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AI · alternatives · 2026

24 alternatives to CordonData

Air-gapped RAG engine & secure drive with native DLS & Audit

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1

    Make any data RAG-ready in seconds

    2025 · its alternatives →

  2. 2

    Secure decentralized storage with AI Assistant

    2024 · its alternatives →

  3. 3

    Hey HN, we’re Adi and Arnav. A few months ago, we hit a wall trying to get LLMs to answer questions over research papers and instruction manuals. Everything worked fine, until the answer lived inside an image or diagram embedded in the PDF. Even GPT‑4o flubbed it (we recently tried O3 with the same, and surprisingly it flubbed it too). Naive RAG pipelines just pulled in some text chunks and ignored the rest. We took an invention disclosure PDF (https://drive.google.com/file/d/1ySzQgbNZkC5dPLtE3pnnVL2rW_9...) containing an IRR‑vs‑frequency graph and asked GPT “From…

    2025 · github.com · its alternatives →

  4. 4

    Get your unstructured data AI-ready in minutes

    2024 · tonic.ai · its alternatives →

  5. 5

    I built a SQLite VFS in Rust that serves cold queries directly from S3 with sub-second performance, and often much faster. It’s called turbolite. It is experimental, buggy, and may corrupt data. I would not trust it with anything important yet. I wanted to explore whether object storage has gotten fast enough to support embedded databases over cloud storage. Filesystems reward tiny random reads and in-place mutation. S3 rewards fewer requests, bigger transfers, immutable objects, and aggressively parallel operations where bandwidth is often the real constraint. This was explicitly inspired…

    Mar 2026 · github.com · its alternatives →

  6. 6

    Work securely in the age of AI

    2025 · polymerhq.io · its alternatives →

  7. 7

    I've built an airgapped Retrieval-Augmented Generation (RAG) system for question-answering on documents, running entirely offline with local inference. Using Llama 3, Mistral, and Gemini, this setup allows secure, private NLP on your own machine. Perfect for researchers, data scientists, and developers who need to process sensitive data without cloud dependencies. Built with Llama C++, LangChain, and Streamlit, it supports quantized models and provides a sleek UI for document processing. Check it out, contribute, or suggest new features!

    2024 · github.com · its alternatives →

  8. 8

    Hey hacker news, We’re the cofounders at Psychic.dev (http://psychic.dev) where we help companies connect LLMs to private data. With the launch of Llama 2, we think it’s finally viable to self-host an internal application that’s on-par with ChatGPT, so we did exactly that and made it an open source project. We also included a vector DB and API server so you can upload files and connect Llama 2 to your own data. The RAG in RAGstack stands for Retrieval Augmented Generation, a technique where the capabilities of a large language model (LLM) are augmented by retrieving information…

    2023 · github.com · its alternatives →

  9. 9

    I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…

    2023 · github.com · its alternatives →

  10. 10

    Multimodal RAG platform, from POC to production in minutes

    Apr 2026 · ignitionrag.com · its alternatives →

  11. 11

    Hey HN, I wanted to share something I’ve been working on: *RAG-Guard*, a document AI that’s all about privacy. It’s an experiment in combining Retrieval-Augmented Generation (RAG) with AI-powered question answering, but with a twist — your data stays yours. Here’s the idea: you can upload contracts, research papers, personal notes, or any other documents, and RAG-Guard processes everything locally in your browser. Nothing leaves your device unless you explicitly approve it. ### How It Works - * Zero-Trust by Design*: Every step happens in your browser until you say otherwise. - * Local…

    2025 · github.com · its alternatives →

  12. 12

    Intuitively secure your data - it's one-TAP-simple

    2023 · fusiontap.com · its alternatives →

  13. 13

    Hey HN, solo dev here. After years of frustration with how LLMs handle complex documents, especially PDFs with tables, I decided to build a solution myself. My approach uses a Markdown conversion step to preserve the table structure, which seems to work surprisingly well for chunking. This little parser is the first public piece of a much larger, privacy-focused AI platform I'm building. I'm pretty much running on fumes financially, so any feedback, critique, or support is massively appreciated. Happy to answer any questions about the approach!

    Nov 2025 · github.com · its alternatives →

  14. 14

    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…

    2024 · tonic.ai · its alternatives →

  15. 15

    RAG-Ready, Any Data & Any Form

    2024 · its alternatives →

  16. 16

    Connect external data to AI apps in minutes

    2025 · ragengine.io · its alternatives →

  17. 17

    Use AI on confidential files — nothing ever leaks

    Jul 2026 · occulta.fr · its alternatives →

  18. 18

    RAGLite is a Python package for building Retrieval-Augmented Generation (RAG) applications. RAG applications can be magical when they work well, but anyone who has built one knows how much the output quality depends on the quality of retrieval and augmentation. With RAGLite, we set out to unhobble RAG by mapping out all of its subproblems and implementing the best solutions to those subproblems. For example, RAGLite solves the chunking problem by partitioning documents in provably optimal level 4 semantic chunks. Another unique contribution is its optimal closed-form linear query adapter…

    2024 · github.com · its alternatives →

  19. 19

    Zero-cloud SQLite FTS5 RAG engine & GitHub Action

    Aug 2026 · github.com · its alternatives →

  20. 20

    Grounding your AI in your own data, with absolute precision

    Jul 2026 · rag.webygraphy.co.uk · its alternatives →

  21. 21

    Linter for dangerous Postgres migration patterns in Diesel and SQLx. Prevents downtime caused by unsafe schema changes. - ayarotsky/diesel-guard

    Dec 2025 · github.com · its alternatives →

  22. 22

    Your AI-native Drive for organizing, using and sharing files

    Nov 2025 · vaultsage.ai · its alternatives →

  23. 23

    Hey HN! If you’re at all interested in LLMs/LLM-apps, you’ve probably heard of RAG: Retrieval-Assisted Generation, i.e. retrieving relevant documents to give to your LLM as context to answer user queries. Today, I’m releasing RAGatouille v0.0.1, whose aim is to make it as easy as can be to improve your RAG pipelines by leveraging state-of-the-art Information Retrieval research. As of right now, there’s quite a big gap between common everyday practice and the IR literature, and a lot of the gap is because there just aren’t good ways to quickly try out and leverage SotA IR techniques.…

    2024 · github.com · its alternatives →

  24. 24

    Dcup is an open-source RAG-as-a-Service that turns your documents into a self-hostable, extensible AI-powered search engine. Connect AWS S3, Google Drive, Dropbox, or direct uploads, then chunk, embed, and index every file in Qdrant. Leverage hybrid semantic+keyword search, optional re-ranking, and lightning-fast OpenAI responses. I believe any tool that works with data should be fully open source—Dcup gives you full control without black boxes or vendor lock-in.

    2025 · dcup.dev · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →