[Illustrative example] Using an AI council to find weak spots in a restaurant's subsidy application

This article is not a real customer case. It is an example for illustration only (no approval or funding is guaranteed).

Example setup: a restaurant owner

Imagine a restaurant owner running three locations who is considering applying for a subsidy to improve operational efficiency. They want the subsidy to cover a self-order kiosk and kitchen display system, but aren't confident about how to write the application.

Step 1: Write the brief

The owner summarized the number of locations, the equipment they wanted, and the use of funds in a short brief for MLP Strategy Council, including concrete figures such as past revenue and current order-processing time.

Step 2: Draft and review from different perspectives

AI roles with different perspectives each produced a draft from a different angle — one emphasized the efficiency gains, another focused on the payback period. The three drafts reviewed each other as Response A/B/C, pointing out where the supporting evidence was weak.

Step 3: A chairman AI merges it, then the reviewer objects

The chairman model merged the strongest elements of the three drafts. Then an AI role playing a subsidy reviewer raised concerns such as “the utilization assumption lacks a clear basis” and “the payback calculation method is unclear.” The chairman model responded with a draft objection table grounded in the owner's past performance data.

Step 4: Check the readiness score and revise

The free review showed a readiness score out of 100 along with the top 5 weaknesses. In this scenario, the initial score was around 60, with “insufficient basis for the financial projections” flagged as the biggest weakness. The owner added 12 months of order-processing data and revised the plan accordingly.

Takeaway

What this example illustrates is the value of seeing, before submission, the angles a reviewer is likely to question — something that's hard to catch when writing alone. That said, this outcome does not guarantee subsidy approval; it's meant only to help with preparation. Always review AI-generated content yourself and confirm it reflects reality.