This service aims to measure whether a responsible LLM integration produces positive gains, or whether the time spent and the risks cancel the benefits of the technology.

Organizations are integrating large language models into decision-making: document review, due diligence, risk assessment, policy drafting, knowledge retrieval. The assumption is that this saves time and improves quality. That assumption is rarely tested.

For each decision type, we measure three things.

A report containing:

Classification of each decision type as suitable, unsuitable, or conditionally suitable for LLM integration. Measured verification burden and time-cost comparison per workflow. Patterns in how decision-makers decide what to verify and what to skip. Specific recommendations: where to continue, stop, or redesign. A repeatable protocol for reassessment as models or requirements change.

Structured observation of real decision tasks, using the client's own tools and scenarios.

Participants complete exercises with and without LLM assistance. Every piece of LLM-sourced information that enters a decision is tracked. Post-task interviews capture whether verification occurred, how, and why. Time is recorded across conditions.

A typical engagement runs a half-day to full-day with a small group. Larger assessments are scoped case by case.

This diagnostic does not evaluate output quality, bias, prompt vulnerability, or model alignment. It does not assess regulatory compliance. It measures one thing: whether the cost of responsible use makes LLM integration worthwhile, or not.