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Dec 08, 2023 11:18 AM
We review a variety of documents for client. Such as bylaws, legal governance documents, and financial statements.
Currently I have a base and a form set up. The form allows a client to submit the documents they would like to have reviewed and provide their client information. Once that is submitted the base is populated with the documents broken down by category, the clients name and contact, and a unique ID for the batch of documents.
At this point we vet the documents to confirm they are the right ones start to extract the information that we need. Data can be in the form of select phrases within a set of bylaws, critical dates of documents expiring or when they were registered, and the financial details of a document. Once this is done we create a summary of our findings and present it back to the client. This is largely done manually at this point with a few automations. We want to start fully automating this process and use Airtable to store our data. But we are struggling to develop an efficient architecture for our database. Either because of limitations in Airtable or our not being aware of how to properly use Airtable.
Given the following:
A client can have many document reviews
A set of documents include written and financial data
Documents can exist in single or multiples : Ie, Bylaws there are only one of per review but financial statements are many
A set of documents submitted apply to one entity exclusively
How would you set up and organize your Base/Bases?
Dec 08, 2023 03:27 PM
HI Hitch,
Setting up an efficient Airtable base for your document review process involves designing a structure that accommodates the relationships between clients, document reviews, and the types of documents involved. Here's what we would recommend:
Tables:
Clients Table:
Document Reviews Table:
Documents Table:
Connections and Relationships:
Connect Clients and Document Reviews:
Connect Document Reviews and Documents:
Automation:
Views:
Sample Workflow:
Form Submission:
Document Upload and Extraction:
Data Review:
Summary Generation:
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