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Onboarding on Circla.ai

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SOP for Getting Started with

Objective

This SOP outlines the steps to sign up and create a model using Circla.ai, ensuring a smooth onboarding process for new users.

Key Steps

Step 1: Sign Up for Circla.ai
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Go to the app.circla.ai website.
Choose to sign in with your Google account or enter your email address.
Example email: robert@minviro.com.
Click on 'Send Login Code'.
Step 2: Verify Your Email
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Check your email for the login code.
Ensure to check your spam folder if you do not see it in your inbox.
Enter the code to verify and sign in.
Step 3: Start Your Free Trial
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After signing in, you will see a subscription prompt.
Click on 'Subscribe Now' to start a 14-day free trial.
No credit card details are required.
Step 4: Create a Model
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Click on 'Create Model' to begin.
Input relevant information for your model, such as:
Description
Geography
Level and scope
Functional unit (e.g., 1 litre bottle)
System boundary details.
Step 5: Generate LCI
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After creating your model, click on 'Generate LCI'.
The AI will process the information and take approximately 5 minutes.
Step 6: Review the Model
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Once the AI has completed its processing, review the model for:
Process breakdown stages
Inventory tables
Linked references and sources.
Step 7: Quality Assurance and Quality Control (QAQC)
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Check the QAQC section for:
Mass and energy balance checks.
Ensure all unit processes are connected logically.
Step 8: Edit and Download the Model
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You can make edits to the model at any time.
To download, click on 'Download' to get a pre-formatted spreadsheet.
This spreadsheet can be directly imported into Cycle as a CSV.

Cautionary Notes

Always check your spam folder for the login code if you do not receive it in your inbox.
Ensure that all information entered in the model is accurate to maintain data integrity.

Tips for Efficiency

Familiarize yourself with the app.circla.ai interface to speed up the model creation process.
Use clear and concise descriptions for each input to avoid confusion later on.
Regularly save your progress while creating models to prevent data loss.



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