Operating Model

Agile Startup Studio Model

A practical model for startups and innovation teams that need to move from concept to production-ready MVP quickly, while keeping architecture and release quality under control.

Approach and proposition

  • Agile, sprint-driven execution model
  • AI-assisted delivery with senior engineering oversight
  • Transparent sprint reviews and sign-off checkpoints
  • Built for fast validation without sacrificing quality

Outcome focus: faster time-to-market, more room for iteration, and a delivery rhythm grounded in real user feedback.

Operating principles

  • Shared backlog with clear priorities and scope choices
  • Weekly alignment and bi-weekly sprint demos
  • Stop-or-go decisions at phase and sprint boundaries
  • Cross-functional collaboration with domain experts

Execution phases

1. Design Sprint

Typically 1-2 weeks

Validate user journeys, define the MVP boundary, and align product and technical scope before full development starts.

  • Validated user flows and key assumptions
  • MVP scope and technical boundaries
  • Prioritized backlog for execution

2. Sprint-Based Development

2-week sprints

Build in short, iterative sprints with demo and sign-off moments, combining AI-assisted implementation with senior engineering supervision.

  • Incremental MVP delivery per sprint
  • Continuous backlog refinement and scope decisions
  • Quality controls across architecture, testing, and security

3. Launch and Stabilization

Typically 1-2 weeks

Prepare a controlled release, monitor live behavior, and stabilize based on early user feedback.

  • Production release readiness and monitoring
  • Post-launch bug fixing and performance optimization
  • Operational handover and next-iteration recommendations

Collaboration requirement: keep a domain expert or product stakeholder available consistently for feedback and decision-making during the trajectory.

When this model fits best

Best fit for new product initiatives, innovation programs, and teams that need both speed and delivery control.

The phased structure makes progress predictable while preserving flexibility to adapt backlog priorities as you learn.