AI Workflow Audit
Map one repeated process, define what correct means, identify risk and approval boundaries, and make a written go/no-go recommendation.
- Current-state workflow map
- Failure and approval analysis
- Prioritized next steps
Flying Fish Digital
AI workflow engineering for technical operations
Flying Fish Digital turns document-heavy processes into tested AI-assisted workflows—with evaluation, human review, and clear handoffs built in.
Prompt chains
Structured outputs
Document workflows
LLM evaluation
Human-in-the-loop design
Start with the problem
The right first step may be an AI workflow, ordinary automation, process simplification—or no new tool at all.
Map one repeated process, define what correct means, identify risk and approval boundaries, and make a written go/no-go recommendation.
Build what the evidence supports
Turn a bounded use case into a working, testable prototype with structured outputs, representative examples, and known limitations.
Test an existing AI feature against representative cases, expose repeatable failures, and turn findings into an actionable remediation plan.
Evidence before automation
Good workflow engineering is less about choosing a fashionable model and more about understanding the real work, its failure cost, and the person responsible for the result.
“Plausible-looking” is not an acceptance criterion.
Walk through the actual process and representative inputs.
Remove waste and clarify ownership before adding automation.
Use the smallest mechanism that can do the job reliably.
Test representative cases, exceptions, and human review paths.
Document operation, limits, and the evidence needed to maintain it.
Built for operational reality
Flying Fish Digital is led by Jared Hagadorn, whose background spans aviation electronics, commercial diving leadership, industrial inspection, safety-sensitive field operations, and building commercial software with AI.
That experience shapes a direct working style: define the job, protect the boundary, test the result, and leave the client with something they can actually operate.
A good first conversation is specific
Bring one workflow, a few representative examples, and the person who knows what a correct result looks like.