled is a "headless" platform - meaning we don't store any customer data - we offer a pricing model based solely on the number of team members using our platform, not the volume of data processed. The approach allowed our customers to focus on marketing efficiency without worrying about the fluctuating costs associated with data usage. We have been building Castled for more than a year and have enabled many businesses to overcome the data limitations of existing marketing platforms. If you have a data problem in marketing, feel free to sign up and try our product for free - no credit card required. We have our solution hosted at https://castled.io . We have been avid followers of HN for years now. So we look forward to your thoughts, insights, questions, and concerns about our product in the comments below. I will monitor the thread throughout the day to answer questions from the community. Also, feel free to reach out to me personally at arun@castled.io.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · 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, email, using · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, 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
69%69% 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 · Missing: mobile apps, ios, entrepreneurs
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
11%11% 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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