GENAPA reads your files, extracts what matters, and builds a searchable knowledge graph you can trace back to the source.
Point it at a folder of documents, code, or notes. GENAPA processes each file, identifies the people, systems, and concepts mentioned in them, connects related material into a structured hierarchy, and gives you semantic search and AI chat that always links back to the specific files where the answers came from.
Three things happen to every file.
You give GENAPA files -- source code, documentation, markdown, PDFs, configuration. It reads each file using AI and produces a summary, a detailed description, and a set of factual claims. It identifies every named person, system, and concept in the file and creates a reference for each one.
When the same person or system is mentioned across multiple files under different names, GENAPA figures out they are the same thing and merges them into one canonical record. Then an AI agent called the Weaver reads across your sources and groups them into a structured outline -- files that belong to the same feature are grouped together, features that belong to the same subsystem are grouped together, and so on.
Search the archive using natural language. Results are ranked by semantic similarity across file names, descriptions, claims, and content. Chat with an AI that uses the archive as its knowledge base. Every answer links back to the specific source files that support it. See the knowledge graph in a real-time interactive visual map.
Files go in, structured knowledge comes out.
Most AI tools treat every conversation as a blank slate. GENAPA does not.
GENAPA builds a persistent, structured archive from your actual content. The archive grows with your work, and every piece of extracted knowledge points back to the file it came from. You are not trusting a black box -- you can verify any answer.