Services

A high-level list of the services I offer.

Best-fit customers

I work best with small companies that need experienced help moving important technical projects forward, with a focus on AI infrastructure, scale, and reliability.

How engagements work

Most engagements start with a specific problem: something broken, slow, expensive, confusing, or moving more slowly than desired.

My level of involvement depends on the nature of the problem and your timeline. That might mean a 30-minute call, a focused debugging session, an architecture review, a short implementation push, or ongoing implementation support.

For larger work, I prefer monthly milestones and weekly updates so progress stays visible. I can also fit into your team’s existing planning and delivery process.

Technical Advising + Due Diligence

Outcome:

• Clear, informed, and defensible technical decisions about architecture, vendors, investments, and platform direction

Typical shape:

• Focused 2–4 week review depending on scope

What I do:

• Review architecture, codebase, infrastructure, operational maturity, technical debt, team shape, and scaling risk
• Deliver findings in decision-ready language

AI Coding Tools and Orchestration

Outcome:

• AI coding workflows that are repeatable, easier to share across teams, and less dependent on one person babysitting local tools

Typical shape:

• 1–2 weeks to audit current workflows and identify repeatable pain points
• 2–4 weeks to create a functional shared repository of prompts, skills, and workflow instructions
• 1–2 months to build and harden a reliable background-agent workflow for recurring work

What I do:

• Turn large nested prompts and skills into deterministic workflows
• Collect scattered team-specific skills into a shared skill repository
• Move useful agent workflows off individual laptops and into scheduled/background execution
• Design agent workflows that use token subscriptions efficiently and keep useful work moving outside business hours
• Add review, routing, and handoff points so AI-generated work stays understandable

Architecture Review + Roadmap

Outcome:

• Written assessment of risks, bottlenecks, tradeoffs, and near-term opportunities
• Sequenced plan your team can execute

Typical shape:

• Roughly 1 day per component under review
• Generally 1–2 calendar weeks depending on cadence

What I do:

• Review architecture, infrastructure, code paths, operational practices, and team constraints
• Interview key stakeholders
• Identify risks, bottlenecks, and tradeoffs
• Deliver a practical recommendation memo and 30/60/90-day roadmap

Hands-on Implementation Support

Outcome:

• Critical infrastructure work moves forward with senior technical leadership embedded alongside your team

Typical shape:

• Fractional engagement, typically scoped in monthly increments

What I do:

• Lead design
• Unblock implementation
• Review code and architecture
• Debug production issues
• Mentor engineers
• Create operating practices that remain after the engagement

AI/ML Production Systems

Outcome:

• AI/ML systems that can move from prototype to production with reliability, observability, and repeatable operations

Typical shape:

• 2–4 weeks for assessment/design
• Implementation support available afterward

What I do:

• Design data pipelines, orchestration, model serving, deployment paths, monitoring, and operational guardrails for ML workflows