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Targets & Scaling Roadmap

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Key Performance Indicators

KPI Alpha Target At-Scale Target Measurement
Spindle Utilization 20-40% >72% Effective machining hours / available hours
Machine Availability 45-65% >80% Machine uptime / scheduled time
Order-to-Ship Time < 7 days < 3 days Order timestamp to tracking number
First Pass Yield > 90% > 97% Parts passing all inspection first attempt
AI-CAM Automation Rate > 60% > 90% Jobs programmed without human CAM intervention

Scaling Roadmap

The standardized cell architecture is designed for multi-facility replication. The plan projects four facilities across three geographies over five years, scaling from 3 machines to 73 deployed machines.

Phase 1: Alpha Facility (Jul 2026 - Aug 2027)

  • Deploy 1-3 DVF 5000s in 8,000 sqft Alpha Facility
  • 5 days/week, 8 hours/day, single shift
  • 60-80% AI-CAM automation, manual billet loading
  • Prove unit economics and workflow with pilot customers
  • Close the learning loop: every part machined trains the AI-CAM

Phase 2: Facility 1 - Massachusetts (Sep 2027 - mid 2029)

  • 50,000 sqft production facility
  • 6 days/week, 22 hours/day (lights-out target)
  • Start with 2 machines, scale to 20+ over 18 months
  • 80%+ AI-CAM automation, robotic loading exploration

Phase 3: Facility 2 - Austin (mid 2029 - 2030)

  • 50,000 sqft, 30 machines max
  • Replicate proven Facility 1 operating model
  • Scale to 25 machines by end of Year 4

Phase 4: Facility 3 - Los Angeles (2030 - 2031)

  • 50,000 sqft, 30 machines max
  • West coast coverage for aerospace/defense customers
  • Scale to 25 machines by end of Year 5
Facility Start Max Machines Size Schedule
Alpha Jul 2026 5 8,000 sqft 5 days, 8 hrs
Facility 1 (MA) Sep 2027 30 50,000 sqft 6 days, 22 hrs
Facility 2 (Austin) ~Mid 2029 30 50,000 sqft 6 days, 22 hrs
Facility 3 (LA) ~Late 2030 30 50,000 sqft 6 days, 22 hrs

The goal at each phase is to prove that the standardized cell, combined with the Anvil software stack, delivers predictable throughput at target quality, and that adding capacity is a deployment decision rather than an engineering challenge.