VectorKan tracks actual delivered story points across consecutive sprints. By weighting recent performance, it calculates a realistic capacity forecast for upcoming cycles.
If committed sprint points exceed projected velocity by more than 15%, the system warns the team during planning. This allows engineering leads to descope lower-priority items before the sprint starts.
VectorKan analyzes ticket descriptions to suggest accurate issue types, priorities, and story point sizes during creation.
Learn how to use VectorKan's 5 proprietary AI engines: AI Task Decomposer, AI Release Notes Generator, AI Revenue Leakage Auditor, AI Client Reply Assistant, and Acceptance Criteria Enricher.
Prevent duplicate bugs and feature requests before filing. VectorKan scans existing project backlog items and alerts you to potential duplicates in real time.
Learn why VectorKan's intelligence features run locally on your server without transmitting customer ticket text to third-party AI APIs.