This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Standard AI ignores existing Access Control Lists, risking massive corporate data leaks. We built an air-gapped RAG engine and native secure file drive that enforces permissions at the document level. By extracting content into clean Markdown, we eliminate heavy data duplication. With hybrid retrieval and audit logging, the LLM only generates answers from authorized data. Deploy on-premise and securely search without sacrificing compliance.
Hey Product Hunt! I’m part of the engineering team building CordonData.
When we started building internal AI tools, we hit a massive security wall: standard RAG pipelines typically ignore existing Access Control Lists (ACLs). If an employee queries an LLM, there is a very real risk the model retrieves and summarizes a sensitive financial or HR document they shouldn't have access to.
To solve this, we spent the last few months building an engine that actually respects source permissions. Instead of copying massive raw files and creating a nightmare of data duplication, we extract content from your existing corporate silos directly into clean, lightweight Markdown.
We run a dual-engine hybrid retrieval, but the core innovation is that we enforce Document-Level Security (DLS). The system inherits permissions directly at the document level. At query time, the LLM only receives context from the Markdown documents the executing user is explicitly authorized to view.
Because enterprise data is sensitive, the entire system is designed to be air-gapped and deployed on-premise, complete with universal audit logging. For teams that don't have a centralized file repository, we also built a secure internal drive directly into the frontend to manage and permission files natively.
If you are an Enterprise Architect, Data Lead, or just dealing with the exact headache of keeping LLMs secure behind a firewall, we’d love for you to check it out.
We would love any brutal feedback on the architecture or our approach to handling Document-Level Security!
CordonData was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #58 on the daily leaderboard. Standard AI ignores existing Access Control Lists, risking massive corporate data leaks. We built an air-gapped RAG engine and native secure file drive that enforces permissions at the document level. By extracting content into clean Markdown, we eliminate heavy data duplication. With hybrid retrieval and audit logging, the LLM only generates answers from authorized data. Deploy on-premise and securely search without sacrificing compliance.
On the analytics side, CordonData competes within Developer Tools, Artificial Intelligence and Search — topics that collectively have 997k followers on Product Hunt. The dashboard above tracks how CordonData performed against the three products that launched closest to it on the same day.
Who hunted CordonData?
CordonData was hunted by Saikat Kumar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of CordonData including community comment highlights and product details, visit the product overview.
Saikat Kumar