Applied artificial intelligence for companies, with judgment and operational depth
I help leadership teams turn AI into decisions, automations and operational products: from strategy and use cases to agent systems capable of working inside real workflows.
Useful AI does not start with the model
The opportunity is not to add yet another AI tool, but to redesign how work flows: what gets decided, what gets automated, what gets supervised and what evidence remains behind. My work is to help separate real opportunities from noise, prioritize what deserves implementation and build systems that can survive outside a demo.
Three ways to work on AI
01
Advisory and implementation services
Senior support to turn intent into roadmap, architecture, governance and execution.
- Applied AI strategy
- AI Opportunity Sprint
- Intelligent automation
- AgentOps and agent reliability
- Fractional CTO/CIO support when AI touches architecture, team or product
02
Executive products
Focused formats to make decisions, prioritize opportunities and start without turning AI into an undefined project.
- Executive AI Briefing
- AI Opportunity Sprint
- Executive AI Inbox
- AgentOps / Agent Reliability Assessment
03
Agent system
Practical experience building agents that work with tasks, memory, tools, limits, approvals and human channels.
- Role-specialized agents
- Persistent memory and context
- Delegation and follow-up
- Traceability, security and operational control
Products to start with focus
A
Executive AI Briefing
An executive session for leadership: what is changing, which opportunities apply to the company and which decisions should be made now.
Deliverables
- Opportunity map
- Risk and priority reading
- Decision criteria
- Next steps
B
AI Opportunity Sprint
A 2 to 4 week sprint to discover, prioritize and turn AI opportunities into an executable roadmap.
Deliverables
- Inventory of processes and decisions
- Impact/effort/risk matrix
- Use case shortlist
- Initial roadmap
C
Executive AI Inbox
A system to prioritize email, documents, follow-up and operational memory in information-intensive executive contexts.
Deliverables
- Triage and prioritization flows
- Memory of topics and decisions
- Follow-up of pending work
- Human review controls
D
AgentOps Assessment
Review of existing agents, LLM automations or intelligent flows that need to move from demo to reliable operation.
Deliverables
- Reliability diagnosis
- Risks and blind spots
- Metrics, alerts and traceability
- 30/60-day improvement plan
Applied AI services
The service side remains, but it is subordinate to the hub: AI creates value when it fits strategy, operations, architecture, security and adoption.
Typical areas
- AI strategy and roadmap.
- Automation of information-intensive processes.
- Operational copilots and assistants.
- Governance, security, metrics and adoption.
- Mentoring and support for leadership and technical teams.
Agent systems that work in real processes
The difference between a demo and an operating system is what happens after the prompt: memory, context, tools, permissions, human escalation, audit, cost, errors and continuity.
- Specialized agents with clear roles.
- Persistent memory and context.
- Tasks, follow-up and delegation.
- Integration with human channels.
- Approvals and operational limits.
- Error review, traceability and governance.
Applied experience
The Augmented Enterprise
A per-employee AI model for organizational transformation and augmented work.
Learn moreWarGame
A C2 platform with optional AI, traceability, operational roles and White Cell control.
Learn moreFractional CTO
Senior technology leadership when AI touches architecture, team, product or investment.
Learn moreCollaborations
Ways to work on modernization, operations, applied AI and technology leadership.
Learn moreIf you want to apply AI, start with the work system
The question is not which model to use. It is what work deserves to change, what risk you accept, what evidence you need and how you will operate it once it stops being a test.
Let’s talk