Using magic features

Track themes with channels (beta)

Using LLM and ML techniques, channels continuously analyzes incoming data and classifies it, allowing you to track themes in large data sets, such as support tickets, app reviews, feedback, and more.

Learn more about channels →

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Automatically create suggested highlights

Automatically find key moments in your data and create suggested highlights for you to accept or reject. Add a manual prompt to refine suggestions further.

Learn more about magic highlight →

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Cluster highlights into groups on canvas

Automatically group highlights with thematic similarities on your canvas. Themes are created from the content of your highlights, not the tags or titles. Titles will also be automatically generated for each group.

Learn more about magic cluster →

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Summarize key points in notes and insights

Save time identifying key themes in interviews, documents, or customer feedback, and turn them into valuable insights using AI. Add data to your notes and insights - including content like PDFs, reels, and transcripts - and we'll automatically generate a summary of the key points.

Learn more about magic summarize→

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Summarize highlights across your workspace

Enable your team to self-serve findings and share evidence-backed insights quickly by automatically summarizing highlight search results, complete with citations.

Learn more about magic search →

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Text, audio, and video redaction

Protect your participant's PII by blurring and muting video and audio content, as well as redacting text content within your transcripts.

Learn more about magic redact →

Why should I use Dovetail’s AI features over ChatGPT?

Dovetail’s tailored AI infrastructure means that your data won’t be used to train models for Dovetail or other customers—it’s fully secure. We select a model for the task at hand—whether that’s summarizing an insight or clustering highlights by theme. While ChatGPT enforces character limits, tailored infrastructure means that our AI features can handle whole transcripts and multiple highlights simultaneously.

Using Dovetail’s AI features ensures that all your customer data is in one place, so you don’t need to copy and paste data between tools and risk human error.

Will my data be secure if I use Dovetail's magic features?

We understand research data can contain lots of personal and commercially sensitive information, and participants trust you to keep it safe. That’s why we are committed to keeping this data secure and confidential.

Unlike tools like ChatGPT—which may use your data to train their models—we use tailored processing infrastructure on AWS, meaning your data remains your own. We deploy all our models in the same place it’s already stored. The request is sent to the model, and the response is returned. Models aren’t learning from your data.

You can read more about our data handling practices in our MSA (see in particular section 4, section 6, and section 11), our privacy policy, data processing agreement, and Dovetail trust center.

How is the model trained and deployed?

Dovetail uses a variety of market-leading LLMs. No customer data is used to improve or train our model—all training occurs before the models are deployed. Our models are constantly updated and improved to ensure you get the best experience.

Which Dovetail features use ML vs. generative AI?

Depending on the task, Dovetail’s AI features use machine learning (ML) or a combination of both ML and generative AI.

ML powers Dovetail’s transcription process. It allows us to identify positive and negative sentiments in transcripts and identify and blur faces to protect your users’ privacy. It’s also used to identify and cluster highlights by theme in canvas.

We use generative AI to summarize notes and insights that make it easy for you to keep stakeholders up-to-date. We also use generative AI to label your themes in canvas.

What is the difference between ML and generative AI?

We use a combination of generative AI and machine learning (ML) to power our AI features. While both are subsets of AI, put simply, ML uncovers and predicts patterns, and generative AI creates new content.

ML uses models and algorithms to do heavy lifting in making sense of large data sets. It’s able to find patterns among lots of data quickly, making it valuable for things like facial recognition and grouping themes.

Generative AI applies large language models and algorithms to create content. It taps into large content repositories, making it useful for generating summaries and answering questions.

Can I turn off Dovetail's AI features?

AI is foundational to many of Dovetail’s core features, including transcription and sentiment analysis, which means deactivating it for specific workspaces is not possible at this time.

Product specific terms

For more on Dovetail AI, check out our product-specific terms here.

What languages do you support for creating a summary with AI?

At this time, we currently support Spanish, French, German, Portuguese, Italian and Dutch. The summary generated, however, will be in English.

What languages do you support for clustering with AI?

Unfortunately at this time, we only support thematic clustering of data that is in English. We are currently monitoring customer feedback to understand how to improve this feature and the languages we support.

What languages do you support for sentiment analysis?

Automatic sentiment analysis currently works on text in the following languages:

Arabic, Chinese (Simplified), Chinese (Traditional), English, French, German, Hindi, Italian, Japanese, Korean, Portuguese, Spanish.

Do you train on user data for semantic search or magic summaries?

No, we use a generic AI model and don’t feed it any training data to ensure user data is kept private.