Get your unstructured data AI-ready in minutes
Hey HN! We're excited to announce the launch of Tonic Textual, the secure data lakehouse for LLMs. Simply stated, Tonic Textual allows you to build generative AI systems on your own unstructured data without having to spend time extracting and standardizing your data. In minutes you can build automated, scalable unstructured data pipelines that extract, centralize, standardize, and enrich data from your documents into an AI-optimized format ready for embedding, fine-tuning, and ingesting into a vector database. While in-flight, we also scan for sensitive information and protect it via…
Get your unstructured data AI-ready in minutes launched on May 28, 2024 with 38 votes, #702 of 4,539 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Tonic Textual is a data lakehouse platform that prepares unstructured data for use with large language models. It automates the process of extracting, centralizing, standardizing, and enriching data from documents into formats ready for embedding and vector databases. The platform includes built-in security features that detect and protect sensitive information through redaction or synthetic data replacement. It is designed for teams building generative AI systems who want to reduce time spent on data preparation.
written from the facts on this page · September 2026
From the sources
Protect sensitive information when building and fine-tuning AI and ML workflows. Tonic Textual protects privacy within unstructured data and keeps sensitive information safe.
In the maker’s words, at launch
Hey HN! We're excited to announce the launch of Tonic Textual, the secure data lakehouse for LLMs. Simply stated, Tonic Textual allows you to build generative AI systems on your own unstructured data without having to spend time extracting and standardizing your data. In minutes you can build automated, scalable…
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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
HelixDB – Open-source vector-graph database for AI applications (Rust)2025 · github.com · ▲237Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…
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Source: Show HN post ↗ · launch text quoted in part
