Onboard faster by importing data from your customers' DBs

Hacker News

Hey all! Wanted to share a project ( https://recaseai.com ) I’ve been working on, which helps B2B SaaS startups integrate with their customers’ data stores (like Snowflake, Looker, etc.) much faster. I previously worked on a tool targeted at fintechs, and we required certain data points from their internal DB. The fintechs couldn't dedicate engineering effort to send data via an API, so we had to build out integrations with their data stores and query the relevant data. This meant that: - We had to create custom integrations and queries for each customer's DB - Our customers would worry about how we’d store their DB credentials For a startup, this was limiting. As such, we built Recase, which is basically a reverse ETL that allows your SaaS to build integrations and sync custom data points from your customers’ databases / data warehouses easily. Here’s how it works: 1. You use our embedded page or API to save your customers’ DB credentials / API keys 2. You or your customer define a schema to send to your endpoint 3. Your customer can select which fields from their DB they’d like to map to the schema, via our UI 4. Data sync can then be triggered via API or CRON Love to hear your thoughts! PS. We had a few early users who enjoyed the tool so now we're looking to take on a few more for our invite-only beta :)

Visit product View on Hacker NewsAug 31, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

57%57% 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: users · Missing: mobile apps, ios, personal

33%33% 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

30%30% 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

22%22% 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.

Incorrect prediction on native model

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