One-minute narrated stories of a flow at work. The scenarios and figures are illustrative, not customer results.
Public health
The challenge. Camille leads the summer heatwave plan for the city of Paris. Each heatwave, the city opens cool rooms in town halls and libraries. Deciding which ones took two days of calls, so they opened after the heat had already arrived. And some opened where few older people live.
The flow. With xMap Flows, Camille built Canopy. Every summer morning at six, for each of eighty cool rooms, Canopy reads the next three days’ forecast, keeps the ones where heat is coming, counts the older people living around each, picks the twenty that matter most, writes each opening notice, and sends it to the district halls.
The result. In July, Canopy saw thirty six degrees coming three days ahead. It picked the twenty rooms with the most people over sixty around them, and the district halls had the notices by eight that morning. Every room was open before the first hot afternoon. Camille stopped racing the heat. Now the cool rooms open first.
3 dayswarning before the heat
20cool rooms opened
8 a.m.notices out, not two days later
Blocks: Schedule → My data → Weather forecast → Filter → Demographics → Sort and limit → xMap LLM → Microsoft Outlook
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Emergency services
The challenge. Kwame runs dispatch for the city ambulance service in Accra. His crews pick a hospital by habit. When the approach road is jammed, an ambulance can sit for twenty minutes a few hundred metres from the door. Dispatchers only learn it from the radio, after the patient has waited.
The flow. With xMap Flows, Kwame built Beacon. Every fifteen minutes, at each of six emergency entrances, Beacon measures how the roads are moving, ranks the hospitals clearest first, updates the dispatch map, and if an approach is at a standstill, warns every dispatcher at once.
The result. Now the dispatch room keeps one live map open. When the road outside one hospital freezes, Beacon warns the team within a quarter of an hour, and the next crew goes to the clear one. The average run is nine minutes shorter. Kwame cannot clear Accra’s roads. But his crews no longer wait in them.
15 minbetween road checks
6hospital approaches watched
9 minshorter average run
Blocks: Schedule → My data → Traffic Flow → Sort and limit → Show on a map → Branch → Microsoft Teams
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Car sharing
The challenge. Manon plans new stations for one of the largest electric car sharing services in Paris. Every week the city offers her a few streets for new stations. To judge one she pulls up population, charger and metro maps, and a single street takes a morning. The city wants answers by Friday, and good streets go to rivals while she checks.
The flow. With xMap Flows, Manon built Scout. When Manon types a street into the team's chat, Scout weighs it like a planner, counts the people living within five hundred metres, finds the public chargers already there, checks the metro stations and car parks nearby, and answers with a verdict and the numbers.
The result. This week the city offers nine streets, and Manon checks all nine before lunch on Monday. Rue de Belleville has fourteen thousand people within a short walk and just two chargers, so the city gets her yes that evening. Manon used to research streets. Now she just has a conversation about them.
9streets checked by lunch
14,000people near the best one
4 dayssooner than before
Blocks: Chat → AI agent → Demographics → EV charging → Nearby places → Reply
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Heritage protection
The challenge. Giulia is a heritage officer for the city of Naples. She looks after sixty protected monuments in the old town. She and one inspector cannot walk past every site each week. Unpermitted roof work and new terraces go up in days. By the time a neighbour complains, the damage is months old and the fine is the only tool left.
The flow. With xMap Flows, Giulia built Sentinel. Every Monday at seven, for each of her sixty protected sites, Sentinel fetches this week’s satellite picture, keeps only the clear ones, pins them on a map, and saves the week’s file in the inspection folder.
The result. Every Monday Giulia opens one folder: a fresh, clear picture of each monument from the last few days. In ten minutes she spots new scaffolding on two roofs and sends the inspector that same morning. Giulia stopped finding out from neighbours. Now she sees the city before they do.
60monuments seen every week
10 minto review them all
2roofs caught in a week
Blocks: Schedule → My data → Satellite images → Filter → Show on a map → Google Drive
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City traffic
The challenge. Rafael is a traffic officer in Sao Paulo. On match days, the roads around the stadium in Morumbi are his. Sixty thousand fans arrive in two hours. One forgotten roadworks site can block an avenue, and he usually finds it at kick off, from a helicopter feed. Then it is too late to send a crew.
The flow. With xMap Flows, Rafael built Radar. On match days at nine, within three kilometres of the stadium, Radar finds every closure and roadworks site, keeps the roadworks, writes a work order for each, sends them to the crew chief, and pins them on the match day map.
The result. On Sunday, Radar found four roadworks sites at nine in the morning. Crews cleared three by noon, and the fourth was coned into one lane. The fans reached their seats, and Rafael watched the second half. Rafael used to find the roadworks at kick off. Now they are gone by lunch.
9 a.m.roads checked on match day
4roadworks sites found
3cleared before noon
Blocks: Schedule → Area of interest → Traffic incidents → Filter → xMap LLM → WhatsApp Business → Show on a map
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Flood control
The challenge. Rina runs operations for the drainage agency in Jakarta, with seventy pump stations along the rivers and canals. In the rainy season, storms build in the afternoon. Crews are called out once the streets are already under water, and some stations sit unmanned for the first hour. That hour floods homes.
The flow. With xMap Flows, Rina built Monsoon. Every morning at four, for each of seventy pump stations, Monsoon reads today’s rain forecast, keeps the stations where heavy rain is coming, puts the heaviest first, hands them to the crew call out, and marks them on today’s map.
The result. At four this morning, Monsoon saw heavy rain coming for twelve stations in the north. Their crews were called out and in place by noon. When the storm broke at three, every pump was already running. Rina stopped chasing the rain. Now her crews wait for it.
70pump stations checked daily
12crews in place by noon
3 hahead of the storm
Blocks: Schedule → My data → Weather forecast → Filter → Sort and limit → Run another flow → Show on a map
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Local government
The challenge. Amelia runs resident services for the council in Edinburgh. Her contact centre takes three thousand calls a week. Most ask the same three things: what are these roadworks on my street, when are my bins collected, where is the nearest recycling centre. Callers wait twenty minutes for a ten second answer.
The flow. With xMap Flows, Amelia built Osprey. When a resident writes on the council website, Osprey works out what they need, looks up roadworks on their street, checks their bin day, finds the nearest recycling centre, and answers in plain words, right away.
The result. In its first month, Osprey answered eleven thousand questions, day and night. Calls dropped by a third, and the waiting time fell to four minutes for the people who really need a person. Amelia’s team still answers residents. Just the ones who need a human.
11,000questions answered in a month
-33%calls to the centre
4 minwait, down from 20
Blocks: Chat → AI agent → Traffic incidents → My data → Nearby places → Reply
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Eyewear retail
The challenge. Priya plans new stores for an eyewear chain in Mumbai. She has the budget for six openings this year. Reading glasses sell to people over forty, but her shortlist comes from brokers and gut feeling. Two stores opened last year in suburbs full of students and young families. One has already closed, and the other barely pays its rent.
The flow. With xMap Flows, Priya built Keystone. Every quarter, across twelve Mumbai suburbs, Keystone counts the people over forty who live there, finds the opticians already open, works out the over-forties for each optician, keeps the six best, and makes the report for the board.
The result. This quarter, Keystone puts Thane and Chembur on top: about nine thousand people over forty for every optician, three times the city average. Priya walks into the board meeting with one report and leaves with six approvals. Priya stopped guessing where customers live. Now she opens where they squint.
12suburbs compared
3xover-forties per optician
6approvals in one meeting
Blocks: Schedule → Area of interest → Demographics → Nearby places → Calculated field → Sort and limit → Make a report
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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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