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.
Featured service
Board AI portfolio review
A half-day session with your leadership team places every AI initiative on the Vector & Value Matrix, with the people who own them. You leave with a one-page board pack: where the portfolio leans against external pressure, which initiatives lack an owner or a tracked measure, where volume is outrunning review, and which business cases overstate ROI by leaving out their zone’s risks.
Every engagement begins with a mutual non-disclosure agreement.
Our approach: the AI Value Stack
Hardware, models, and applications chosen together for each business decision, guided by the Vector & Value Matrix and grounded in the economics of complementary, hard-to-reverse choices.
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, including agent readiness before AI acts on its own.
Agent readiness, before AI acts on your behalf.
When AI moves from advising people to acting for them, the question changes from whether its answers are good to what it would do on its worst day. We test the agent and the controls around it, and give leadership a clear recommendation: go, go with conditions, or not yet.
Readiness audit
Two to three weeks, for one agent approaching launch. We review what the agent can reach and do without approval, run the AgentProbe scenario library against it, and review the controls around it. You receive a risk report, a prioritized list of fixes, and a briefing for leadership.
Adversarial testing sprint
Three to six weeks, for agents already live or in high-risk workflows. Everything in the audit, plus attack scenarios built from your own workflows and documents, and a test of whether your controls would contain a compromised model. You keep the test suite, and we retest after your fixes.
Agent operations retainer
Monthly, for teams releasing agents regularly. We retest every release, add new attack scenarios as they appear, review incidents, and maintain the check that stops a release on any critical finding. You receive a monthly report and a sign-off for each release.
Testing runs in simulated environments built from your workflows. We never test against live production systems without written agreement. Passing our tests shows resistance to known attack patterns, not proof that an agent is safe.
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
Confidentiality. Every engagement begins with a mutual non-disclosure agreement, signed before any client information is shared. Client details are never published without written consent. Our work is decision support and does not constitute legal, regulatory, financial, or investment advice.
Have an AI problem worth solving?
Let’s identify where AI can create real economic and organizational value.