Workflows Available in Relevance AI

Relevance AI offers a variety of very useful analyses that are all accessible under Workflows on the navigation bar. Our workflows are prepared in a no-code format, meaning you do not need any programming skills to be able to use them. Alternatively, if you are into Python programming, you can use our Python SDK.

Below are the top workflows available in Relevance AI.

All Data Types

Vectorize

Vectorize your data with a ready-to-use neural network, and benefit from the magic of AI and machine learning.

Benefits
1. More precise data analysis
2. Fast processing of large amounts of data
3. Easy access to a variety of state-of-the-art vectorizing models
Top Use Cases
Semantic search
Clustering
Recommendation system
Start Analyzing
1. Upload your dataset to Relevance AI
2. Choose the model and the fields you want to encode
3. Run Vectorize workflow

Clustering

Cluster

Group/cluster your data based on the content similarity, and discover hidden patterns using AI and machine learning.

Benefits
1. Identify hidden patterns in your data using AI algorithms
2. Understand your data better
3. Fast and more informed decision making
Top Use Cases
Analysing top trends in reviews
Understanding different categories in costumer feedback (issues, requests, admiration)
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Vectorize the field in your data to be analyzed
3. Run Clustering workflow

Cluster Auto

Group/cluster your data based on the content similarity, and discover hidden patterns using AI and machine learning. The number of groups/categories is decided automatically.

Benefit
1. Identify hidden patterns in your data using AI algorithms
2. Understand your data better
3. Fast and more informed decision making
4. Does not require you to specify the number of clusters in advance
Top Use Cases
Analysing top trends in reviews
Understanding different categories in costumer feedback (issues, requests, admiration)
When there is no good estimate of the number of clusters
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Vectorize the field in your data to be analyzed
3. Run Auto-clustering workflow

Cluster Hybrid

Hybrid-clustering workflow combined several clustering methods for grouping data.

Benefit
1. Identify hidden patterns in your data using AI algorithms
2. Understand your data better
3. Fast and more informed decision making
4. Combines different clustering methods
Top Use Cases
Analysing top trends in reviews
Understanding different categories in costumer feedback (issues, requests, admiration)
When there is no good estimate of the number of clusters
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Vectorize the field in your data to be analyzed
3. Run Hybrid-clustering workflow

Subcluster

Subcluster existing clusters/categories into smaller ones for a higher level of granularity.

Benefits
1. More fine-grained analysis of the data
2. Extract all the hidden information about your data
Top Use Cases
Understanding the inner categories of a category (e.g. for a category focused on water leakage, subcategories could be "leakage in the kitchen" or "leakage in the balcony")
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Vectorize the field in your data to be analyzed
3. Run clustering workflow
4. Run Subcluster workflow

Reduce Dimensions

Reduce the size of vectors associated with data entries.

Benefits
1. Reduce the amount of time and memory needed to work with vector analysis
2. Possibility to explore data in a 3D space
Top Use Cases
2D or 3D data representation
Increase the efficiency of using resources (memory, computation)
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Vectorize the field in your data to be used for analysis or visualization
3. Run dimensionality reduction workflow on the vectors

Text Data

Extract Sentiment

Perform sentiment analysis on your textual data using state-of-the-art AI and machine learning to discover text polarity (i.e. positive, negative, neutral).

Benefits
1. Understand your customer better
2. Identify areas of strong/weak points
Top Use Cases
Understanding clients better
Understanding how staff feel about different aspects of work
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run sentiment analysis workflow

Extract Emotion

Perform emotion analysis on your textual data using state-of-the-art AI and machine learning to discover different underlying emotions in your data (i.e. anger, satisfaction, happiness, disapproval, etc.)

Benefits
1. Understand your customer better
2. Identify areas of strong/weak points
3. More fine-grained analysis compared to sentiment identification
Top Use Cases
Understanding clients better
Understanding how staff feel about different aspects of work
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run emotion analysis workflow

Split to Sentences

Use AI to split large chunks of texts to their composing sentences.

Benefit
The ability to analyse all points mentioned in comments and reviews separately
Top Use Cases
Understand the comments better
fine grained analysis of the action points
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the split to sentence workflow

Extract Nouns

Use machine learning to extract nouns from text and build a taxonomy with the results.

Benefits
1. Build a taxonomy from extracted nouns
2. Use the taxonomy to better understand your data
3. Data summarisation using the taxonomy
Top Use Cases
Understand the data better by focusing on the top existing nouns
Fast data summarisation using the extracted taxonomy
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the Extract nouns workflow

Tagging

Generate AI Tags

Use AI to automatically generate AI tags. This includes tag/code frame extraction and labelling the dataset with the tags.

Benefits
1. Use AI to automatically generate conceptual keywords/phrases (code frames) for your data 2. Use the code frames to better understand your data
3. Data categorisation using the code frames
Top Use Cases
Understand the data better by extracting the top code frames
Date categorisation relying on automatically extracted and conceptual key phrases
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the generate AI tags workflow

Guided Tagging

Guided tagging workflow enables you to modify (add to/ remove from) previously extracted tags before tagging the a dataset.

Benefits
1. Capability of modifying the automatically extracted code frame 2.Use AI to automatically assign conceptual keywords/phrases (code frames) for your data 3. Use the code frames to better understand your data
4. Data categorisation using the code frames
Top Use Cases
Understand the data better by extracting the top code frames
Date categorisation relying on automatically extracted and conceptual key phrases
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the generate AI tags workflow
3. Run Guided tagging workflow which allows you to modify the extracted code frames if necessary and then apply them to the dataset

Sub-Tagging

Sub tagging allows you to perform a more fine-grained analysis on a large category. Sub-tagging breaks a parent category to a defined set of child categories.

Benefit
1. More fine-grained analysis of the data
2. Controlled parent to child categorization
Top Use Cases
Analysing client feedback and when extracted categories need to be broken into their composing categories
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run a tagging workflow (e.g. generate AI tags workflow )
3. Run Sub tagging workflow.

Guided Sub-Tagging

Benefit
Coming Soon
Top Use Cases
Coming Soon
Start Analyzing
Coming Soon

Tag With Your Own Tags

Tag with your own tags is recommended when a preselected code frame (i.e. tag list) is ready. In other words when AI is not required to extract candidate tags from the dataset.

Benefit
1. Allows using a pre-selected set of tags
2. Allows using AI for tag assignment
Top Use Cases
1. When data trends are known and the user's knowledge of the dataset is vast 2. When a limited tag list is allowed
Start Analyzing
1. Upload your dataset
2. Run Tag with your own tags workflow

Add Tags To Existing Tags

Add tags to existing tags allows you to update an existing tag list.

Benefit
1. No need to run tagging from scratch
2. Update the tag list based on the new entries or discoveries in the dataset
Top Use Cases
1. When some new trends/insights are discovered in data
2. When a new subset is added to a dataset
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run a tagging workflow (e.g. generate AI tags workflow )
3. Run Add tags to existing tags

Rename/Combine Tags

Rename/Combine Tags allows you to rename tags that are generated by other workflows. A great functionality of this workflow is to move from different child tags to one parent tag.

Benefit
1. Easy and fast rename
2. Upgrading from child tags to a parent category
Top Use Cases
When some tags needs to be renames
When larger categories are preferred in analysis of the data
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run a tagging workflow (e.g. generate AI tags workflow )
3. Run Rename/Combine Tags workflow

Remove Tags

There might be cases where an existing tag should be removed. Remove Tags workflow provides you with a quick scan of your dataset and removes all occurrences of a specified tag.

Benefit
1. Quickly scan the whole dataset and remove an existing tag
Top Use Cases
1. Updating the existing tags after several analysis
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run a tagging workflow (e.g. generate AI tags workflow)
3. RunRemove Tags workflow

Translate

Translate your data to / from English.

Benefits
1. Easy access to state-of-the-art translator model
2. Understand your non English speaking client better
3. Unify the dataset by analysing the data in the same language
Top Use Cases
Translate comments from different languages and analyse them together
Understand clients using a language other than English better
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the Translate workflow

Extract Named Entities

Benefit
Coming Soon
Top Use Cases
Coming Soon
Start Analyzing
Coming Soon

Extract text count

Extract features such as number of sentences, number of words and number of characters from a selected field in your dataset.

Benefit
1. Easy analysis of free-text fields in your dataset
2. Access to a numeric value to represent data under metrics and filters
Top Use Cases
Access to statistics such as which age range provides more detailed feedback
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the Extract Text Count workflow

Media (Image, Audio, etc.)

Connect Media

Connect Media allows you to upload your media files (e.g. images or audio files) to Relevance AI, so that the file can be processed.

Benefit
1. Easy upload
2. Free space management
3. Safe platform
Top Use Cases
1. Image processing
2. Audio processing
Start Analyzing
1. Create an empty dataset
2. Zip your media files (Optional) 3. Run the Connect Media workflow

Audio Intelligence

Benefit
Coming Soon
Top Use Cases
Coming Soon
Start Analyzing
Coming Soon

Dates

Count Days

Count days workflow provides you with access to data calculation tools to be used for calculating the number of days between two date fields in a dataset.

Benefit
1. Easy to access accurate date-tools
Top Use Cases
1. Calculate the duration a ticket was in a syterm
Start Analyzing
1. Upload your dataset to Relevance AI's platform
and make sure there are at least two date fields included in the dataset (e.g. date-raised and date-completed) 2. Run the Count days workflow

Datasets

Connect to Appstore

Mine app reviews in AppStore using our scraping software and further analyze them with machine learning

Benefits
1. Easy access to recent and valuable data
2. Scrape customer feedback from any content in Appstore
Top Use Cases
Working with recent data from Appstore
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the Connect to Appstore workflow

Connect to Playstore

Mine app reviews in PlayStore using our scraping software and further analyze them with machine learning

Benefits
1. Easy access to recent and valuable data
2. Scrape customer feedback from any content in both Playstore
Top Use Cases
Working with recent data from Playstore
Start Analyzing
1. Upload your dataset to Relevance AI's platform
2. Run the Connect to Playstore workflow

Copy dataset

To create an exact copy of an existing dataset or copy a subset of a dataset.

Benefit
Versioning and keeping track of changes
Top Use Cases
1. Versioning
2. Sharing a subset of data
Start Analyzing
1. Upload your data
2. Run Copy Dataset Workflow

Export to xlsx

Export all or a subset of your dataset.

Benefit
Export all or a subset of your data
Top Use Cases
Export a subset of data or the whole dataset to an Excel file
Start Analyzing
1. Upload your data
2.Run Export Xlsx workflow

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