The Scenario
You do supply-chain planning for a distribution company with 60 warehouse locations spread across six western states. Every quarter you submit a risk-scoring report to the VP of operations that includes fire weather index forecasts for each site—four weeks of weekly FWI scores that feed into the insurance and contingency planning models. The Q2 report is due Friday. The data isn't in the sheet yet.
The bad version:
- Find Ambee's fire risk forecast endpoint, read the documentation for the weekly FWI output format, build an API call with your location and the forecast horizon parameter.
- Run it for warehouse 1, copy the four weekly risk scores, open the sheet, paste them into columns B through E, change the coordinates, run warehouse 2.
- Hit rate limiting at warehouse 23, wait 60 seconds, resume, lose your place in the list, realize you've been populating row 25 with data from warehouse 24 for the last several iterations.
This report exists because someone decided fire risk belongs in the quarterly model. That decision was correct. But the data pipeline to fill 60 rows with 4-week forecasts was apparently left as an exercise for whoever inherited the spreadsheet.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that runs inside your Google Sheet. It reads your location list, calls Ambee's fire risk forecast API for each site, and writes the weekly FWI scores into the columns you specify—without you managing rate limits or tracking which row you're on.
Here is the prompt for this task:
For every location in column A, fetch the 4-week fire risk forecast (Fire Weather Index) from Ambee and paste the weekly risk scores into columns B–E
What You Get
- Column B: Week 1 FWI score
- Column C: Week 2 FWI score
- Column D: Week 3 FWI score
- Column E: Week 4 FWI score
- Rows where Ambee cannot resolve the location return "No data" across B–E
What If the Data Is Not Quite Ready
The warehouse list uses facility codes rather than place names or coordinates
Column A has internal facility codes (e.g., "WH-042"). Column B has the corresponding city and state. Use the city and state in column B to fetch the 4-week FWI forecast from Ambee and write weekly scores into columns C–F, keeping the facility code in column A intact.
The quarterly model needs FWI scores classified as Low / Moderate / High / Extreme rather than raw numbers
For each location in column A, fetch the 4-week FWI forecast from Ambee, convert each weekly score to a risk classification (Low <15, Moderate 15–35, High 35–50, Extreme >50), and write the classified labels into columns B–E instead of raw scores.
The report requires a composite 4-week average risk score per location
Fetch the 4-week FWI forecast from Ambee for each location in column A, write the four weekly scores into columns B–E, then compute the average of B–E and write it as a single "4-Week Avg FWI" value into column F.
Kill-chain: fetch forecasts, classify risk, flag sites above threshold, generate executive summary tab
For every location in column A, fetch the 4-week fire risk forecast from Ambee, write raw FWI scores into columns B–E, compute the average in column F, classify it as Low / Moderate / High / Extreme in column G, flag any site with an Extreme rating with "ESCALATE" in column H, then create a tab called "Exec Summary" listing only flagged sites with their location, average FWI, and classification.
One prompt covers the full pipeline—data fetch, classification, flagging, and summary output.
Try It
Open the quarterly risk workbook in Google Sheets and get the 7-day free trial of SheetXAI. Ask it to pull 4-week FWI forecasts from Ambee for every warehouse in column A and classify them by risk tier, and your VP of operations has the report data without a single API call on your end. Also useful: checking active wildfire status for coordinates or the full Ambee integration overview.