Good questions. Straight answers.

Carlo connects task planning with schedule and cost uncertainty, quantified risks, resource capacity and progress tracking. You can explore the probability of meeting a target, compare scenarios, and carry reviewed meeting actions into the project plan.

You can start with optimistic, most likely and pessimistic estimates for your tasks. Carlo samples possible outcomes and presents confidence levels such as P80: a value that 80% of simulated outcomes fall at or below. The results depend on your estimates and modelling assumptions; they are not guarantees.

AI project drafts and meeting-to-plan proposals are reviewed before you apply them. Meeting recording, note enhancement and plan handoff are separate steps. You choose which proposed changes to accept, and Carlo preserves source context for applied meeting changes.

Yes. Paste delimited rows or import CSV task estimates, check the preview and choose whether to append or replace tasks. You can export records as CSV and back up or move a whole project with a validated JSON export.

Yes. Plan and track projects directly, or import existing notes or a transcript when you want AI-assisted meeting notes. Recording is optional, and using the planning and simulation tools does not depend on it.

Export task estimates, the risk register, resources, scenario comparisons and simulation percentiles as CSV. Settings offers a whole-project ZIP with CSVs, re-importable JSON and chart images from the latest run; chart images require a simulation in the current session.

Choose the dependency-network schedule model to calculate project duration from the critical path through predecessor relationships. Criticality shows how often each task sits on the critical path across simulations. The sequential model instead sums task durations.

No. Confidence levels describe simulated outcomes under your inputs and model assumptions. Review estimate quality, dependencies and quantified risks, and compare scenarios as the project changes.