RAG + Memory Workflow
Revive
A historical-saved-content reuse tool for knowledge workers, helping past collections become usable again when writing proposals, reviewing work, or preparing reports, while gradually adapting to task preferences.
Live
Problem
Many knowledge workers save articles, cases, methods, and experience-based content for years, but when writing proposals, doing retrospectives, or preparing reports, they still need to search, reread, filter, and summarize everything manually. The problem is not only that content is hard to find; saved materials rarely turn into directly usable work output at the moment a real task appears.
Target Users
Product managers, operators, marketers, consultants, and content workers who keep long-term collections and need to turn external information into proposals, reviews, reports, planning materials, or communication drafts.
Existing Alternatives
Users usually rely on platform bookmarks, read-it-later tools, knowledge bases, or manual research combined with general AI. These tools can store or generate content, but they require high upfront organization effort, provide unclear evidence for results, and rarely adapt to a user's own output habits over time.
Product Solution
Revive is not a general second brain or a heavy knowledge-management system. It focuses on reusing saved content when a task happens. Users import a batch of content, then start from a concrete task. The system generates structured output that can be copied, edited, and traced back to supporting evidence.
AI Workflow
The system is built on a RAG workflow: imported content is parsed, chunked, and indexed. When users submit a task, the system retrieves relevant passages from the current content set and generates structured results. Key conclusions are connected to citation evidence that shows original text snippets, not only source titles.
Memory System
On top of RAG, Revive adds task preference memory to remember how users apply knowledge to complete work. It captures preferred output structures, citation habits, writing style, and negative preferences, then uses them as soft constraints during generation so results gradually fit real work habits.