Make.com x PandaDoc x Monday.com help
Hi! I’m trying to create a scenario in make.com that populates a table into a Pandadoc Proposal template from Monday.com subitems.
I have everything running correctly as far as the connection and trigger of the scenario is concerned I’m even as far along as the proposal is created and populates all the data I need, except the table only pulls in one subitems data instead of all subitems (the amount of subitems for each proposal varies)
Has anyone built a scenario with this need before? I need some expert help/guidance!
The short answer is:
What is the key to mapping multiple monday.com subitems into a single PandaDoc table?
Your issue is almost certainly a data structure problem where the multiple subitems you retrieve from monday.com are not being correctly aggregated into a single, cohesive array that PandaDoc expects for populating a table or quote block.
In Make.com, the solution requires a specific workflow: first, use a “Search Items” module (or similar) to get all subitems for the main item; second, use the “Iterator” module to break that list into individual bundles; and most importantly, third, use the “Array Aggregator” module to reassemble those individual subitem bundles into the exact JSON array format that the PandaDoc API documentation specifies for table data.
The long answer is:
The PandaDoc API expects table data to be sent in a precise format, which is an array of objects or an array of arrays, depending on whether you are using their standard table variable mapping or a custom API call for a Quote Block.
When you retrieve your subitems from monday.com using Make’s modules, the data comes through as separate bundles or an unstructured list of all column values combined, which is why your scenario is only mapping the first one.
To fix this in Make.com, you need to follow a crucial sequence of modules:
First, your initial module should retrieve the main item and then find its subitems.
This step provides the raw data.
Second, you must use the “Iterator” tool to ensure that each subitem’s data is treated as its own distinct bundle of information.
Third, and this is the most critical step, you must pass the output of the Iterator into an “Array Aggregator” module.
In the Array Aggregator’s settings, you need to manually define the structure of the outputted array to match the precise JSON format PandaDoc requires for populating tables, using keys like “name,” “description,” and “price.” You will map the corresponding subitem column values from the Iterator (e.g., Subitem Name, Subitem Price Column Value) to these keys inside the Aggregator.
This Array Aggregator module takes all the individual subitem bundles and spits out a single JSON array that can be mapped directly into the PandaDoc “Create a Document” module, usually into a field that accepts a collection or array of items.
Regarding a custom solution, your question hints at an appreciation for powerful automation, and while Make.com can solve this, for high volume or highly complex data transformation needs, a custom server-side solution is still the ultimate choice.
An architecture using monday.com’s Platform API to retrieve the subitems, and then an intermediate layer like Google Tag Manager Server, Stape, or a simple GCP Cloud Function, would allow you to write a clean Javascript or Python script to handle the data transformation.
This script can easily map the array of subitem column values into the required PandaDoc JSON structure and then call the PandaDoc API’s “Create Document” endpoint.
This approach is cheap, extremely fast, and completely bypasses the limitations or complexities of visual array mapping within no-code tools, giving you complete control over the final output structure.
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