AI Workflow Operating System

Problem

When AI-assisted work spans long projects, context often becomes fragmented: goals live in chat history, sources sit in web pages, PDF files, spreadsheets, and images, progress depends on memory, and incoming materials lack a consistent trust or audit model.

ai-workflow-os turns this into a routed workflow. It first classifies a request into project lifecycle, project memory, knowledge intake governance, or cross-source synthesis, then applies a stable order when multiple modules are involved.

How It Works

• Project lifecycle clarifies goals, MVP scope, risks, milestones, and next actions when a project starts or drifts.
• Project memory saves checkpoints, completed work, pending work, risks, and handoff notes across sessions.
• Knowledge intake governance normalizes web pages, PDF files, spreadsheets, images, datasets, and manual notes into records with trust levels such as trusted, allowed, review, and blocked.
• Cross-source synthesis compares, deduplicates, reconciles conflicts, and summarizes findings before archiving.

Use it when project knowledge needs to persist across chats, sources are mixed, or work must be handed off between people and agents. It does not automatically upload private files to cloud storage and should not automatically archive sensitive documents; ambiguous sources should be staged for confirmation first.