A knowledge base vs a RAG pipeline
Ragie's job is to take a pile of documents and turn them into a context-aware API. You upload PDFs, Word docs, and Markdown. Ragie handles chunking, embeddings, hybrid retrieval, and reranking. Your AI calls the hosted MCP endpoint and gets relevant snippets back. The hard parts of building a RAG system are abstracted away.
Hjarni is a different product. You write notes in the app. The notes are Markdown, organized in folders, tagged, linked. The built-in MCP server lets ChatGPT, Claude, and other clients search and read them. There is no upload step because the notes already live in the app.
Different inputs, different workflows
With Ragie, the document is the source of truth. You write a brief in Word, upload it, and any change goes through re-uploading or syncing. The team's writing tools stay where they are.
With Hjarni, the note is the source of truth. You write in the app, edit in the app, and the next time your AI asks, it sees the edit. There is no ingestion lag because there is no ingestion.
Ragie indexes the documents you already have. Hjarni is the place you write the notes in the first place.
When Ragie is the better fit
If you already have hundreds or thousands of files, a steady ingestion pipeline matters, and you want to ship a RAG feature inside your own product, Ragie is built for that. Their value is in the pipeline you do not have to operate.
When people choose Hjarni instead
The switch usually happens when the workflow is "I want to write notes that my AI can read", not "I have a corpus of files I want to query". Hjarni is the knowledge base and the MCP server in one product. There is no document pipeline to feed, no embedding model to pick, no chunking strategy to tune. You write a note, your AI reads it.
Per-folder AI instructions also matter. Tell Claude to keep one folder formal and another casual. Tell Cursor to skip the marketing folder when answering coding questions. Ragie does not have an equivalent because its unit is a document, not a folder.