A restaurant where posts, review replies, notices and banquet quotes waited until after closing and were shelved when busy. Now one AI manager dispatches 23 specialist assistants: one sentence yields a ready-to-use draft, and every piece is still approved by the owner before it goes out.
A business in restaurants (presented anonymously; the client name and company size will be published once the client approves). This is a system PeakQi actually delivered and put live — screenshots are from the real product.
Posts, review replies, closure notices and banquet quotes waited until after closing and were written from scratch each time. When things got busy they were set aside — reviews went unanswered for days.
One AI manager dispatching 23 specialist assistants: one sentence in, ready-to-use posts, review replies, notices and quotes out — into a review queue.
Additional integrations are scoped per project.
Built and delivered between August and September 2026 (per project records). For reference: Phase 1 on PeakQi standard modules goes live in as little as 10 working days (sign Day 0 → build Days 1–4 → test Days 5–7 → calibrate and launch Days 7–10); a full vertical platform like this one starts around six weeks.
AI only drafts, and asks first when hard facts like prices or dates are missing; every piece is approved by the restaurant before it goes out.
| Area | Before | After |
|---|---|---|
| When it gets done | Only after closing — often never | One sentence to delegate; schedules run on their own |
| What content is based on | Rewritten from memory each time; prices easy to get wrong | Cites the uploaded menu and rules; asks when info is missing |
| Publishing | Posted as written, no second pair of eyes | Everything queues for review; approved before publishing |
※ Outcomes reported for this specific case. The measurement period, pre-rollout baseline and calculation method are still being compiled for publication; until then these figures are excluded from structured data and are not a general performance promise. Actual results vary with your process, data quality and rollout scope.
