Can you test whether an AI workflow misses important facts?
Yes. A bounded evaluation can test whether a workflow notices critical evidence, asks for missing information, chooses a defensible next action, and expresses uncertainty appropriately. It is not a certification that the system is safe in every setting.
What a solution might involve
Define the workflow and its authority. Assemble representative and difficult cases with a defensible reference, separate the evidence from hidden evaluation criteria, and record actual outputs. Review consequential findings before turning automated screening labels into claims about performance.
What needs care
An unfair prompt, ambiguous benchmark, or missing reference can make the evaluation itself wrong. Results depend on model version, settings, evidence, and tool access. A demonstration on unrelated systems is not evidence that a prospective client's system has the same failures.
How we would judge whether it works
Each reported failure includes the observed answer, relevant evidence, why it matters, and limitations. Benchmark concerns remain separate from model findings. Remediation is followed by a comparable retest rather than an unsupported claim of improvement.
Likely engagement
Business Engine: $5,000 to $15,000. This is a likely starting tier, not a quote. Scope, access, dependencies, and responsibility for operation need agreement first.
Discuss Business EngineWhat to send first
Bring one defined workflow, its permitted actions, and examples of consequential mistakes you want to prevent.
Experience and scope
Selected builds documents the currently authorized public proof. This page describes a proposed approach, not a claim of a completed client engagement in this exact workflow.
Related problems
- Can you help us use AI in a real business workflow?
- Can we automate a workflow but keep a person in control?
Have something you need built, fixed, or figured out?
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