Corrently Integration
Connect Corrently to Arahi's no-code AI analysts and automate workflows in minutes.
About the Corrently integration
Electricity tariff with a loyalty program allowing every electricity customer to do micro investments into renewable energy generation facilities With Arahi AI, you can connect Corrently to Baremetrics, Datawaves, Google Analytics and 1,500+ other apps using AI-powered workflows. No coding required — set up triggers, actions, and intelligent automation in minutes.
- Category
- Analytics & Reporting
- Authentication
- keys
Corrently automation use cases
- Anomaly detection alerts
- Watch Corrently metrics for deviations from baseline and post to Slack when a threshold is breached.
- Exec dashboard digest
- Pull weekly KPIs from Corrently and deliver a one-page email to leadership every Monday.
- Funnel step drop-off alerts
- Alert product team the moment a Corrently funnel step drops more than 10% week-over-week.
- Cohort retention sync
- Export Corrently cohort retention curves to Google Sheets or Notion for monthly board deck prep.
Corrently workflow examples
- Push CO2 Meter Update to Google Sheets
Run CO2 Meter Update on Corrently every hour and append the returned carbon intensity values as rows in a Google Sheets sustainability workbook.
Hourly CO2 Meter Update call; Google Sheets row appended.
- Post PV Generation Forecast to Slack
Call PV Generation Forecast each morning for tracked sites and post the day-ahead solar output estimate to a Slack operations channel.
PV Generation Forecast runs; Slack posts forecast summary.
- Store forecasts in Airtable site records
Combine CO2 Meter Update and PV Generation Forecast results from Corrently and upsert values onto an Airtable table of installation sites.
Corrently data returned; Airtable site records updated.
Corrently actions
- CO₂ Meter Update
- Tool to create or update a co₂ meter reading for emissions tracking. use when sending new or updated electricity consumption readings to corrently.
- PV Generation Forecast
- Tool to get solar energy production forecasts (hourly output and loss estimates) for a specific location. use when you need hourly pv generation data to optimize energy scheduling.