Trellis, ETL specifically for unstructured data

Hacker News

Hey HN — We're excited to share Trellis -- Run SQL like queries directly on top of your unstructured data. We've built an AI engine that turns unstructured data into structured SQL-format based on the schema you define in natural language. We spent a lot of time building ML infrastructure and realized that most data warehouses and data pipelines are not designed for unstructured data (documents, PDFs, calls). While something like a Vector database and RAG are great at search tasks, they really struggle with aggregation and SQL type queries such as 1. How many emails in the past 6 months contain complaints about the product? 2. What are the top 3 features from feature request tickets? Check out the sandbox (no sign-in required) at https://demo.runtrellis.com/ Some interesting results from analyzing Enron email can be found at https://demo.runtrellis.com/showcase/enron-email-analysis You can also run the transformation on a larger amount of data by signing up here https://dashboard.runtrellis.com/ We would love to hear your feedback and the different use cases that you come up with.

Visit product View on Hacker NewsMay 11, 2024

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

85%85% 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, pipe, io · Missing: https docs, just released, exist

84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

14%14% 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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