AImation / Engineering project management
See which deadlines are at risk, where team capacity is missing and which decisions are pending. The PM Demonstrator connects work packages, milestones and workload in one project view. AI assists with risk suggestions and report drafts. You review and decide.
Watch the application in action ↘For your working day
A green status indicator means little if it misses the dependency on a delayed test. Project management needs the connection between work packages, available people and milestones. The report is the result of that work.
Illustrative workflow / not a customer case
The question in the review is what the delay changes. This example shows how a project update connects to dependencies, capacity and an approved report.
Project update
The team records a new forecast date and the reason for the delay.
Deterministic checks
Check dependent work packages, the milestone and available team capacity against the plan.
AI assistance
AI drafts a report from project data and suggests risks for the project lead to check.
Human approval
The project lead checks the draft, agrees actions and approves the report. Changes remain traceable.
The video shows the proof of concept with fictional data. For your pilot, we agree project phases, status rules, report structure and interfaces. A changed date alone does not trigger an automatic replanning decision.
Original recording / German
Follow the path from a work package through a bottleneck to the report draft.
From input to decision
Bring work packages, milestones and dependencies into one agreed data version. Keep missing information visible.
Review utilisation and competing team tasks. Bottlenecks need to be traceable to the underlying plan.
The video shows an AI risk radar. Its suggestions support expert review, not automatic decisions about schedules or people.
Check the demonstrated AI report draft against project data. Actions, ownership and approval remain with the project lead.
AI, rules and data history
Built with AI, the PM Demonstrator combines structured project data with an AI risk radar and AI report drafts. Calculations and status rules remain deterministic. The documented demonstrator uses PostgreSQL and includes change histories for project activities and milestone baselines. For your implementation, we agree database connections, interfaces, access permissions and retention. The project lead reviews AI suggestions and approves reports.
Evaluation plan / results not yet measured
One project review with a frozen data version. Work packages, dependencies, capacity and the report template are identical for both runs.
The project lead checks status, sources and actions. Data maintenance, review and report corrections all count towards total time, even when drafting is fast.
For a first test
Before we start
No. It is a proof of concept. Production use requires agreement on scope, authentication, integrations, data processing and operations, among other things.
No. The video shows risk suggestions and a draft report. The project lead checks statements and decides on actions, priorities and approval.
That is not assumed. In the initial call, we check whether a scoped data export or reporting workflow is sufficient for a first test.
Your next step
Choose one engineering project and a recurring status report as the starting point. We discuss where data is gathered by hand, which bottlenecks currently surface late and how a pilot needs to fit your workflow.
