AI Models & Platforms
6 toolsFrontier and open-weight models used across orchestration, evals, and delivery.
- Glean
- Anthropic
- OpenAI
- Google AI
- OpenRouter
- vLLM
Skills & Capabilities
I turn messy operational systems into reliable AI workflows with clear owners, controls, and business outcomes. Forward-deployed discovery. Applied AI systems. Revenue-aware design. Production delivery.
Strategic Capabilities
Solutioning
From ambiguous asks to scoped, production-ready architecture.
I sit between the business ask and the technical build. Discovery turns broad AI interest into delivery constraints, success criteria, and measurable outcomes — risks surfaced before commit, not after.
Translate vague requests into implementation plans with constraints, dependencies, and success metrics. Validate feasibility and surface data and governance gaps during scoping.
Stress-test before production through wireframes, pilots, A/B testing, load testing, and customer advisory board feedback.
Telemetry-driven systems with product usage tracking, CSP integration, and deployment patterns that survive handoff to CSM ownership.
SOWs, API and SDK documentation, solution blueprints, gap analyses, and executive narratives that align technical output with stakeholder expectations.
Tools of the Trade
The tools matter because they connect data, operators, and delivery paths into one working system.
Frontier and open-weight models used across orchestration, evals, and delivery.
The orchestration layer — where prompts, tools, and workflows actually meet.
Tools that let me ship production code faster without losing review discipline.
What the production systems and demos are actually built on.
The systems of record that feed every scoring engine, briefing, and agent.
The Experience page covers shipped systems. The demo runner shows agents and cards in action against simulated data.