Nathan Austin
AI Workflow Architect
Routing rules + verification loops + templates integrated into your delivery process.
Not this: one-off prompt tricks or replacing engineers with "magic" boxes.
Yes: industrial-strength workflow design + verification loops + GitHub integration.
The bottleneck is rarely model access. It's missing structure: no shared standards, weak verification, and inconsistent handoffs that lead to regressions.
Every engagement starts with a diagnostic audit. Once we've identified the highest leverage changes, we align the team and harden the workflow.
Diagnose how AI is used today, where quality breaks down, and what should be standardized first.
Align the team on shared standards and teach the core workflow so people can use it immediately.
Implement the workflow in your repo, iterate on real work, and make it stick (templates, standards, automation).
Fixed-scope engagements. Investment scales with team size and format. See full details and pricing →
“Die Beratung war für mich ein echter Gamechanger. Ich habe gelernt, wie man verschiedene AI-Agents gezielt kombiniert, um deutlich effizienter und strukturierter an komplexe Coding-Projekte heranzugehen. Besonders wertvoll war der Ansatz, Probleme in klare Strukturen zu zerlegen und anschließend passende Prompts und Workflows für unterschiedliche Tools zu entwickeln. Dadurch hat sich meine gesamte Arbeitsweise nachhaltig verbessert.”
Implementation-level examples of workflow architecture and enablement patterns that move the needle.
A structured self-review pass before opening a PR: diff summary, risk scan, assumptions list, and test checklist.
Reduces reviewer load and catches common issues earlier.
Separate planning prompts from implementation prompts, then run an independent verification pass against acceptance criteria.
Improves consistency and reduces 'plausible but wrong' output.
Use structured issue/PR templates and task checklists instead of ad-hoc prompts so teams can share a process.
Turns individual AI skill into team capability.
Best fit: founder-led startups and technical teams that already use AI and want a structured workflow that improves output quality without slowing delivery.
Useful to include