Turnkey Quote-to-Sales
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A library of representative tasks is built. This can include things like handling inbound questions, qualifying leads, drafting quotes, troubleshooting issues, processing cancellations, or responding to objections. This way evaluations reflect real-world usage.
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Scoring Rubrics that address accuracy, compliance, tone, completeness, next-step correctness, escalation logic, and hallucination avoidance are created. Each benchmark must have objective pass/fail and scoring thresholds.
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For every benchmark scenario, we need to include SME-written ideal responses or step-by-step resolutions. These serve as the reference outputs that the model is evaluated against during fine-tuning and regression testing.
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As client SOPs, pricing, messaging, or compliance rules evolve, we need to refresh benchmark sets to ensure the AI model is always tested against the current, accurate version of the domain-specific information.
