How I help
AI strategy and scoping
- Use-case framing, prioritization, and technical reality checks
- Defining what should and should not be built before resources are committed
- Translating product ideas into concrete system, delivery, and evaluation plans
Architecture and agentic systems
- System design for multi-step AI workflows, reasoning, retrieval, and structured knowledge
- Agent orchestration with evaluation, constraints, and failure-aware design
- Context and memory design beyond naive prompt chaining
Delivery, MLOps, and LLMOps
- Model and pipeline design with deployment, observability, and maintenance in mind
- Reproducible workflows, CI/CD, monitoring, and evaluation pipelines
- Moving teams from experimentation to systems that behave predictably in production
Technical due diligence
- Review of data, models, architecture, and delivery maturity
- External technical judgment before major investment or vendor commitment
- Identifying gaps between the AI narrative and the actual system
Workshops and speaking
- Technical workshops for engineering and product teams
- Executive and university sessions on practical AI adoption
- Talks focused on systems, trade-offs, and failure modes rather than trends
Best fit
- Product and engineering teams with a real system to build or fix
- Founders and leaders making AI investment or architecture decisions
- Universities and organizations that want technical depth in workshops
Usually not the right fit
- Teams looking for a demo without a plan for evaluation or deployment
- Agent ideas without meaningful data, structure, or ownership
- Generic AI talks that stay at the buzzword level