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 actually matters. That includes business and product objectives, constraints (time, budget, team, risk), existing data and systems, and what's already been tried. The goal isn't completeness. It's shared understanding of the problem we're solving.
2. Analyze intent, best practices, and opportunities
Next, we apply experience. We analyze the intended purpose, relevant best practices, benchmarks, 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 concrete proposal: a recommended approach, assumptions and tradeoffs, what success looks like, and what we are not doing right now. If the direction isn't approved, we loop back and refine. If it's approved, we move forward with execution planning. This decision gate prevents momentum in the wrong direction.
4. Plan execution
Once direction is clear, we plan how to deliver it: scope, sequencing, sprint/milestone plan, ownership, and risks/dependencies. Planning is pragmatic - enough to move confidently, revisited as reality changes.
5. Implement
Execution happens here. Depending on the engagement, we build product features, implement AI/automation, or run delivery management. Throughout implementation, priorities stay visible and decisions explicit. Progress is measured by working outcomes, not activity.
6. Document and learn (per sprint)
At the end of each sprint or cycle, we document what was delivered, what was learned, what changed, and what to adjust next. Documentation isn't bureaucracy - it's institutional memory that makes each iteration smarter.
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