Nathan Austin|AI Workflow Architect
Workflow architecture, not prompt coaching
Routing rules + verification loops + templates integrated into your delivery process.
For founder-led startups and small to mid-sized technical teams. I diagnose how AI is used today, design a workflow system your team can actually run, and enable people to use it well.
Optional add-on: implement and harden automation like pre-PR review agents, checks, and templates once the workflow is proven.
Not this: prompt tricks
Not this: replacing engineers
Yes: workflow design + verification + integration into delivery
In 30 minutes, we map your current AI touchpoints and identify the highest-leverage workflow change.
Problems this fixes
The issue usually is not model access. It is missing structure: no shared standards, weak verification, and inconsistent handoffs.
Offers
The audit is the entry point. Design, enablement, and implementation follow once we know what your team actually needs.
Diagnose how AI is used today, where quality breaks down, and what should be standardized first.
This is the starting point for every engagement.
Design the workflow system, create reusable templates, and teach the team how to run it independently.
Implement and harden repeatable automation once the workflow is proven manually.
Typical audit deliverables (what you get)
Outcomes
Structured AI workflows the whole team can follow
AI-assisted pre-PR review patterns that reduce rework
Better output quality through explicit verification loops
Cleaner handoffs between engineering, data, and product
Faster cycles with less context-switching overhead
Internal templates, checklists, and routing rules
How it works
This is workflow architecture + enablement. The goal is a process your team can run consistently, not dependency on an external operator.
Review current usage patterns, bottlenecks, and quality issues across engineering, data/analytics, or product workflows.
Define structured steps, model routing, verification loops, templates, and team standards that fit your stack.
Run enablement sessions and hand over practical patterns so the team can use the workflow on real work immediately.
Optional add-on: automation hardening
Once the workflow is stable, I can help implement and harden automation such as PR agents, repo templates, checks, and CI quality gates.
Proof / examples
These are examples of the kind of workflow architecture and enablement work I help teams implement.
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.
Contact
Best fit: founder-led startups and technical teams that already use AI and want a structured workflow that improves output quality without slowing delivery.