One-minute narrated stories of a flow at work. The scenarios and figures are illustrative, not customer results.
Organic cosmetics
The challenge. Jonas leads sales for an organic cosmetics brand in Hamburg. His team of five sells to concept stores and beauty shops. Their prospect list comes from three places, so the same shop shows up two or three times. Reps call stores that already stock them. One owner in Ottensen got four calls in a week, and asked them to stop.
The flow. With xMap Flows, Jonas built Prospector. Every Monday at eight, across greater Hamburg, Prospector finds the beauty and concept stores, searches the map for zero-waste and organic shops, keeps each shop once, leaves out the ones that already stock the brand, scores how well each one fits, and puts them in the team’s prospect sheet.
The result. This Monday, the sheet holds thirty-one fresh shops, each listed once, best fit first. No rep calls a current stockist. In a month the team opens nine new accounts, and the shop in Ottensen orders its first box. Jonas’s team stopped calling the same shop four times. One call is enough now.
31fresh shops a week
0calls to stockists
9new accounts in a month
Blocks: Schedule → Area of interest → Nearby places → Map business scan → Remove duplicates → Match with my table → xMap LLM → Google Sheets
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Machine tools
The challenge. Marek sells used machine tools in Krakow, on his own. Lathes, mills and presses, mostly to factories in southern Poland. A factory buys a machine about once in five years, and nobody tells him when. Marek cold-calls from an old list of three hundred plants. Nine in ten say no. When one is really ready to buy, a bigger dealer usually got there first.
The flow. With xMap Flows, Marek built Radar. Every Monday at seven, Radar finds the factories around Krakow hiring engineers and machine operators, looks each one up on the map, skips the ones already in his customer list, reads each job ad for the machines they run, keeps the best ten, and emails him the week’s calls.
The result. This Monday, Radar finds twenty-three factories hiring. One in Wieliczka wants three machine operators for a new line. Marek calls before nine, and on Friday he sells them two used lathes. A job ad is a factory saying it is growing. Radar makes sure Marek hears it.
23factories hiring this week
10calls that matter
2lathes sold by Friday
Blocks: Schedule → Job postings → Map business scan → Match with my table → xMap LLM → Sort and limit → Email
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Business telecom
The challenge. Sakura leads business sales for a big telecom company in Tokyo. She sells office internet and phone lines. The best moment to win a company is when it moves office. But she hears about moves after the contract is signed with someone else. Her team calls hundreds of offices in Marunouchi and Shinagawa, asking if anything is changing. Almost nothing is.
The flow. With xMap Flows, Sakura built Birch. Every Monday at eight, Birch finds the Tokyo companies hiring office and facilities managers, looks up each company’s office, scores how likely each is to be moving or opening an office, keeps the top fifteen, and sends the list to her team.
The result. This week, Birch finds ninety companies hiring office managers, and fifteen that look like a move. Her team calls those first. Four are planning new floors in Shibuya and Toyosu, and two sign before they move. Sakura’s team stopped asking who is moving. Now they call the ones who are.
90companies hiring office staff
15likely to move
2contracts signed early
Blocks: Schedule → Job postings → Map business scan → Jev decision → Sort and limit → Email
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Food distribution
The challenge. Sara is a field sales rep at a big flour and baking supplies company in Tunis. She sells to bakeries. Every morning she scrolls the map looking for new bakeries. Most are closed, or already buy from someone else. By six, she has made four visits, and two were dead ends. And the new bakery she drove past on Tuesday? A rival signed it yesterday.
The flow. With xMap Flows, Sara built Scout. Every morning at seven, across greater Tunis, Scout finds the bakeries that opened recently, skips the ones already on her books, scores how likely each one is to buy, keeps the best six, plans her route for the day, and sends each one a hello.
The result. At eight, Scout tells her twelve new bakeries opened this week. The best six are on her route, and each one has already heard from her. Sara makes nine visits, every one a real prospect, and she is first through the door. Sara stopped searching for customers. Now she just meets them.
12new bakeries found this week
9real visits a day, up from 4
6hellos sent before she arrives
Blocks: Schedule → Area of interest → Nearby places → Match with my table → Jev decision → Sort and limit → Route planner → Outreach
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Craft beer
The challenge. Mateo leads sales at a craft brewery in Monterrey. Four reps sell his beer to bars and restaurants across the city. New bars open every week in San Pedro and Barrio Antiguo, and his reps hear about them late. Two reps visited the same bar last Thursday. Nobody visited the new rooftop bar in Cumbres, and a big brewer put its taps in first.
The flow. With xMap Flows, Mateo built Radar. Every Monday at seven, across metro Monterrey, Radar finds the bars and gastropubs that opened recently, keeps only the ones it has not seen before, puts the best rated first, splits them fairly between the four reps by area, and emails each rep their own list.
The result. On Monday, Radar finds eleven new bars. Each rep gets two or three, close together, with the address and the rating. No double visits, no missed openings. By Friday, seven of the eleven are pouring Mateo’s beer. Mateo’s reps stopped chasing rumours. Now they arrive before the paint dries.
11new bars found on Monday
0double visits
7bars pouring by Friday
Blocks: Schedule → Area of interest → Nearby places → Only new → Sort and limit → Split by territory → Email
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Healthcare staffing
The challenge. Ruby leads the healthcare desk at one of Australia’s largest staffing agencies, in Sydney. She places nurses in aged-care homes. Every morning her team trawls job sites for homes that need nurses. By the time they call, the ad is a week old and another agency has filled it. And when they do get through, it is to reception, not the director of nursing.
The flow. With xMap Flows, Ruby built Beacon. Every weekday at half past six, across greater Sydney, Beacon finds every aged-care home that posted a nursing job overnight, skips the homes already on the books, puts the fifteen hardest roles to fill first, finds the director of nursing at each, and writes to each one, once Ruby approves the note.
The result. At seven, Beacon has eighteen homes that posted overnight. The fifteen hardest to fill each get a note to the director of nursing, with three nurses ready to start. By lunch, six directors have replied, and four nurses are placed by Friday. By the time rivals read the ad, Ruby’s note is already in the inbox.
18homes hiring, found overnight
6directors replied by lunch
4nurses placed by Friday
Blocks: Schedule → Area of interest → Job postings → Match with my table → Sort and limit → Apollo → Outreach
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Bookkeeping
The challenge. Lauren runs a one-woman bookkeeping practice in Houston. Her clients are independent shops, cafes and studios. New clients come only through referrals, and referrals have dried up. Every new business needs a bookkeeper in its first month. But by the time Lauren hears about one, its owner has signed with a big firm downtown.
The flow. With xMap Flows, Lauren built Ledger. Every Monday at seven, across the Heights and Montrose, Ledger finds the shops, cafes and studios that opened this month, keeps the independents, scores which ones need a bookkeeper most, keeps the best eight, and emails Lauren the list, with a note drafted for each.
The result. On Monday, Ledger finds twenty-one new businesses, and the best eight are in her inbox with their phone numbers. Lauren calls them over coffee and visits three that afternoon. Two sign before their first month is out. Lauren stopped waiting for referrals. She meets new owners in their first week.
21new businesses this week
8best fits, called by ten
2new clients a week
Blocks: Schedule → Area of interest → Nearby places → Filter → xMap LLM → Sort and limit → Gmail
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Restructuring advice
The challenge. Lea is an independent restructuring adviser in Berlin. She helps family manufacturers when a supplier gets into trouble. When a key supplier files for insolvency, her clients have days to find another. The court notices are public, but nobody reads them every day. Last year a client heard three weeks late, and a production line stood still.
The flow. With xMap Flows, Lea built Lens. Every morning at seven, Lens reads Berlin’s insolvency news for new filings, keeps the ones that supply her clients, writes what each filing means for the client, emails the client from Lea’s inbox, and books a call with them the same day.
The result. On Tuesday, Lens spots that a gear maker in Spandau has filed. It supplies two of Lea’s clients. Both hear from her before nine, and by Friday each has a second supplier lined up. Neither line stops for a day. Lea can’t stop a supplier failing. She makes sure her clients hear it first.
2clients warned before nine
3 wksearlier than last time
0days of lost production
Blocks: Schedule → Web page watch → Match with my table → xMap LLM → Gmail → Google Calendar
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Estate agency
The challenge. Layla is an independent estate agent in Jeddah. She sells villas along the north coast, from Al Shati to Obhur. When a seller calls, she has a day to name a price. She rings other agents and reads listing prices, which run far above what homes really sell for. Last spring she priced one too high, it sat for four months, and the seller left.
The flow. With xMap Flows, Layla built Gauge. When she adds a seller to her sheet, Gauge pulls every villa sale recorded in that district in the last three months, keeps the ones of a similar size, works out the middle price per metre, writes a short note on what the villa should fetch, and sends it to her inbox.
The result. Ten minutes after a seller calls, Layla has the note: eleven villas of that size sold in the district this quarter, at a middle price of about five thousand riyals a metre. She calls back the same afternoon, with real sales to show. Layla no longer guesses a price. She shows the sales that set it.
10 minfrom new seller to price note
11recorded sales behind the price
3months of sales in each note
Blocks: Microsoft Excel 365 event → Regional data sources → Filter → Custom code → xMap LLM → Microsoft Outlook
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