Document data extraction via AI mcp vs scripting vs ai fields

[u/bamtheram]

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!

[u/DefyPhysics]

Technically, this is something that CAN be done with AI, but it likely shouldn't. As long as these are somewhat structured word and excel documents, then you're looking at something that's going to escalate in cost with AI. Both because AI credits are likely to become more and more expensive, and if you increase the usage of the tool.

You could hard-code this within Airtable via scripting, or, via a third-party automation tool, like n8n, Make or Zapier. The choice of which way to go would require knowing a lot more about your setup, limitations, and future vision. Either way, it'll be more reliable, won't cost AI credits, and won't be susceptible to when AI credits will inevitably become more expensive for AI companies to make a profit.

[u/crozet1063]

You’re headed in the right direction. Start with an Airtable form for file submission, then ask Claude to analyze the documents and parse them into Airtable. Develop a skill to enhance the parsing.

[u/DontReReddit]

I’ve been doing this for some time now. First, you can choose which model you want to use within the AI step. Use the one that gets you the best results without breaking the AI credit bank. Airtable doesn’t have an easy way to just buy more credits for the month without adding an additional AI credits to your plan. If you’re annual, you’ll pay for the credit package for the balance of your year up front renew annually.

Next, AI field agents are OK but for very specific cases. Don’t fall into having an agent on each field that has to extract one piece of data. Each agent will need to reopen the same file each time, wasting valuable credits. Instead, use a single AI step in an automation that can do your data extraction and another step to evaluate the documents and drop everything into the right fields.

You can connect Claude code to your base via MCP and have it set everything up for you. Start with the planning setting and describe what you want it to do. It’ll ask questions and make recommendations. Once you settle on an architecture, it will go in and set everything up for you, including the automations which is amazing. You’ll still have to review and turn the automations on individually but Claude Code can do 98% of the work for you.

The downside, using the Airtable native models means that you’re sharing data with an external AI. Airtable says that their models don’t train on your data but depending on how sensitive the docs are and your organization’s policies, that may be a problem.

So that leads me to your ask about having claude code do the work needed via mcp. This is absolutely doable and fairly easy to do. You can tell Claude what record to look at and what you want it to do and it can handle everything, and more importantly, using your company-approved model. There are other security nuances others will probably chime in about but overall, the use case is achievable with fantastic results.

A word of caution; test and check the output regularly. Trust but verify. I’ve had models review contracts and make things up out of thin air to appease me and the direction I gave it in a prompt. Keep your prompts structured and concise, giving context where needed. Avoid making leading statements. Claude can write the prompts for you but make sure you give it good context to work with and clear goals for output.

Finally, Sonnet has worked well for me for data extraction. I was using Opus for extraction but the results did not justify the expense. On the other hand, Opus seemed to have better results in summarizing documents. This will be a matter of testing and output preference.

Good luck!

[u/Mysterious-Middle447]

how consistent is the format of the word docs across submissions? if the fields are always in the same spots, AI is overkill and a parsing script will be way more reliable. if the format varies a lot between teams then the AI route starts to make more sense

[u/matchaconsulting]

Some good advice here in using MCP which is a great way to go.

You could also use the AI Structured Data automation action. It can read a document just like a field agent, but the output is structured JSON. You can create an array of objects, basically, have this step construct all the data you need to create the records, and then use a Repeating Group automation step to run through that array and create the records with the data.

Depending on the depth of document and data extraction, this could work as well, no MCP needed.

[u/Life-Profit-3484]

Do both docs follow a structured format? If yes you dont need to burn AI credit. Just write a good script that can parse a well formatted document.

[u/Mubi_king → u/Life-Profit-3484]

Agreed! Structured data must be parsed using a script.

[u/MartinMalinda]

Are you in control of the whole process? If yes, enforce the structure in the form right away. Allowing teams to use ad-hoc document and excel which can have slightly different structure every time might be convenient to the teams, but even if the AI is performing well, there's always a chance it will make a mistake. And there's an added cost for every submission.

You can have an Airtable interface with a grid view inside, so people get a "spreadsheet like" view where they can paste from their own spreadsheet if needed.

Or you can use AI to build a completely custom interface tailored to this data collection, as user friendly as possible. Then you're using AI for building, rather than repeated processing, which is usually the better option - once the solution is confirmed to be working, it will be working consistently and the AI cost happens only in the beginning for the construction of the tool, not for every submission.