Deep research over your own library beats generic web search
Web search is optimized for the average question. But the questions that matter to you are never average — they are downstream of everything you have already watched, read and saved.
The generic answer problem
Ask a search engine "how do I learn retrieval-augmented generation?" and you get the same listicle everyone gets: tuned for beginners, ranked by SEO, blind to the fact that you already saved three conference talks and a repo about exactly this.
What grounded research looks like
In ORANO, research starts from your library. Every saved Reel, video, article, repo and PDF is already understood — summary, key points, category, difficulty, estimates. When you run research on a project, ORANO combines that personal context with fresh, cited web results (via Tavily, Exa and Brave adapters), so the output cites your sources and the live web side by side.
The difference is easiest to feel in follow-ups. "Compare this to the talk I saved in March" is impossible for generic search. For a system that remembers what you saved, it is one query.
And the agent gets the same context
Through the personal, read-only MCP server, your own AI agent — ChatGPT, Claude, Cursor — reads the same library and produces answers with the same grounding. Machine-readable manifest: oranoai.com/mcp.json.