Use AI productively without giving up engineering control.
AI can accelerate software development and expand what a product can do. It can also introduce subtle reliability, quality, maintainability, and oversight problems that ordinary development practices were not designed to address.
The AI Engineering Review helps teams understand where AI is useful, where it is risky, and what practical controls are needed.
Possible review areas
Product architecture
- LLM integration strategy
- Prompt and workflow design
- Reliability and failure modes
- Human approval points
Engineering workflow
- Claude Code adoption
- Agentic development
- Code ownership
- Review and testing practices
Quality and control
- Evaluation strategy
- Engineering standards
- Change management
- Where AI should and should not be used
AI-generated codebase assessment
I can help teams assess applications built heavily with AI coding tools, including whether the resulting architecture, testing, documentation, and ownership model are sustainable.
Deliverables
- Intake call
- Workflow or architecture review
- Risk and gap assessment
- Concise written findings with prioritized recommendations
- Findings presentation
The scope and required materials will be agreed upon before the engagement begins.
The review focuses on practical engineering quality and decision-making. It is not a legal compliance certification or a guarantee of model behavior.
Introducing AI into your product or engineering workflow?
Describe where AI fits into your product or process and what you want reviewed. I review every request personally.
Request an AI ReviewEngagements are scheduled by mutual availability and conducted remotely.