Cool rooms open where Paris’s elders live

The challenge

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.

How the flow works

With xMap Flows, Camille built Canopy. Canopy is an AI employee: a flow of 8 blocks.

1. Every summer morning at six,

   Summer mornings at 6 (Schedule)

2. for each of eighty cool rooms,

   Eighty cool rooms (My data)

3. Canopy reads the next three days’ forecast,

   Next three days (Weather forecast)

4. keeps the ones where heat is coming,

   Heat coming (Filter)

5. counts the older people living around each,

   People over sixty nearby (Demographics)

6. picks the twenty that matter most,

   Top twenty rooms (Sort and limit)

7. writes each opening notice,

   Opening notice (xMap LLM)

8. and sends it to the district halls.

   To the district halls (Microsoft Outlook)

What it solves

• Two days of calls: solved by Schedule and xMap LLM and Microsoft Outlook
• Opened too late: solved by Weather forecast and Filter
• Wrong neighbourhoods: solved by Demographics and Sort and limit

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.

An illustrative scenario: the figures are modelled to show what the flow produces, not a customer's results.

The flow, block by block

1. 1

   Summer mornings at 6

   Schedule

   Starts the flow on a timetable.

2. 2

   Eighty cool rooms

   My data

   Brings in a table you uploaded or a list you saved on the My data page: your stores, customers, target accounts or sites.

3. 3

   Next three days

   Weather forecast

   The weather forecast (Open-Meteo, from national weather services) for up to 16 days: temperature, rain, wind and UV.

4. 4

   Heat coming

   Filter

   Keeps only the items that match a rule.

5. 5

   People over sixty nearby

   Demographics

   Who lives in an area: the number of residents, men and women, and nine age groups, from xMap’s population grid (hexagons of about 0.1 km², about 100 countries).

6. 6

   Top twenty rooms

   Sort and limit

   Puts the items in order, and can keep only the first ones: the 10 best rated, the 20 nearest, the highest fit scores.

7. 7

   Opening notice

   xMap LLM

   Tell the AI what to do in plain words.

8. 8

   To the district halls

   Microsoft Outlook · Outlook

   Runs an action in Microsoft Outlook.

Templates to build it

Ready-made flows with the same blocks. Open one, change the city and the kind of place, and run it.

• Where growers need a cold storeGrower areas with the fewest cold stores, scored for a packhouse, approved.
• High UV days: advice for my clinics' patientsOn high UV days, a short sun safety tip for each clinic's patients.
• Heavy rain at our facilities: spill prevention checklistA spill prevention checklist for each site when heavy rain is coming.
• Heat at my plants: shift and break planTomorrow’s heat at each plant, with a shift and break plan.
• Store rollout: weather at each site this weekThe week’s weather at each rollout store, riskiest first.
• Frost and heat warnings for my pitchesFrost and heat warnings for every pitch, the evening before.

Industries

Learn how it works

• How to score candidate store sitesCompare sites on residents, competitors and neighbours, with an AI score and a reason.
• Location intelligenceUsing where things are, and what is around them, to make business decisions.
• Site selectionChoosing where to open a store, branch, clinic or facility.
• Build your first flow in ten minutesFrom a blank page to a flow that emails you a ranked list every morning.
• Ask the map a questionHow many of a kind of place, how many people for each, and where the gaps are, answered on the spot.
• Enrich a list onceA CSV of addresses or companies back with phone, website, rating and an AI score.

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