Frequently AskedQuestions

The 'Prompt-to-Project' Wizard. A user describes a project in natural language: 'Organize a summer party for 200 people'. The AI instantly creates a complete project structure with phases, tasks, risk assessments, and budget plan—all structured and ready to use.

AI-driven Requirements Engineering. The AI analyzes new technical specifications against existing project goals and dependencies. It identifies contradictions and proposes precise, testable alternatives to prevent scope creep before the execution phase begins.

AI-powered Action Item Generation based on intelligent project context. The user inputs text or meeting minutes, and the AI automatically creates fully structured tasks with complexity, priority, and deadlines—context-aware rather than simply generated.

Participant Management module based on intelligent project context. The user creates a 'Sarah Müller' profile. The AI automatically generates communication strategies that consider her specific influence on linked requirements and risks—context-based rather than isolated.

Intelligent Budget Management view based on intelligent project context. The video shows the €15,000 'Summer Party' budget with automatic categorization into buckets like 'Catering' and 'Venue'. The AI provides precise burn rate forecasts and early warnings about overspending.

Interactive Gantt Chart with intelligent dependency management. The user drags a task bar via Drag & Drop. Based on the complete project context, the AI instantly recalculates all dependency chains and the critical path to ensure 100% schedule integrity.

Agile Kanban workflow with context-based AI support. Tickets are moved between columns ('In Progress', 'Blocked', 'Completed'). Based on intelligent project context, the AI automatically suggests next steps for dependent tasks and keeps the workflow logic consistent.

Integration of the Model Context Protocol (MCP). The video demonstrates connecting external tools via standard protocols. These external data sources are integrated into the intelligent project context, allowing the AI to work seamlessly across both internal project data and live external sources.

Demonstration of the intelligent project context. The user asks a specific question about 'Summer Party 2025'. The AI—using either cloud models or local LLMs—analyzes the complete project context including files and stakeholder data, providing precise, hallucination-free answers with specific budget limits and responsibilities. Depending on the model choice, processing happens locally or transiently in the cloud, without permanent storage.

Showcase of Proactive Execution Guidance based on intelligent project context. The system identifies a critical blocker in the 'Venue Finalization' phase and automatically suggests prioritized next steps—with exact calculation of the impact on the critical path, based on the complete project context.

AI Distillation workflow converting unstructured data into structured project artifacts. The user selects raw meeting notes, and the AI extracts—based on intelligent project context—distinct requirements and action items, linked to specific phases and owners.

Demonstration of the File Explorer with Privacy-First approach. A user drags a PDF requirement document via Drag & Drop. The AI analyzes the content and integrates it into the intelligent project context. Depending on the choice between local LLM or cloud model, processing happens either completely offline or transiently in the cloud—without permanent storage (see Privacy Policy for details).

Project Health Dashboard based on intelligent project context. The screen displays a 'Health Score' calculated from precise metrics on Budget, Time, and Scope. The AI analyzes all project data and delivers a data-driven diagnostic with precise key performance indicators—context-based without manual reports.

Integrated Time Tracking demonstration based on intelligent project context. A user logs time on an action item. The AI immediately integrates this data into the project context and compares 'Estimated' vs. 'Actual' duration to continuously refine future effort estimates—context-based and learning-enabled.