CTO resource planning powered by contextual intelligence
AI that learns from your engineering data to optimize team capacity and project allocation
Backed by the best
Resource planning chaos burns engineering velocity
Your Sprint planning meetings run two hours because capacity data lives in GitHub, Google Calendar, Jira, and tribal knowledge. You're manually correlating commit histories with upcoming PTO to estimate delivery dates. Mid-sprint scope creep forces reactive resource shuffling because you lack real-time visibility into team bandwidth and skillset allocation across concurrent projects.
- Sprint planning requires manual GitHub analysis and calendar checking
- Capacity estimates ignore actual code velocity and upcoming vacations
- Project delays surface late when dependencies hit overallocated engineers
- Skills gaps discovered during implementation, not during planning phases
Intelligence that compounds for smarter resource allocation
- Contextual capacity analysis drawing from GitHub, calendars, and project data in one response
- AI learns your team patterns to predict realistic delivery timelines and bottlenecks
- Automated sprint reports correlating velocity trends with resource allocation decisions
- Skills mapping that identifies coverage gaps before they impact critical deliverables
How it works
- 01
Connect your stack
Link GitHub, Google Calendar, Jira, and Slack. AI builds contextual understanding of team patterns and capacity.
- 02
Query resource scenarios
Ask capacity questions. AI analyzes commit data, PTO schedules, and sprint velocity to provide realistic estimates.
- 03
Execute allocation plans
Generate reports, update project timelines, and create automated workflows. Solutions become reusable across future planning cycles.
What Brainvolt brings to your workflow
Contextual Resource Intelligence
AI draws from GitHub commits, calendar availability, and project timelines in one response. Correlates actual team velocity with upcoming capacity constraints to provide realistic resource allocation recommendations with source citations.
Compounding Team Knowledge
Every resource planning decision becomes institutional knowledge. AI learns from successful allocation patterns, sprint retrospectives, and capacity experiments to improve future recommendations across your entire engineering organization.
Automated Planning Workflows
Schedule recurring capacity reports, sprint preparation tasks, and resource utilization analysis. Build custom dashboards correlating code velocity with team satisfaction scores and delivery predictability metrics.
Engineering Stack Integration
Pre-built connections to GitHub, Jira, Slack, and Google Calendar. Create on-demand integrations with performance monitoring tools, time tracking systems, and project management platforms through conversational setup.
Frequently asked questions
Connect your world.
Get things done.
Try the AI workspace that keeps context and helps you turn conversation into action.