Where AI strategy meets execution.
Strategy that never reaches execution is opinion. We work across both, from identifying where AI creates value to the operating model, architecture, and governance required to make it real.
Four areas of practice.
Most engagements touch more than one. We scope around the decision you need to make, not around a service line.
AI opportunity & strategy
Identify where AI can create economic value, not merely where it can be deployed. We start from your business model and P&L, not from a list of AI use cases.
AI product & system design
Design AI-powered products, workflows, architectures, and human–AI systems that hold up in production, not just in a demo.
AI workforce & economics
Understand how AI changes jobs, skills, productivity, organizational design, and economics, grounded in evidence rather than forecasts.
AI governance & decision systems
Build frameworks for evaluation, accountability, risk, human oversight, and responsible deployment that scale with adoption.
Every engagement follows the same four steps.
Start with the economic problem, find the decisions that matter, design the system, and measure whether it worked.
- Value
Where could AI create real economic or organizational value.
- Decision
Which decisions, made by which people, determine that value.
- System
The AI–human system and architecture built to support it well.
- Measurement
Whether the system, in production, created the value it was built for.
Engagements, in outline.
Representative structure of our engagements. Client details are withheld; the shape of the work is real.
- Client and industry
- Financial services, mid-market lender
- Business problem
- Underwriting decisions were slow and inconsistent across teams
- AI intervention
- Decision-support system with traceable reasoning for underwriters
- Measurable outcome
- Outcome measurement in progress
- Client and industry
- Enterprise software, operations team
- Business problem
- Unclear where AI investment would move the P&L
- AI intervention
- Opportunity assessment and prioritized roadmap
- Measurable outcome
- Outcome measurement in progress
- Client and industry
- Health AI, US startup
- Business problem
- Published benchmark claims needed independent scrutiny before peer review
- AI intervention
- Code-level methodology audit and a statistical audit design
- Measurable outcome
- Findings report rating each published claim by confidence
Have an AI problem worth solving?
Let’s identify where AI can create real economic and organizational value.