AI Workflow Assessment
Start with one workflow and a clear question: would AI make this work more useful, reliable, or efficient?
HARTRO helps teams identify an opportunity, build a focused prototype, and evaluate the result before deciding what comes next.
Start with the work
Bring a recurring task, a slow handover, or a decision that takes too much manual effort. We look at how the work happens today: the people involved, the inputs they use, the exceptions they handle, and the cost of getting it wrong.
The result may call for an AI assistant, ordinary automation, a change to the process, or no new system at all. The assessment should make that choice clearer.
What you leave with
| Stage | Concrete output | Decision it supports |
|---|---|---|
| Assess the workflow | A workflow map, constraints, a scoped use case, and success criteria | Is this worth prototyping? |
| Build a focused prototype | A working demonstration with representative inputs and documented limitations | Does the approach hold up on realistic examples? |
| Evaluate the result | Test cases, observed results, failure analysis, and a recommendation | Continue, revise, or stop? |
Scroll sideways to see all columns.
We agree the scope and outputs before starting. Each stage informs the next decision; a promising prototype is a reason to investigate further, not proof that a system is ready for daily use.
Define success before building
A useful baseline describes how the work performs today. Depending on the workflow, that might include completion time, review effort, errors, or the quality of the resulting decision.
We also identify behavior that should block progress: an unauthorized action, missing evidence, or a confident answer where the system should have asked for review. A single average score can hide those failures.
Work you can inspect
In our skin-analysis investigation, an AI coding agent helped explore methods, train small models, and build review tools. The experiments also exposed an evaluation limit: finding more marked examples did not establish better overall accuracy.
That is why we treat evaluation as part of the work. A result is useful when we can explain what it establishes and what remains uncertain.
Bring one workflow
Email hi@hartro.com with a short description of the task, who does it, and what you would like to improve. Mention any systems it depends on and any constraints you already know. You do not need a finished specification or a dataset to start the conversation.
Please leave confidential data and credentials out of the initial email. You can also use the contact form.