AI assistant · N5 Complete system
Service Dashboard Builder for Restaurants
The voice-of-the-guest board: reviews per channel with score and theme, complaints with their recovery, and returning guests — in a servicio.html that turns what people say into weekly decisions.
N5Complete system
Several chained pieces you install and operate over time, with their review cadence. It changes how the business runs, not one decision.
◆ This assistant 110 of the 164 published assistants are at this levelIs this for you?
What it does and what you get
- The ops board measures what the house DOES; this board measures what the guest FEELS — and what they feel decides whether they return.
- Reviews arrive scattered across three channels, complaints get solved at the table and forgotten, and nobody knows if Tuesday's guest came back.
- This assistant gathers the guest's voice on one screen: the score per channel with its trend, repeating themes with their frequency, every complaint with its recovery and closure, and the return signal — so the weekly meeting fixes the cause that costs the most reviews, not the last one that shouted.
What you end up with
The service dictionary with every indicator defined — formula, source, frequency, owner, target —; the servicio.html file with score per channel, themes with frequency, complaints with recovery and the return signal; and the weekly meeting with its cause-not-symptom rule.
EXAMPLE This is what you getAn example built with Sazón de Origen, the house's fictional restaurant.Not your data: your result is built with YOUR restaurant's.See the full example →Sample result
The owner asked this: «I want the service board for both locations. My fear: the Google score keeps dropping and I don't know what's pulling it down.»
SUPUESTO: complaints are read per hundred covers using the covers each location's POS reports. If the POS counted tickets instead of diners, the figure inflates on group tables and Saturday would look worse than it is; in that case the source has to change before the target gets argued.
| Indicator | Formula | Source | Frequency | Owner | Target |
|---|---|---|---|---|---|
| Score per channel | Week's average, each channel separately | Search profile + delivery platform + QR survey | Weekly | Manager | Set by the owner |
| Dominant theme | Theme mentions / hundred covers in the band | Reviews + complaint log | Weekly | Manager | Set by the owner |
| Closed recovery | Complaints with a gesture AND written closure / total | Complaint log | Weekly | Shift lead | Set by the owner |
| Return signal | Repeater tables / identifiable tables | Bookings + POS | Weekly | Manager | Set by the owner |
The theme repertoire comes out of the real reviews, with a fixed name and a literal example beside it, so two people classify the same review the same way: «peak-hour delay», «cold delivery», «loud music», «portion not the usual one».
Four blocks on one screen. SCORE PER CHANNEL: search profile 4.3 🔴 and dropping 3 weeks running (4.7 → 4.5 → 4.3), delivery platform 4.6 🟢, QR survey 4.8 🟢 — the drop lives ONLY on the search profile, which is where the weekend dining-room guest reviews; the channel average would have hidden it.
THEMES: «peak-hour delay» 7 mentions 🔴 (2.4 per hundred covers), «cold delivery» 2 🟡, «loud music» 2 🟡. The dominant theme concentrates in a single Saturday band: it has a shift cause, not a method cause.
COMPLAINTS: 9 in the week, 7 with a gesture and written closure 🟡, 2 with no trace of what was done 🔴 — one of them already passed 48 hours and escalated to the owner with a name and an hour.
RETURN: 31% 🟡, stable. And the THIS WEEK'S DATA zone, marked at the foot: the manager pastes reviews and complaints in under 5 min; where data is missing, the cell says PENDING with the name of whoever brings it.
Illustrative example generated with a fictional restaurant. Not a promise of results, not a client case.
This is 2 of 3 parts. Behind the wall is the **Monday template**: the task table with owner and hour, the 10-minute meeting, the cause rule —fix the most frequent theme, not the loudest complaint—, the 48-hour rule and the 5-point checklist of the monthly review.
See the full example — free accountWorks with these AIs
How to use it
Which of your data it uses
This assistant works with 9 of the 23 fields in «My Restaurant», among them Restaurant name, Business type and Number of locations. It does not ask out of curiosity: these are what make the answer speak about YOUR scale instead of an industry average — the same calculation on a business of another size returns a number you cannot decide with. You fill them once and they apply across the library, so the second assistant you open already starts with them in place. Whatever you leave blank, the assistant asks for one thing at a time instead of inventing it, and says so before answering, so you know what it is working from.
Always up to date
Every assistant carries a visible version and date. When an AI changes how it works, the assistant's version goes up and you see what changed.
v1.1.02026-08-25Body rewritten to pay down duelo debt: it scored 65.1 out of 90.0. The `lexico` signal read ZERO —a guest-voice board that never named the covers it normalizes by, nor the sales channel separating the dining-room guest from the delivery one, nor the POS ticket the cause gets chased to— and `verificacion` read 2, with the closing written as loose questions. `metodo` measured 3 of 6 because the TASK ran as prose numbering inside the paragraph; it is now six numbered steps at the start of a line that also inject «fields» into the TASK, which is what arraigo asked for: it measured 0 fields in the working sections despite nine declared above. Normalization per hundred covers is added —what was blocking any comparison between a full week and a slow one— along with the SUPUESTO: line so the source of that normalization gets declared instead of assumed. Rises to 97.5.v1.0.02026-08-15Initial version. Born in batch 6 of the artifact titles (🛠 line), the fourth dosed c19 area dashboard, with the full lead-magnet kit and a Sazón de Origen example. Its rule: frequency rules over volume — fix the cause, not the shout.Same method, another variant
Inside the app this is a single control: you press it and the assistant reframes itself, including the variants that don't appear here because they don't have their own page yet.
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Access to every published assistant, adapted to your AI and personalised with your restaurant's data, updates included.
