Operators
COOs, founders, and team leads with support, research, document, onboarding, or internal tool bottlenecks.
Services
AI deployment services for teams that need production outcomes.
I work with operators, founders, product leaders, and technical teams who need practical AI systems connected to real workflows, real data, and clear operating constraints.
Who I work with
Best fit: teams with real workflow pressure and enough access to test the system honestly.
Product and technical teams
Teams that can prototype but need help turning AI into reliable workflow software with evals and deployment discipline.
AI-ready SMBs
Companies with repeated knowledge work, scattered data, and a practical reason to improve speed, quality, or capacity.
Offer ladder
Start with the smallest useful step, then build toward production.
1 week
serviceAI Opportunity Audit
Map the workflow, assess data readiness, estimate ROI, and decide whether AI is worth building into the process.
- Workflow inventory
- Data readiness review
- Opportunity and risk ranking
- First-build roadmap
2 weeks
serviceAI Prototype Sprint
Turn one high-value workflow into a working prototype connected to real inputs and evaluated against useful examples.
- Working prototype
- RAG or agent workflow
- Evaluation set
- Production-readiness checklist
4 to 8 weeks
serviceProduction AI Workflow Build
Ship a reliable AI workflow with integrations, monitoring, review loops, deployment, and handoff documentation.
- Production app or internal system
- Data and tool integrations
- Evals and guardrails
- Observability and handoff
1 to 3 days/week
serviceFractional AI Engineer
Embedded senior AI engineering capacity across strategy, architecture, implementation, and production troubleshooting.
- Architecture
- Implementation
- Stakeholder workshops
- Vendor and model selection
- Production troubleshooting
Typical workflows
Where AI can create value without becoming theater.
Customer support automationInternal knowledge assistantsAnalyst and research copilotsSales and account intelligenceCompliance and document reviewContent and AEO operationsOperations dashboardsDecision support tools
Deployment standard
The prototype is not the finish line.
A durable AI workflow needs more than a model call. The system has to survive messy data, real users, changing requirements, cost limits, and operating constraints.
Workflow owner
Data access
Evaluation set
Human review
Permissions
Observability
Cost tracking
Handoff documentation
FAQ
Direct answers for buyers.
What does good AI deployment include?
Good AI deployment includes workflow fit, data readiness, evaluation, human review, permissions, security, observability, cost controls, and user adoption.
How do engagements usually start?
Most engagements begin with a focused audit or prototype sprint before committing to a production build.
What teams are the best fit?
The best fit is a team with a real workflow owner, access to sample data or documents, and a desire to ship a working system.
Do you replace internal engineering teams?
No. I can build independently, but the strongest engagements pair AI engineering with the team's operator context, product knowledge, and existing technical ownership.