We made Trellis – a way to run SQL query on your unstructured data
Hey HN — We're excited to share Trellis — a snowflake for 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…
We made Trellis – a way to run SQL query on your unstructured data launched on April 30, 2024 with 8 votes, #1,348 of 4,347 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
What it does
The Trellis AI engine converts complex data sources—such as financial documents, voice calls, and emails—into structured SQL format for use by data and operations teams.
In the maker’s words, at launch
Hey HN — We're excited to share Trellis — a snowflake for 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…
Does a similar job
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Structured output from LLMs without reprompting2023 · automorphic.ai · ▲174Built a tool for transforming unstructured data into structured outputs using language models (with 100% adherence). If you're facing problems getting GPT to adhere to a schema (JSON, XML, etc.) or regex, need to bulk process some unstructured data, or generate synthetic data, check it out. We run our own tuned model (you can self-host if you want), so, we're able to have incredibly fine grained control over text generation. Repository: https://github.com/automorphic-ai/trex Playground: https://automorphic.ai/playground
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Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic…
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