How we work: a clear cycle from intent to execution
Every engagement at Vulcan follows the same principle: understand the real objective, apply expert judgment, execute deliberately, and learn fast. Not a framework. A working cycle we've used across products, teams, and stages.
Our working cycle
Instead of locking teams into rigid processes, we operate with a clear execution cycle. It's designed to reduce uncertainty, force clarity, and keep work aligned with purpose. This cycle repeats continuously as products evolve.
1. Gather objectives, intelligence, and data
We start by understanding what matters: objectives, constraints, data, systems, and what's already been tried. The goal is shared understanding, not completeness.
2. Analyze intent, best practices, and opportunities
Next, we apply experience to find best practices and opportunities to simplify, sequence, or de-risk. We're not copying playbooks - we're adapting them to your context.
3. Commit to direction (and validate)
We translate analysis into a proposal: approach, assumptions, tradeoffs, success criteria, and what we are not doing yet. Approval becomes the decision gate before planning.
4. Plan execution
Once direction is clear, we plan scope, sequencing, ownership, and risks. Planning is pragmatic and revisited as reality changes.
5. Implement
Execution happens here. We build features, implement AI/automation, or run delivery management with priorities visible. Progress is measured by working outcomes, not activity.
6. Document and learn (per sprint)
At the end of each sprint, we document what shipped, what changed, and what to adjust next. Documentation is institutional memory, not bureaucracy.
This cycle repeats across every engagement.
A continuous loop, not a straight line
Objectives evolve. Constraints shift. New data emerges. We loop back when needed - not because something failed, but because learning happened. That's how products improve without chaos.
How this adapts to different engagements
- Discovery: more time in steps 1-3
- Development: tight loops between planning, implementation, and learning
- AI: deeper analysis and validation before execution
- PMO / Delivery: continuous ownership across all steps