From catering request to a draft worth reviewing.

A catering request can combine an event description, items, quantities, preferences and exclusions. Turning it into a quotation involves both interpretation and decisions governed by business rules. Those responsibilities need to remain distinct.

The Peninsula work addresses that handoff: use AI to help interpret an incoming request, apply catalog-based rules in the application, and give the sales team a draft to review. The application does not make a model suggestion the final commercial decision.

A draft needs to be examined before it becomes a customer-facing quotation. The reviewer must be able to identify an incomplete request, an uncertain match or a detail that changes the scope. The useful unit of work is the reviewed quotation, not simply the generated text.

The hosted tool includes feedback and evaluation tooling to support learning during the trial. That creates a way to examine use and corrections. It does not, by itself, demonstrate that the tool has reduced effort or improved quotation quality.

The current stage is a sales-team trial. Outcomes have not yet been established. A credible assessment needs observations from real use, with a comparison that makes the starting process clear.

• How much reviewer work is needed to turn a draft into a usable quotation?
• Which missing details, uncertain matches or exclusions require correction?
• Does the workflow help the team complete the job, including exceptions and revisions?
• What problems need to be resolved before broader use?

A Jev evaluation interface has also been implemented for candidate decision tasks. Jev remains inactive in the sales quotation workflow while live qualification is pending. That evaluation is separate from the hosted quotation trial.

There is no established claim here about time saved, quotation accuracy, revenue, or one model outperforming another. Those require completed measurements and an account of how the comparison was made.

• Read the Jev evaluation setup note