Who is this assistant for?

Something is bleeding this month and every person on your team points at a different cause. You applied the fix that worked for a colleague and the problem came back. You put out the noise last week and it is already coming back. You sell almost what you sold last year and the margin has vanished.

Facing a serious problem, owners tend to do two things: blame the last thing that changed and apply the remedy that worked for a colleague. Both fail often, because critical restaurant problems almost never keep their cause where the symptom shows: sales drop in the dining room but the cause lives in the kitchen, or in a price, or in a badly built shift. This assistant walks you through a root cause analysis with method: measured symptom, rival hypotheses, evidence that eliminates them, and a two-speed plan, containment now and deep correction.

AI assistant · N5 Complete system

◆ Included in the annual plan🎯 General Management and Strategyv1.2.0 · 2026-08-20

Critical Problem Analyzer for Restaurants

When something serious is happening in your restaurant — sales dropping, staff leaving, margin vanishing — this assistant hunts the root cause with evidence and separates the urgent patch from the lasting cure.

Depth

10/23

Your data

Compatible AIs

Steps to use it

Depth of the resulttap a level to see what you get

◀ Put out today's fireRun the year ▶

🏛️

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 level

Is this for you?

What it does and what you get

Facing a serious problem, owners tend to do two things: blame the last thing that changed and apply the remedy that worked for a colleague.
Both fail often, because critical restaurant problems almost never keep their cause where the symptom shows: sales drop in the dining room but the cause lives in the kitchen, or in a price, or in a badly built shift.
This assistant walks you through a root cause analysis with method: measured symptom, rival hypotheses, evidence that eliminates them, and a two-speed plan, containment now and deep correction.

What you end up with

A case file of the problem: quantified symptom, hypothesis table with evidence for and against, the root cause identified with its causal chain, and an immediate containment plan plus definitive correction with dates and verification.

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

Sazón de OrigenFictional contemporary Colombian restaurant in Medellín, 2 locations, 6 years in business. Every sample result in the library comes from this same case. Fictional restaurant

The owner asked this: «Tuesday-to-Friday lunch dropped from 52 to 45 covers per location and it has been eight weeks. On the floor everything looks the same and nobody knows what happened.»

What: covers in the 12:00–14:30 window, Tuesday to Friday. How much: −7 per location per day, 13.5% off the baseline of 52. Since when: the curve falls from week 3 of the last 8 and flattens in week 6. Where it hurts: dining room, not delivery. What the month costs: 7 covers × 2 locations × 4 days × 4.3 weeks = 241 covers, which at the 48,000 COP ticket is 11,568,000 COP of sales and 7,635,000 COP of product contribution a month.

To put it on the house's scale: operating profit for the whole month, at 8% on 214,000,000 COP, is 17,120,000 COP. This leak is taking 45% of that.

Week	What changed	Who decided it	Observed effect
1	Nothing on record	—	Baseline: 52 covers per location
2	The cook on the pass at location 2 leaves	Resignation	None that same day
3	An assistant covers the pass, with no written standard recipe	Chef	The drop begins, at location 2 only
4	Delivery-platform promotion	The platform	90 more orders a month through that channel
6	A new meat supplier comes in	Purchasing	No measurable effect on covers
8	The drop shows up at location 1 too	—	45 covers per location

SUPUESTO: product contribution is calculated at 66%, the complement of the 34% food cost the house declares; payroll and rent stay out of the dish per the house costing rule. If the real food cost of lunch were higher than the menu average —and it usually is when the dish that rules is a bandeja— the leak in money is smaller than calculated and drops one notch of urgency. The cause does not change.

Hypothesis	Evidence for	Evidence against	Status	Missing data
A · Delivery is eating the dining-room customer	Delivery orders rise from week 4	They rise by 90 orders a month and the dining room loses 241 covers: not even half adds up. And the drop began in week 3, before the promotion	Discarded	—
B · A competitor opened nearby	Nobody has looked	The drop starts at one location and the other takes 5 weeks to follow; a new competitor does not respect that order	On hold	A 20-minute walk around the block. Costs nothing and nobody has done it
C · Lunch has been going out slower since the pass changed	The drop starts in week 3, at location 2, the week after the assistant came in. It concentrates in 12:30–13:30, the hour of whoever has a counted lunch break	The floor manager says «everything looks the same» — and it does look the same: the tables are full, they just take longer to clear	Standing	Time 20 orders in that window at each location, 2 days

The root cause, with its chain. It is not the cook who left. It is that the lunch ticket time was never written down, so it lived in one person's memory. When that person left, the assistant rebuilt it his own way; the pass got longer; the 30-to-45-year-old professional eating on a counted hour waited once, waited twice and stopped coming back. Location 1 followed location 2 five weeks later because there the same post is covered by different people each week, and with no written standard everyone does it their own way.

The cause is a standard that does not exist, not a person who failed. And there is a second one, which shows up when you ask why the pass could stretch for five weeks without anyone noticing: nobody measures ticket time. The symptom took eight weeks to reach the till because there was nowhere else it could have surfaced sooner.

Illustrative example generated with a fictional restaurant. Not a promise of results, not a client case.

This is 2 of 3 parts. The case file can be read here up to the root cause and its chain. Behind the wall is the double plan: the 72-hour containment that does not spike labor cost or destroy evidence, the 30-day root fix with an owner and a date on every line, and the verification autopsy that decides whether the cause stayed dead or merely went quiet.

See the full example — free account

Works with these AIs

Claude — the best for this assistant A root-cause investigation is won by resisting the first explanation. Ask it to think step by step and demand it kill one hypothesis WITH evidence before naming the cause. It holds the back-and-forth interrogation well, which matters here: it will ask for data one item at a time and you need to answer without losing the thread.

How to use it

Take the assistant to your AI and describe the problem as you live it, with whatever figures are at hand.

Follow its hypothesis interrogation; it will ask for specific data, dig it up even when tedious.

Apply this week's containment and schedule the deep correction with its verification date.

Which of your data it uses

of 23

This assistant works with 10 of the 23 fields in «My Restaurant», among them Restaurant name, Business type and Team size. 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.

Restaurant nameBusiness typeTeam sizeActive sales channelsStrong and slow daypartsAverage monthly salesApproximate food cost (%)Approximate labor cost (%)Best-selling dishesMain goal this year