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Delphi – A Natural-Language-to-SQL AI for the Modern Data Stack

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Delphi – A Natural-Language-to-SQL AI for the Modern Data Stack

Hi HN! Before my current job, I worked in data analytics and spent 3 years leading data at an ed-tech nonprofit. A big problem we had was "self-service" data. Everyone wanted to use data and make data-driven decisions, but it was often too complicated. So our data team spent a lot of time answering ad-hoc questions and requests to "do a quick pull." Tons of data teams face this problem. It's the source of a bunch of memes and jokes in the community. So I'm super excited to share Delphi. Delphi takes plain English questions like "how much money did we make from gift cards in California last week?" and answers them using the data in your company's data warehouse. We're using OpenAI's Codex, but unlike some of the text-to-SQL products you might have seen, Delphi integrates deeply with the Modern Data Stack tools teams are already using. Instead of just generating arbitrary SQL, this lets Delphi scale to real-life data sets. Plus, users can use Delphi as a jumping-off point to explore data in their favorite BI tool. Our prototype, a Slack bot, is ready for you to try today! And if you're using dbt's Semantic Layer, you can run queries against your own data. Check out the website for more, and reach out if this is a space that interests you! Thanks everyone <3 Michael

Visit productView on Hacker NewsNov 17, 2022
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
93%93% 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: slack, user, codex · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
74%74% 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: answers, users · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, users · Missing: platform, intuitive, reviews
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: profit · 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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