We used and evaluated Tray.io for approximately three to six months during a proof of concept evaluation phase. During this period, our engineering and operation teams utilized the platform to build high-volume data integration pipelines, specifically syncing student enrollment data between our student information system and the LMS system that we have. While we ultimately decided to consolidate our day-to-day automation needs on Make due to its lower barrier to entry and visual ease for non-developers, our time with Tray.io gave us a solid understanding of its enterprise-grade capabilities and structure. Our primary use case during our evaluation of Tray.io was automated student roster management and enrollment synchronization. Specifically, we needed to ensure that when a student registers for a course on our platform, their profile gets updated, and course access to the particular subject is also done across our internal database as well as our LMS systems. A specific example would be that we set up a workflow which would handle batch updates between our core student database that was running on Supabase in Postgres and our LMS system, which was using a version of Canvas during that time. The workflow operated as follows: when a dual trigger is queried, our database is queried at a specific time at night, maybe around 12:00 a.m. or 1:00 a.m., to fetch all the new student registrations and course changes that were done in the last 24 hours. Then, Tray.io received the data as a nested JSON payload. Using the Tray loop helper, the workflow iterated through each student record to map a field in each specific column for student ID, email, course name, and role based on the format required in the LMS API. The logic that was built handled different user roles; if a record indicated a teaching assistant, the workflow sent them specific permission in the LMS, and if it was a standard student, they would be assigned the standard access. The workflow sent formatted data to the LMS API so that we could create or update enrollments. If an API call failed due to any issues such as invalid email format, Tray.io's error handling branch caught the failure, isolated this specific record, and sent us a notification in our Slack channels with the error details, allowing us to manually fix it while the rest of the batch could sync without any interruption.
My main use case for Tray.io is webhook functionality. We send webhooks from our application to Tray.io and then run workflows from Tray.io to our different use cases to send data to multiple other applications.
Principal AI and Data Science Engineer at a manufacturing company with 10,001+ employees
Real User
Top 10
Mar 18, 2026
My main use case for Tray.io is to conduct A/B testing for marketing initiatives that the company has undertaken. We test the deployment of different campaigns across similar cohorts and evaluate which one performs better. Tray.io fits into my A/B testing process by analyzing the number of words used by consumers in comments and the number of times they stopped campaign videos at specific points. Through this analysis, we can investigate the attention levels of users and determine what thoughts are elicited by the campaign. I have found that the error management in my main use case with Tray.io is not as effective as we would prefer. We would appreciate having a way to recycle cases that do not carry much value. Every user is precious in their own way, and even if a user does not provide much information, we would still value the ability to extract some information from those boundary cases.
Operations Analyst at a tech vendor with 51-200 employees
Real User
Top 20
Mar 12, 2026
We run automation workflows with Tray.io to take data from our internal databases and update a third-party software that we use. Specifically, my company offers digital trade credit, and we use that software to support our collections processes. At a high level, our systems call a webhook in Tray.io. The workflows in Tray.io then triage and process the incoming data, and finally makes API calls to the third-party service to modify objects.
Tray.io is an advanced integration platform that allows seamless connectivity between applications, designed to automate workflows and streamline business processes.
Tray.io provides an extensive library of pre-built connectors and powerful automation tools, making it easy for businesses to boost efficiency. Its drag-and-drop workflow builder enables integration without code, catering to both technical and non-technical users. Being highly customizable and scalable, Tray.io helps...
We used and evaluated Tray.io for approximately three to six months during a proof of concept evaluation phase. During this period, our engineering and operation teams utilized the platform to build high-volume data integration pipelines, specifically syncing student enrollment data between our student information system and the LMS system that we have. While we ultimately decided to consolidate our day-to-day automation needs on Make due to its lower barrier to entry and visual ease for non-developers, our time with Tray.io gave us a solid understanding of its enterprise-grade capabilities and structure. Our primary use case during our evaluation of Tray.io was automated student roster management and enrollment synchronization. Specifically, we needed to ensure that when a student registers for a course on our platform, their profile gets updated, and course access to the particular subject is also done across our internal database as well as our LMS systems. A specific example would be that we set up a workflow which would handle batch updates between our core student database that was running on Supabase in Postgres and our LMS system, which was using a version of Canvas during that time. The workflow operated as follows: when a dual trigger is queried, our database is queried at a specific time at night, maybe around 12:00 a.m. or 1:00 a.m., to fetch all the new student registrations and course changes that were done in the last 24 hours. Then, Tray.io received the data as a nested JSON payload. Using the Tray loop helper, the workflow iterated through each student record to map a field in each specific column for student ID, email, course name, and role based on the format required in the LMS API. The logic that was built handled different user roles; if a record indicated a teaching assistant, the workflow sent them specific permission in the LMS, and if it was a standard student, they would be assigned the standard access. The workflow sent formatted data to the LMS API so that we could create or update enrollments. If an API call failed due to any issues such as invalid email format, Tray.io's error handling branch caught the failure, isolated this specific record, and sent us a notification in our Slack channels with the error details, allowing us to manually fix it while the rest of the batch could sync without any interruption.
My main use case for Tray.io is webhook functionality. We send webhooks from our application to Tray.io and then run workflows from Tray.io to our different use cases to send data to multiple other applications.
My main use case for Tray.io is to conduct A/B testing for marketing initiatives that the company has undertaken. We test the deployment of different campaigns across similar cohorts and evaluate which one performs better. Tray.io fits into my A/B testing process by analyzing the number of words used by consumers in comments and the number of times they stopped campaign videos at specific points. Through this analysis, we can investigate the attention levels of users and determine what thoughts are elicited by the campaign. I have found that the error management in my main use case with Tray.io is not as effective as we would prefer. We would appreciate having a way to recycle cases that do not carry much value. Every user is precious in their own way, and even if a user does not provide much information, we would still value the ability to extract some information from those boundary cases.
We run automation workflows with Tray.io to take data from our internal databases and update a third-party software that we use. Specifically, my company offers digital trade credit, and we use that software to support our collections processes. At a high level, our systems call a webhook in Tray.io. The workflows in Tray.io then triage and process the incoming data, and finally makes API calls to the third-party service to modify objects.