What problem does it solve?
It reduces uncertainty in planning by turning a vague task into a structured effort estimate grounded in complexity, dependencies, historical velocity, and risk.
Core Features & Use Cases
- Task scoping: Clarifies what’s being asked and flags when requirements are too vague to estimate.
- Impact scanning: Identifies affected modules/files, checks integration points, and finds existing test coverage relevant to the change.
- Evidence-informed estimation: Incorporates similar past work from sprint evidence and evaluates risk factors to select an optimistic/expected/pessimistic range.
- Actionable output: Produces a table-driven estimate with confidence level, risk register, dependencies, and a suggested sub-task breakdown.
Quick Start
Ask the AI to estimate a specific engineering task by providing a clear task description as the input argument.