Document data extraction via AI mcp vs scripting vs ai fields
Hi all,
We are exploring a pipeline to have teams submit a word document and a Excel table via a form. The word doc is only about 7 or so pages usually and the Excel has about 400 rows and 5 columns of data.
The expected behavior is that the data entered in specific fields within the documents are to be populated across multiple tables, creating new records where applicable.
I would be curious to hear people's experiences with this, including what processes and tools you have considered, if you had to make a choice and how you juggled tradeoffs.
We have access to Claude code and there is openess to having the submission analyzed in Claude if it improves accuracy and changes pushed through via the mcp. My take is that there are various ways to tackle this kind of problem via AI scripts, ai fields within the base, and Claude code/mcp server but just not familiar with all the detailed tradeoffs to come to a confident decision.
Edit: Thanks all for your guidance! The Word document has fields where expected values are meant to go, but I can't ever guarantee someone will use it in the way that it was designed.The Excel table has about 5 columns, each with drop down options for field validation and a numeric field for the values that the user will enter, so would say it is structured. We have been shying away from interfaces mainly because of the seat cost in getting other teams within our organization to collaborate and use Airtable. I am also exploring Softr and Noloco as possible ways to reduce cost but also wanted to see if we could use what is available in our available tech stack.
I'll do some more research into the recommendation for parsing, as flag the cost variability associated with an AI solution that others on the team have pushed for- appreciate the ideas y'all have shared. Thank you!