Consulting

Architecture guidance for AI systems that must work beyond the prototype.

I help technical leaders examine architectural risk, sustainability, adaptability, and operational readiness in ML-enabled products and services.

Where I can help

Focused engagements with useful outputs.

Engagements can range from a focused review to a workshop series, depending on the maturity of the system and team.

Examine system boundaries, model dependencies, quality attributes, failure modes, and deployment trade-offs.

Output: architecture findings and prioritized decisions.

Identify where architecture, infrastructure, data, and model choices create avoidable resource consumption.

Output: measurement plan and reduction roadmap.

Build shared understanding around AI engineering, architectural decision-making, and responsible system evolution.

Output: tailored workshop and reusable decision tools.

Approach

01
Frame
Clarify the decision, constraints, stakeholders, and evidence already available.
02
Examine
Review architecture and assumptions through technical and sustainability lenses.
03
Decide
Translate findings into explicit options, trade-offs, and prioritized actions.

A useful first step