STEP 1
Connect AWS Comprehend and VLM Run
Save credentials in WEXTL once. We store them encrypted and reuse the authorization across every workflow [3]. No coding or API skills required.
AWS Comprehend and VLM Run Integration
Build ai workflows between AWS Comprehend and VLM Run on a visual canvas — set up data fields sync between apps once and let data flow when events come automatically. Integration will work without code and your team will see the workflow inside WEXTL® builder.
3-day trialCancel anytimeUp to 10 minutes quick set up
- GDPR
- Data Encryption
- 2FA
- Local Data Region
Learn about AWS Comprehend and VLM Run
Detailed info you need to know about AWS Comprehend and VLM Run integrations in WEXTL. Explore which modules apps support and how you connect them.
AWS Comprehend
Amazon's natural language processing service for finding insights in text. [5]
How to connect AWS Comprehend to VLM Run
Set up your AWS Comprehend and VLM Run integration in 3 simple steps [2] — no coding required. Up to 10 minutes setup.
STEP 2
Build a workflow
Paste selected AWS Comprehend and VLM Run nodes onto the canvas [4], or describe what you want and WEXTL wires the flow together with AI Builder.
STEP 3
Map your data
Choose which fields need to sync between AWS Comprehend and VLM Run. Use filters or leverage a JS custom code step in between — everything is on the canvas.
How does the integration between AWS Comprehend and VLM Run work in WEXTL?
WEXTL exposes actions and triggers for AWS Comprehend and VLM Run that help you connect apps and automate repetitive tasks [1]. A trigger is an event that happens, an action — a step that performs when the event has occurred.
VLM Run triggersAWS Comprehend actions
VLM Run modules
- Analyze an ImageGenerates a structured prediction for the given image.
- Get a CompletionRetrieves the results of parsing or transcribing.
- List FilesRetrieves a list of uploaded files.
- Make an API CallPerforms an arbitrary authorized API call.
- Parse a DocumentGenerates structured predictions for the given document.
- Parse a VideoGenerates structured predictions for the given video.
- Transcribe an AudioGenerates structured prediction for the given audio file.
- Upload a FileUploads a file to VLM.
- Watch Completed PredictionsTriggers when a prediction has been completed.
AWS Comprehend modules
- Batch Detect Dominant LanguageDetects the dominant language of up to 25 texts in one request.
- Batch Detect EntitiesFinds named entities in up to 25 texts in one request.
- Batch Detect Key PhrasesExtracts key noun phrases from up to 25 texts in one request.
- Batch Detect SentimentDetects the overall sentiment of up to 25 texts in one request.
- Batch Detect SyntaxTags the part of speech of every word in up to 25 texts in one request.
- Batch Detect Targeted SentimentDetects the sentiment about each entity mentioned in up to 25 texts in one request.
- Classify DocumentClassifies a text or document in real time with a custom classifier endpoint.
- Contains PII EntitiesChecks whether a text contains personal information and lists the kinds found.
- Create DatasetAdds a training or test dataset from Amazon S3 to a flywheel.
- Create Document ClassifierCreates and starts training a custom document classifier from labeled data in Amazon S3.
- Create EndpointCreates a real-time endpoint for a trained custom classifier or entity recognizer.
- Create Entity RecognizerCreates and starts training a custom entity recognizer from annotated data in Amazon S3.
- Create FlywheelCreates a flywheel that manages training and versions of a custom model.
- Delete Document ClassifierDeletes a trained or failed document classifier.
- Delete EndpointDeletes a custom model endpoint and stops its billing.
- Delete Entity RecognizerDeletes a trained or failed entity recognizer.
- Delete FlywheelDeletes a flywheel without deleting its data lake or trained models.
- Delete Resource PolicyRemoves the resource-based policy from a custom model.
- Describe DatasetReturns the status and details of a flywheel dataset.
- Describe Document Classification JobReturns the status and settings of a document classification job.
- Describe Document ClassifierReturns the training status, settings, and accuracy metrics of a document classifier.
- Describe Dominant Language Detection JobReturns the status and settings of a dominant language detection job.
- Describe EndpointReturns the status, model, and capacity of a custom model endpoint.
- Describe Entities Detection JobReturns the status and settings of an entities detection job.
- Describe Entity RecognizerReturns the training status, settings, and accuracy metrics of an entity recognizer.
- Describe Events Detection JobReturns the status and settings of an events detection job.
- Describe FlywheelReturns the status, active model, and settings of a flywheel.
- Describe Flywheel IterationReturns the status and model metrics of one flywheel iteration.
- Describe Key Phrases Detection JobReturns the status and settings of a key phrases detection job.
- Describe PII Entities Detection JobReturns the status and settings of a PII entities detection job.
- Describe Resource PolicyReturns the resource-based policy attached to a custom model.
- Describe Sentiment Detection JobReturns the status and settings of a sentiment detection job.
- Describe Targeted Sentiment Detection JobReturns the status and settings of a targeted sentiment detection job.
- Describe Topics Detection JobReturns the status and settings of a topics detection job.
- Detect Dominant LanguageDetects the languages a text is written in, with a confidence score for each.
- Detect EntitiesFinds named entities such as people, places, and organizations in a text or document.
- Detect Key PhrasesExtracts the key noun phrases from a text.
- Detect PII EntitiesFinds personal information in a text and returns its type and position.
- Detect SentimentDetects whether a text is positive, negative, neutral, or mixed.
- Detect SyntaxSplits a text into words and tags each word with its part of speech.
- Detect Targeted SentimentDetects the sentiment expressed about each entity mentioned in a text.
- Detect Toxic ContentScores short text segments for toxic content such as insults, profanity, and hate speech.
- Import ModelCreates a copy of a custom model shared from another AWS account.
- List DatasetsRetrieves a list of flywheel datasets.
- List Document Classification JobsRetrieves a list of document classification jobs.
- List Document Classifier SummariesRetrieves a summary of each document classifier name with its version count and latest version.
- List Document ClassifiersRetrieves a list of custom document classifiers.
- List Dominant Language Detection JobsRetrieves a list of dominant language detection jobs.
- List EndpointsRetrieves a list of custom model endpoints.
- List Entities Detection JobsRetrieves a list of entities detection jobs.
Explore alternative integrations
There are other apps [9] supported by WEXTL that can be paired with AWS Comprehend and VLM Run integrations. Pick any from the list below if the current combination isn't the one you need.
Frequently asked questions
Common questions about connecting AWS Comprehend and VLM Run.
Connect your AWS Comprehend and VLM Run accounts once, pick a trigger in one app and an action in the other, then map the fields so data flows automatically between them. [3]
Example: when VLM Run's "Watch Completed Predictions" fires, WEXTL can "Batch Detect Dominant Language" in AWS Comprehend. AWS Comprehend has 86 actions (including Batch Detect Dominant Language, Batch Detect Entities, and Batch Detect Key Phrases); VLM Run has 8 actions (including Analyze an Image, Get a Completion, and List Files). Add filters and transforms between steps when you need them. [1]
Yes — the integration works in both directions. Use each app's triggers to start a workflow and the other app's actions to update it. [2]
No. AWS Comprehend-to-VLM Run automations are built visually on the canvas. You can optionally add custom JavaScript with Functions when you need advanced logic. [4]
Yes. Trigger-based automations start the moment AWS Comprehend (or VLM Run) fires a new event — webhook-backed apps run within seconds, and polling-only apps re-check on a schedule you control. [7]
A first automation typically takes 5–10 minutes: connect each app's credential once, drop a trigger + action onto the canvas, map the fields, and turn the workflow on.
You can build your AWS Comprehend + VLM Run automation on a 3-day trial with every feature unlocked. Cancel anytime. For your protection, a small deposit is charged to your card when your trial starts. It counts toward your first payment and is non-refundable. [8]
References
List of sources used to gather information about the AWS Comprehend and VLM Run integration.
- AWS Comprehend and VLM Run integration source code, module specs and credentials implementation.
- Our AWS Comprehend and VLM Run integration documentation, structured input and output parameters, scopes, and authentication methods.
- Official vendor API documentation for AWS Comprehend and VLM Run — used to verify field meanings, authentication flows, and anything our own specs don't fully capture.
- WEXTL® visual workflow builder, anonymized data from our technical support team.
- AWS Comprehend integration information
- VLM Run integration information
- WEXTL® Scheduling
- WEXTL® Pricing and Trial
- WEXTL® Apps Library