We designed and coded AtlasQ for teams that need faster access to internal information. The assistant connects structured documentation, project notes, policies, and frequently used references so staff can ask questions and get concise, source-aware answers.
The product focuses on accuracy and day-to-day usefulness. It can surface relevant documents, summarise long materials, suggest next steps, and help teams reduce repeated questions across Slack, email, and shared drives.
Tech stack
AtlasQ was built around a retrieval-augmented generation workflow, combining a web chat interface with a backend ingestion pipeline for internal documents. The system can process PDFs, Markdown, docs, policy files, project notes, and structured records, then prepare them for semantic search using embeddings and metadata filters.
The assistant layer uses source-aware prompting, confidence checks, citation display, and answer review tools so teams can see where an answer came from before relying on it. The architecture is designed to integrate with tools such as Slack, Google Drive, Notion, or SharePoint, with permission-aware access and logging for sensitive internal information.
By turning existing knowledge into an accessible AI interface, the tool helps teams work faster while keeping important information easier to find, understand, and reuse.