STEP 1
Connect AWS Comprehend and AWS Rekognition
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 AWS Rekognition Integration
Align data between AWS Comprehend and AWS Rekognition to automate ai workflows. Actions made in AWS Comprehend integration can trigger sequences of multiple actions inside AWS Rekognition app - automatically, without any line of code.
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Learn about AWS Comprehend and AWS Rekognition
Detailed info you need to know about AWS Comprehend and AWS Rekognition 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]
AWS Rekognition
Detect faces, labels, text, and moderation content in images with Amazon Rekognition. [6]
How to connect AWS Comprehend to AWS Rekognition
Set up your AWS Comprehend and AWS Rekognition integration in 3 simple steps [2] — no coding required. Up to 10 minutes setup.
STEP 2
Build a workflow
Paste selected AWS Comprehend and AWS Rekognition 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 AWS Rekognition. Use filters or leverage a JS custom code step in between — everything is on the canvas.
How does the integration between AWS Comprehend and AWS Rekognition work in WEXTL?
WEXTL exposes actions and triggers for AWS Comprehend and AWS Rekognition 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.
AWS Comprehend actionsAWS Rekognition actions
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 ClassifiersRetrieves a list of custom document classifiers.
- List Document Classifier SummariesRetrieves a summary of each document classifier name with its version count and latest version.
- 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.
AWS Rekognition modules
- Compare FacesCompare Faces via Amazon AWS.
- Create CollectionCreate Collection via Amazon AWS.
- Detect FacesDetect Faces via Amazon AWS.
- Detect LabelsDetect Labels via Amazon AWS.
- Detect Moderation LabelsDetect Moderation Labels via Amazon AWS.
- Detect Protective EquipmentDetect Protective Equipment via Amazon AWS.
- Detect TextDetect Text via Amazon AWS.
- Face Search By ImageFace Search By Image via Amazon AWS.
- Index FacesIndex Faces via Amazon AWS.
- Make an API CallPerforms an arbitrary authorized API call to the AWS Rekognition endpoint.
- Recognize CelebritiesRecognize Celebrities via Amazon AWS.
Explore alternative integrations
There are other apps [9] supported by WEXTL that can be paired with AWS Comprehend and AWS Rekognition 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 AWS Rekognition.
Connect your AWS Comprehend and AWS Rekognition accounts once, pick an action in one app and an action in the other, then map the fields so data flows automatically between them. [3]
Example: use AWS Comprehend's "Batch Detect Dominant Language" action to perform AWS Rekognition's "Compare Faces" action. AWS Comprehend has 86 actions (including Batch Detect Dominant Language, Batch Detect Entities, and Batch Detect Key Phrases); AWS Rekognition has 11 actions (including Compare Faces, Create Collection, and Detect Faces). Add filters and transforms between steps when you need them. [1]
Yes — pick an action in either app, then an action in the other, and map the fields between them. [2]
No. AWS Comprehend-to-AWS Rekognition 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 AWS Rekognition) 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 + AWS Rekognition automation on a free 3-day trial with every feature unlocked. Cancel anytime — no charge if you decide it's not for you. [8]
References
List of sources used to gather information about the AWS Comprehend and AWS Rekognition integration.
- AWS Comprehend and AWS Rekognition integration source code, module specs and credentials implementation.
- Our AWS Comprehend and AWS Rekognition integration documentation, structured input and output parameters, scopes, and authentication methods.
- Official vendor API documentation for AWS Comprehend and AWS Rekognition — 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
- AWS Rekognition integration information
- WEXTL® Scheduling
- WEXTL® Pricing and Free Trial
- WEXTL® Apps Library