AI assistant · N5 Complete system
Stage-by-Stage Customer Journey Builder for Restaurants
Pick the channel above, and the assistant maps the six stages of the journey —discovery, decision, arrival, consumption, exit and return— with their touchpoints one by one, who administers each today, the friction written as a measurable fact and what it costs per month. It closes with the five prioritised frictions, their owner and their date.
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
- To stop arguing about experience with adjectives.
- Almost every journey map circulating in the trade starts when the guest sits down and ends when they pay, which is 40 % of the route: left out are the Google listing where the decision was made, the car park where they had second thoughts, and the silence of the following thirty days, which is where an already-won guest is lost.
- This assistant builds it whole and stage by stage, names the touchpoints one by one, forces a job title behind each —those without one come out marked orphan— and writes every friction as a fact with its minute or its percentage and its monthly cost.
- With no figure beside it, what gets fixed is whatever annoys the owner most; with a figure, what gets fixed is what costs most, and those are rarely the same thing.
What you end up with
The complete map for the chosen channel: the six stages —discovery, decision, arrival, consumption, exit and return— with what the guest does, what they expect and what the house does today; the touchpoints one by one with the job title that administers each and the orphans marked; each point's measure with its source and its frequency; eight to twelve frictions written as facts with their minute or their percentage, classified as process, people or facilities, with their monthly cost calculated in the open; three to five repeatable WOW moments with cost per guest and verification; the return stage at one, seven and thirty days with channel, message and permission; and the quarter's plan with the five prioritised frictions, their owner, their date, their cost to fix and the indicator that must move.
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: «80-seat restaurant in Medellín, 78,000 COP average ticket, around 190 million a month. People come once and do not return; on Google I am at 4.1 with 320 reviews. I want the dining-room journey, but the whole thing — I suspect the problem is before they walk in and after they leave.»
The map starts before the door. The two stages almost nobody has are the first and the last — and they are the two you suspected.
| Stage | What the guest does | Touchpoint | Who administers it today | What is measured today |
|---|---|---|---|---|
| Discovery | Searches «where to eat nearby» | Google listing, Maps | 🔴 ORPHAN | Nothing |
| Decision | Looks at photos, reads 3 reviews, checks price | Google photos, Instagram | Community manager (Instagram only) | Followers, not visits |
| Arrival | Parks, walks in, waits | Car park, door, host | Host (the door only) | Nothing |
| Consumption | Sits, orders, eats, asks for the check | Menu, server, bathroom, order ticket | Service captain | Order time |
| Exit | Pays, says goodbye, leaves | Till, receipt, farewell | Cashier | Average ticket |
| Return | (nothing) | (none) | 🔴 ORPHAN | Nothing |
Three of the six points that decide whether they come back belong to nobody. The Google listing —where 100 % of the decision stage happens— has no owner and is today the restaurant's first face.
Not one adjective. Every line is a fact with its minute or its percentage, and the arithmetic stays in the open.
| Stage | Friction (observable fact) | Type | Figure | Monthly cost |
|---|---|---|---|---|
| Decision | 4 of the 12 Google photos are from 2021 and 2 are not on the current menu | Process | 4.1 ★ with 320 reviews | ~9.4 M COP (est. 5 % lost visits) |
| Decision | 61 unanswered reviews, 14 of them 1 and 2 stars | People | 0 % reply rate | ~6.2 M COP |
| Arrival | They stand 11–14 min with nobody speaking to them or giving an estimate | Process | Fridays 20:00–21:30 | ~7.1 M COP (tables that leave) |
| Arrival | The car park fills at 20:15 and no channel says so | Facilities | — | ~4.0 M COP |
| Consumption | The starter arrives with the main at 4 of 10 tables | Process | 9-min order time | ~3.3 M COP in unsold starters |
| Exit | The check takes 7 min from being asked for | Process | 30 % of tables | Lost table turns |
| Return | No follow-up contact of any kind exists | Process | 0 messages | ~23.7 M COP ← the most expensive |
The return arithmetic, in the open: if 18 % of 2,440 monthly guests return within 90 days and that rises to 21 % (3 points), that is 73 more guests × 78,000 COP × 4 months ≈ 23.7 M COP a year. It is friction number one and it costs nothing to fix: it costs writing it down.
| Stage | What is done | Cost per guest | Who | How it is verified |
|---|---|---|---|---|
| Arrival | At 3 minutes of waiting, someone comes out with a complimentary drink and the real time | ~900 COP | Host | Wait log in the door book |
| Consumption | When a birthday or first visit is mentioned, dessert comes out with the name written on it | ~2,100 COP | Captain | Photo in the shift group |
| Exit | The check is handed over with the server's name and a concrete invitation to a date | 0 COP | Server | Audit of 10 checks a week |
None depends on somebody feeling inspired, which is why they are WOWs and not a good day.
Illustrative example generated with a fictional restaurant. Not a promise of results, not a client case.
This is 3 of 5 parts. Behind the wall sit the two pieces that turn the map into execution: the return stage written day by day —channel, message and permission— and the quarter's plan with the five prioritised frictions, their owner, their date, their cost to fix and the review routine.
See the full example — free accountWorks with these AIs
How to use it
Which of your data it uses
This assistant works with 18 of the 23 fields in «My Restaurant», among them Restaurant name, Business type and Cuisine or concept. 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.
v1.0.02026-08-27Born on Diego's brief (2026-08-27): «we are missing a professional stage-by-stage customer journey builder for restaurants». It merges the eight journey cards in c29 —general journey, by buyer persona, by channel, by consumption moment, by consumption reason, for reservations, for delivery and for events— plus the integral experience map, which were ONE axis written nine times: the CHANNEL. The six stages of the route are the deliverable's structure and not a variant, so they live inside it and not in the selector.v1.0.12026-09-21The deliverable stops being an analysis in blocks and becomes a printable OPERATING DOCUMENT: one customer_journey.html with the print button inside, sixteen sections, THREE FIGURES — the journey band, the emotion curve and what each friction costs a month, each with its data table — and the blank landscape walk sheet. Micro-stages arrive: a stage cannot be fixed and a micro-stage can.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.
Included in the always-growing library
Access to every published assistant, adapted to your AI and personalised with your restaurant's data, updates included.
