Four systems.
One infrastructure.

What Problem It Solves

• AI capability exists but generates no attributable revenue
• Pricing models inherited from pre-AI service structures
• No clear path from deployed capability to commercial value
• Internal AI tools with no monetization logic, even where external value exists

What Clients Get

• Commercial model design for AI-driven products and services
• Revenue attribution architecture
• Pricing and packaging frameworks for AI capability
• Go-to-market conversion pathway

Typical Engagement

A focused commercialization architecture engagement typically follows a diagnostic working session. Duration depends on the complexity of the existing capability portfolio and the number of commercial conversion pathways being designed.

What Problem It Solves

• No clear ownership of AI in production
• Accountability gaps between data science, engineering, and business
• Workflows designed for human-only execution, not human + AI
• Operating roles unchanged despite AI deployment at scale

What Clients Get

• Operating model blueprint for AI-native operations
• Ownership and accountability framework
• Workflow redesign specifications
• Role architecture for AI-augmented teams

Typical Engagement

Assessment and design phase is typically 4–8 weeks. Implementation support engagements run longer depending on the scope of operating model change and the number of functions being redesigned.

What Problem It Solves

• Pilots that never reach production — high activity, low conversion
• No release gate between experimentation and client-facing use
• No measurement of what has shipped versus what is still in testing
• PCI below 0.20 — most AI investment not reaching defensible production

What Clients Get

• Production conversion pipeline based on our Aegir release control framework.
• Release gate framework with defined ownership at each transition
• PCI measurement and tracking system
• Production readiness assessment

Typical Engagement

Often the core engagement for organizations with active AI portfolios and low production conversion. Includes diagnostic, pipeline design, gate framework implementation, and measurement system activation.

What Problem It Solves

• AI cannot be deployed in regulated environments without evidence of governance
• Sovereign hosting and data residency requirements unmet
• No defensible audit trail for AI-driven decisions
• Platform dependency creates unacceptable control risk in critical environments

What Clients Get

• Sovereign deployment architecture design
• Regulatory evidence framework aligned to applicable regulations.
• Trust and assurance documentation
• Hosting and control logic for sovereign environments

Typical Engagement

Often combined with Operating Model or Production Conversion work. The regulatory evidence framework and hosting design are delivered in parallel with production architecture. Standalone engagements begin with an exposure and gap assessment.