One-minute narrated stories of a flow at work. The scenarios and figures are illustrative, not customer results.
Dental care
The challenge. Mariam runs patient services at a dental group with eight clinics across Dubai. Patients message on WhatsApp all day, and all night. Four hundred messages a day: prices, times, a broken tooth at eleven at night. The front desk answers between patients, hours later. By then, half of them have booked somewhere else.
The flow. With xMap Flows, Mariam built Concierge. Whenever a patient writes on WhatsApp, day or night, Concierge answers their question in plain words, checks the free chairs table for the clinic nearest them, replies with a time, and puts it in the dentist's calendar.
The result. Now every message gets an answer within a minute, even at three in the morning. Concierge books sixty appointments a week on its own, and the front desk finally looks up at the patients in front of them. The clinics close at ten. The front desk never does.
1 minto answer, day or night
60bookings a week on its own
0messages left unread
Blocks: WhatsApp Business event → AI agent → My data → WhatsApp Business → Google Calendar
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Patient transport
The challenge. Ingrid plans patient transport at a big hospital in Oslo. Every morning, her vans collect sixty patients for dialysis. Dialysis cannot wait. But when a road closes, the driver finds out at the barrier. The van turns back, the patient waits in the cold, and the whole shift starts late. Ingrid spends her mornings on the phone, rerouting by hand.
The flow. With xMap Flows, Ingrid built Fox. Every morning at half past five, Fox takes the day's sixty pickups, checks for road closures and roadworks near each one, and where a road is closed, works out how much earlier the van must leave, and tells the driver before the engine starts.
The result. Yesterday, Fox found four closures on the morning rounds, two of them roadworks in Grünerløkka. Those drivers left fifteen minutes early, and every patient was in the chair on time. Ingrid drank her coffee while it was still hot. The roads still close in Oslo. Ingrid's patients just never find out.
60pickups checked by six
4closures caught yesterday
100%patients on time
Blocks: Schedule → My data → Traffic incidents → Branch → Calculated field → Slack
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Pharmaceuticals
The challenge. Amani is a medical rep for a pharmaceutical company, covering four hundred clinics and pharmacies across Nairobi. She can see eight a day. So which eight? She goes back to the doctors she knows, and some clinics have not seen her since March. Meanwhile the busiest clinics in Kilimani have never heard her name.
The flow. With xMap Flows, Amani built Badger. Every Sunday evening, Badger looks at every clinic and pharmacy in her area, matches them with her visit log, counts the weeks since she was last there, asks Jev how much each one needs a visit, keeps the top forty, plans the shortest drive, and puts the week in her calendar.
The result. This week, Badger gave her forty visits. Twelve are busy clinics she had not seen since spring, and three are big pharmacies she had never walked into. Her driving fell by a third, and within two months her orders in Westlands doubled. Amani used to visit the clinics she remembered. Now she visits the ones that need her.
40visits planned each week
12forgotten clinics back on
2xorders in Westlands
Blocks: Schedule → Nearby places → Match with my table → Calculated field → Jev decision → Sort and limit → Route planner → Google Calendar
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Home care
The challenge. Huong is a home-care nurse in Ho Chi Minh City. She rides her scooter to nine elderly patients a day. In the rainy season, the afternoon storms come fast. Streets flood in minutes, and she sits under a bridge for an hour while a patient waits for insulin. She plans her day by looking at the sky, and the sky keeps changing its mind.
The flow. With xMap Flows, Huong built Lynx. Every morning at six, Lynx takes her nine patients, checks the rain forecast at each home, puts the homes facing heavy rain in the morning, plans a morning loop and an afternoon loop, and tells each family when she will arrive.
The result. Yesterday, Lynx saw heavy rain coming to Binh Thanh. Huong saw those four patients before lunch, and spent the storm indoors with a patient across the river. Nine visits, no flooded streets, and every dose of insulin on time. Huong still works in the rainy season. She just stopped working in the rain.
9visits, every one on time
4homes moved before the storm
0hours stuck in the rain
Blocks: Schedule → My data → Weather forecast → Calculated field → Route planner → WhatsApp Business
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Health insurance
The challenge. Megan runs provider contracts at a health insurer in New York. Members keep writing: there is no urgent care near me in your network. She knows the gaps exist, but not where. Every quarter, an analyst spends a month comparing lists of clinics by hand. By the time the report is ready, new centres have opened, and other insurers have signed them.
The flow. With xMap Flows, Megan built Cartographer. On the first Monday of each month, across the five boroughs, Cartographer finds every urgent care centre, skips the ones already in the network, keeps those rated four stars or more, scores how much each one is worth signing, and hands the contracting team a report with a map.
The result. Last month's report found thirty-eight well-rated centres outside the network, nineteen of them in Queens and the Bronx, where complaints were highest. Her team signed eleven in six weeks, and complaints about access fell by half. Megan stopped counting clinics. Now she closes the gaps.
38centres outside the network
11signed in six weeks
-50%access complaints
Blocks: Schedule → Area of interest → Nearby places → Match with my table → Filter → Jev decision → Make a report
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Health insurance
The challenge. Freya leads member services at a health insurer in London. Members email when their doctor sends them to physio: which clinic can I use? Her team looks up each policy, searches for clinics, checks the network. Each answer takes two days, and some members pay privately and claim later, at twice the cost.
The flow. With xMap Flows, Freya built Triage. When a member's email arrives, Triage reads what treatment they need, looks up their policy and where they live, finds physiotherapy clinics near their home, keeps the ones in the network, picks the three nearest, and replies within the hour.
The result. Yesterday, two hundred and forty members asked. Every one had three covered clinics near home within the hour, most a short walk away. Private bills fell by a third, and her team now spends its time on the hard cases. Freya's members used to wait for an answer. Now they wait for their appointment.
1 hto answer, down from 2 days
240members answered yesterday
-33%private bills
Blocks: Microsoft Outlook event → xMap LLM → Match with my table → Nearby places → Sort and limit → Microsoft Outlook
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Pharmacy
The challenge. Hassan owns a pharmacy in Gueliz, the new town of Marrakesh. When a heatwave hits, everyone wants the same things at once: rehydration salts, sun cream, cooling gel. His wholesaler needs three days. Last July he ran out on day two of forty-five degrees, and sent forty customers across the street.
The flow. With xMap Flows, Hassan built Steward. Every evening at nine, Steward reads the week's forecast for Marrakesh, looks for days above forty-two degrees, checks his stock list for what is running low, works out how much he will need, and emails the order to his wholesaler.
The result. On Sunday night, Steward saw forty-four degrees coming on Thursday. The order went out four days early: six hundred sachets of rehydration salts, two hundred tubes of sun cream. When the heat came, his shelves were full and the queue was at his door. Hassan no longer reacts to the heat. He is ready for it.
4 daysof warning before the heat
600sachets ordered in time
0customers sent next door
Blocks: Schedule → Weather forecast → Filter → Match with my table → Calculated field → Gmail
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Family clinics
The challenge. Ploy leads expansion at one of Bangkok's biggest family clinic chains. The board wants five new clinics next year. For every site, her team spends two weeks on spreadsheets and visits. Who lives nearby? How many clinics already compete? The answers are always out of date. The last clinic opened in a street full of offices: busy at lunch, empty every evening.
The flow. With xMap Flows, Ploy built Atlas. Whenever her team adds a possible site, Atlas draws everything within fifteen minutes by car, counts the children and older people living inside, finds the clinics already there, works out how many people each clinic serves, and sends the board a report the same day.
The result. This month her team tested twenty-two sites, not four. In Bang Na, two hundred and ten thousand people live within fifteen minutes, one clinic for every nineteen thousand. That site went to the board on Monday and was approved on Friday. Ploy stopped guessing where people live. Now she opens where they need her.
22sites tested, up from 4
1 dayfrom idea to board report
19,000people per clinic in Bang Na
Blocks: New rows → Travel area → Demographics → Nearby places → Calculated field → Make a report
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Hospitals
The challenge. Noah runs night operations at a large hospital in Seattle. Every night, twelve surgeons and nurses are on call. On icy nights, the calls come in and the people do not. A surgeon who lives forty minutes away needs ninety on frozen roads. Last winter, an emergency operation waited an hour and ten minutes for its team.
The flow. With xMap Flows, Noah built Nightwatch. Every afternoon at three, Nightwatch takes tonight's on-call list, checks the night's forecast at each person's home, works out how far they live and the risk of ice, and if they live far on an icy night, offers them a room by the hospital, while the others get a drive-safe note.
The result. This January, Nightwatch saw ice coming on nine nights. Thirty-one people slept a few minutes from the theatre instead of an hour away. On the worst nights the team now arrives in eighteen minutes, and no operation waited for them. On icy nights, Noah's team no longer races the roads. They are already there.
18 minfor the team on icy nights
31rooms offered before the ice
0operations kept waiting
Blocks: Schedule → My data → Weather forecast → Calculated field → Branch → Microsoft Teams
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