Consulting that helps teams apply workshop principles in real systems
Training sticks when teams use it on real systems and real decisions. I offer short, focused consulting engagements that build on the same workshop ideas—ADRs, C4, CI/CD and deployment practices, modular design, and AI-assisted engineering practices—so your team can apply them where it counts.
Architecture and practice audits
- Review of your current architecture, deployment setup, and engineering practices, with a focus on what actually blocks delivery or makes change risky.
- Identification of delivery bottlenecks and high-leverage improvement areas so you know where to act first.
- Written summary and concrete next steps that your teams and leadership can use without requiring ongoing external support.
Decision support
- Facilitated sessions to capture key technical decisions as ADRs so the rationale outlives the current team.
- Support in comparing architecture options against explicit trade-offs so choices are defensible and repeatable.
- Help teams articulate risks and constraints so non-technical stakeholders can participate in trade-offs.
Mentoring for leads
- 1:1 or small-group mentoring for tech leads and senior engineers, aligned with the same concepts as the workshops.
- Practical discussions on introducing and sustaining workshop practices—ADRs, boundaries, CI/CD—in your codebase and team.
- Feedback on architecture proposals, RFCs, and rollout plans.
New focus area
AI-assisted engineering transformation
AI tooling genuinely helps teams that already have solid engineering practices — and quietly accelerates technical debt for teams that don't. This focus area is about adopting it deliberately.
- AI adoption in the developer workflow: introducing it so it goes beyond "we have Copilot" and actually changes how the team works.
- Agentic and LLM-based workflows: when they genuinely help and when they are just expensive autocomplete.
- Team-level AI usage: building shared practices instead of everyone improvising as a random prompt engineer.
- AI and quality: supporting review, testing, and refactoring — not just generating code.
- Diagnosing "AI isn't delivering" situations: where it actually breaks — data, process, ownership, or inflated expectations.
- AI adoption anti-patterns: the proof-of-concept marathon that never becomes production reality.
How consulting complements training
Engagements are short and focused. The aim is knowledge transfer and team autonomy: your people learn to run ADR sessions, maintain diagrams, and improve pipelines themselves. The goal is not ongoing dependency—it's enabling your team to own the practices and decisions after we're done.
