Integrate VLM Run with Attention Insight to automate workflows, sync data between apps, and eliminate repetitive tasks with AI-powered automation.
Image to JSON using VLM Run when New Studies Created in Attention Insight
Convert Audio to JSON using VLM Run when New Studies Created in Attention Insight
Upload File to VLM Run when New Studies Created in Attention Insight
Convert Document To JSON with VLM Run when New Studies Created in Attention Insight
Start from a real workflow other teams are already running.
VLM Run
When this happens...
Attention Insight
Automatically do this!
Get Profile
Initiate Full Attention Analysis
Perform Quick Attention Prediction
Check Study Status
Image to JSON using VLM Run when New Studies Created in Attention Insight
Use this flowConvert Audio to JSON using VLM Run when New Studies Created in Attention Insight
Use this flowUpload File to VLM Run when New Studies Created in Attention Insight
Use this flowConvert Document To JSON with VLM Run when New Studies Created in Attention Insight
Use this flowEverything you can automate between VLM Run and Attention Insight .
When this happensTriggers
A trigger is an event that starts a workflow.
Triggers when a new studies created.
Action is the task that follows automatically within your VLM Run integrations.
Generate structured prediction for the given image.
Generate structured JSON from a document; configure domain, custom JSON schema, or agent skills.
Convert audio files or URLs into structured JSON; configure skills, custom schema, and callback.
Upload a file to VLM Run and return the file metadata including an optional public URL.
This action Provide information about account.
Upload an image to run a full attention analysis (focus, clarity, auto-AOI, contrast) and receive a study ID plus attention heatmap results.

Follow a simple walkthrough to create, test, and launch your first automation.
Connect your apps
Link the apps you want to automate.
Configure your workflow
Set up triggers, actions, and map your data.
Test & publish
Test your workflow and publish it.
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Just describe the task in plain English. viaSocket AI selects the right apps, builds the workflow, maps the fields, and prepares everything for review before you publish.

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VLM Run is a platform designed to enhance your running experience by providing tools and resources for tracking, analyzing, and improving your running performance. Whether you're a beginner or a seasoned marathoner, VLM Run offers features to help you set goals, monitor progress, and connect with a community of runners.
Learn moreAllows you to upload images to Attention Insight platform, perform attention prediction analysis and return attention heatmap image
Learn moreSign up for a free viaSocket account, then authorize both your VLM Run and Attention Insight accounts. From there, pick a trigger in one app and an action in the other. Your first workflow can be live in under five minutes.
Yes. viaSocket uses instant triggers where available, so data moves between VLM Run and Attention Insight as soon as the event happens. Scheduled polling triggers run at a maximum interval of 15 minutes.
Yes. You can map specific fields, apply filters to skip records that do not match your conditions, and transform values before they reach Attention Insight . No coding required.
Yes. You can set up a workflow where VLM Run triggers actions in Attention Insight , and a separate workflow where Attention Insight triggers actions in VLM Run. Both run independently and in real time.
viaSocket logs every run so you can see exactly what succeeded and what failed. Failed tasks can be retried from the dashboard without re-configuring the workflow.
Yes, there is a free plan that covers basic workflows between VLM Run and Attention Insight . Paid plans unlock higher task limits, faster polling, and advanced features like multi-step workflows and conditional logic.
No. The entire VLM Run and Attention Insight integration is built through a visual, point-and-click interface. Code blocks are available if you want them, but they are never required.