Sold by: Dovetail
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Dovetail is a customer intelligence platform, the first always-on, AI-native system that transforms customer feedback into real-time intelligence, and most importantly, into action.
4.5
Overview
Dovetail is the AI-native customer intelligence platform that transforms customer feedback into real-time intelligence and action. By unifying every signal from user feedback and customer interviews to support tickets and sales calls, Dovetail continuously applies AI analysis and integrates directly with prototyping and coding workflows, ensuring product development moves at the speed of customers.
The Dovetail customer intelligence platform operates as a continuous cycle of four stages:
Assemble: Centralize every customer voice - sales calls, support tickets, surveys, app reviews, usability tests, and more.
Analyze: Let AI classify automatically, build dashboards, compare segments, and track sentiment shifts.
Uncover: Use AI-powered Chat to surface insights, answer questions, and generate PRDs, research reports, and Voice of Customer updates.
Act: Close the loop with Linear tickets, Slack alerts, automated reports, and Alloy prototypes.
For custom pricing, EULA, or a private contract, please contact AWS-Marketplace@dovetail.com , for a private offer.
Highlights
- AI Contextual Chat: Drill down on data with scoped AI answers
- AI Docs: Generate PRDs, VoC reports, and summaries with citations
- AI Agents: Intelligent operators that reason over Dovetail data and act on your behalf
Details
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Pricing
Free trial
Try this product free according to the free trial terms set by the vendor.
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Enterprise | Please reach out to AWS-Marketplace@dovetail.com for custom pricing. | $100,000.00 |
The following dimensions are not included in the contract terms, which will be charged based on your usage.
Dimension | Cost/unit |
|---|---|
Overage above contracted amount. | $0.01 |
Dimensions summary
You buy Dovetail through a contract with two pricing dimensions. The Enterprise dimension is a custom-priced commitment; you contact the vendor at AWS-Marketplace@dovetail.com to set your price based on your needs. The Overage dimension covers usage beyond your contracted amount. It applies only if you exceed what your Enterprise contract includes. Together, these dimensions form a committed base plus a variable charge for any excess. Pricing scales with how much you commit to and whether you go over that commitment.
Top-of-mind questions for buyers
Users are the billable unit. A user is anyone who contributes data to your workspace or does analysis within it. Most buyers cover product management, design, customer experience, strategy, and innovation teams. Some also add sales, support, customer success, and marketing users who use customer feedback.
You pay a committed base under the Enterprise dimension. If your usage passes the amount that contract includes, the Overage dimension bills the excess. The Enterprise commitment forms your base; the Overage adds a variable charge only when you exceed it. Both can appear together on your invoice.
No. There is no way to pause the subscription. If you cancel, you can use Dovetail through the end of your billing period. After that, your workspace stays read-only for 30 days so you can export data. Then data is deleted per your retention policy. Refunds are not offered.
dovetail.com
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You will not be entitled to a refund of paid and outstanding fees.
Custom pricing options
Request a private offer to receive a custom quote.
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Find the answers you need to help leverage the full potential of Dovetail and start enabling your teams to build better products.
You can contact support at support@dovetail.com . Support is available 24/7. We'll respond within 24 hours.
You will have a dedicated customer success manager. The customer success team can be contacted at customersuccess@dovetail.com . Customer success is available from 7:00 a.m. to 7:00 p.m. US PST, Monday through Friday.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Benjamin M.
A Research Repo That Scales Beyond Interviews
Reviewed on Nov 05, 2026
Review provided by G2
What do you like best about the product?
The best thing about Dovetail for us is that it holds far more than interviews. Our workspace has over 100 projects, and next to the interviews we run channels that pull in NPS, support tickets, app reviews, and survey responses. When a question comes up, like why users churn or what they say about AI, the evidence is in one workspace and can be searched together.
The MCP connector is what made that useful beyond the research team. I connect Dovetail to Claude to ask questions across projects and check answers against the original data. We also use it to build a research library and weekly digests that colleagues in product, marketing and design can query without asking me. Dovetail is the layer that holds the raw evidence, and everything we build on top of it traces back to a highlight or a transcript.
The MCP connector is what made that useful beyond the research team. I connect Dovetail to Claude to ask questions across projects and check answers against the original data. We also use it to build a research library and weekly digests that colleagues in product, marketing and design can query without asking me. Dovetail is the layer that holds the raw evidence, and everything we build on top of it traces back to a highlight or a transcript.
What do you dislike about the product?
Two things have slowed us down. The first is capacity. Once we started bringing NPS, support tickets, reviews and surveys into Dovetail next to interviews, our data grew quickly and we hit the data point limits on our earlier plan. That forced choices about which sources to keep, which works against having everything in one place, and we moved to Enterprise to fix it. It would help to see usage limits and what is approaching them more clearly before you hit them.
The second is that some of the AI features only work in the interface. Dovetail's AI chat is available in the app, but I can't reach it through the MCP connector, so a workflow that runs in Claude has to rebuild the analysis from the underlying data instead of reusing what Dovetail's AI already produced. As more of our work moves into agent workflows, I'd like the AI features to be available through the connector too.
The second is that some of the AI features only work in the interface. Dovetail's AI chat is available in the app, but I can't reach it through the MCP connector, so a workflow that runs in Claude has to rebuild the analysis from the underlying data instead of reusing what Dovetail's AI already produced. As more of our work moves into agent workflows, I'd like the AI features to be available through the connector too.
What problems is the product solving and how is that benefiting you?
Customer evidence is scattered across interviews, support tickets, NPS, app reviews, community posts and surveys, and each source lives in a different tool owned by a different team. Dovetail gives us one place where all of it can be stored, tagged and searched.
For me, the benefit is that I'm the only UX researcher at the company, so I can't run every study myself. PM, PMM and designers run their own customer conversations, and their recordings and findings end up in the same workspace as mine. Everything stays linked to the original transcript or data point, which lets me verify a quote before it goes into a report.
The MCP connector has also changed what the data is used for. I can query Dovetail from Claude, and we build a research library and weekly digests from it, so the evidence reaches people who would never open a research repository.
For me, the benefit is that I'm the only UX researcher at the company, so I can't run every study myself. PM, PMM and designers run their own customer conversations, and their recordings and findings end up in the same workspace as mine. Everything stays linked to the original transcript or data point, which lets me verify a quote before it goes into a report.
The MCP connector has also changed what the data is used for. I can query Dovetail from Claude, and we build a research library and weekly digests from it, so the evidence reaches people who would never open a research repository.
Lizz C.
Scaling the impact of research with Dovetail
Reviewed on Nov 04, 2026
Review provided by G2
What do you like best about the product?
Dovetail makes research and customer data more accessible, actionable, and scalable beyond the Research team. With user interviews, connected sales notes, and other customer feedback in one place, anyone in the company is able to use the AI chat to ask questions and get reliable answers immediately. The Research team no longer has to go searching through drives or local folders for answers, often unable to efficiently triangulate insights - Dovetail makes that possible for us now. Dovetail's transcription is best in class, the highlighting, tagging, and reel generation has been immensely helpful in speeding up our analysis and synthesis process to we're able to deliver insights, better and faster to teams. The AI generated summaries at the interview level have also been an accelerant to get folks up to speed on what was learned during an interview they didn't attend.
What do you dislike about the product?
It’s difficult for an individual to navigate to a specific project folder or interview unless they have a direct link. To get to a particular project folder, you have to open a project first and then use the breadcrumb trail to back into the folder. With the way we organize our data, some people want to chat with an entire folder rather than just a single project or interview, while others want the opposite: to chat with one project or one interview without having to use the entire repository.
What problems is the product solving and how is that benefiting you?
Having all our sales notes automatically connected to a Channel, which then generates themes and summaries, has been a game-changer for us. In the past, trying to manually identify themes and pull out insights would have been a quarter-long initiative. Now, with the ability to connect different sources in Dovetail, we can get actionable insights at our fingertips. With an incredibly small research team, time is our most sacred resource. Dovetail allows people to find answers to their questions without having to take time away from researchers. It helps everyone become more knowledgeable, which leads to higher-confidence decision-making for our customers.
Information Technology and Services
Excellent research repository, but the MCP isn’t ready for how we want to use it to
Reviewed on Nov 04, 2026
Review provided by G2
What do you like best about the product?
Dovetail is a great home for our raw research data. Interview recordings and transcripts are easy to store, organise and find, and it’s become our central place for customer evidence. The built-in Dovetail AI has improved a lot over the past year, and it’s useful for quick analysis inside the platform.
What do you dislike about the product?
We want to use Dovetail mainly through its MCP connector, so our own internal tools can query our research. At the time of writing, two critical gaps stop us from doing that properly:
No OAuth on the MCP. For an enterprise team, this is a blocker for rolling it out securely and at scale.
The MCP can’t read PDFs in Docs (insights). It can read PDFs in Data (raw transcripts), but not in Docs. Nearly all our Docs are PDFs, so the MCP misses most of our synthesised insights. That’s arguably the most valuable layer of our research.
No OAuth on the MCP. For an enterprise team, this is a blocker for rolling it out securely and at scale.
The MCP can’t read PDFs in Docs (insights). It can read PDFs in Data (raw transcripts), but not in Docs. Nearly all our Docs are PDFs, so the MCP misses most of our synthesised insights. That’s arguably the most valuable layer of our research.
What problems is the product solving and how is that benefiting you?
Dovetail centralises our research so it isn’t scattered across drives and individual laptops, which makes past interviews much easier to find and reuse. The potential is much bigger: our tools and AI workflows could draw directly on everything we’ve learned about our customers. I understand OAuth and PDF support in Docs are on the roadmap, and I’d strongly encourage Dovetail to prioritise them. Once they land, I’d expect this to go from a good research tool to an essential part of how we work.
marco c.
Powerful AI for research, but navigation needs clarification
Reviewed on Nov 02, 2026
Review provided by G2
What do you like best about the product?
I like that Dovetail integrated AI from the start: I can chat with the data and query the repository easily. This way, people can find answers directly in the repository without having to go through the research team, saving time. It also helps me rediscover older insights, including those collected by people who no longer work at the company. I appreciate being able to organize and share both raw material and research reports on the platform. The AI can analyze transcripts and bring out things the researcher hadn't noticed, as well as secondary insights. I can find these insights thanks to tags and use them in future research.
What do you dislike about the product?
The initial learning curve is quite steep, and some elements have recently changed names. I don't always understand where I am in the navigation, and if I want to change the folder structure, it's really complex to move the contents. I find the information architecture complicated and don't always understand what certain elements of the interface and system objects are. I'm not sure I've managed to define an effective information architecture for our needs.
What problems is the product solving and how is that benefiting you?
I use Dovetail as a repository to organize and share raw research materials and reports within the company. People can query the research with AI; the analysis of transcripts brings out unnoticed or secondary insights, which I can tag and find in future research instead of losing them.
Nathan J.
Dovetail’s MCP Functionality Makes User Research Accessible Organization-Wide
Reviewed on Oct 30, 2026
Review provided by G2
What do you like best about the product?
Without question, the best part of Dovetail is it's MCP functionality. We've been able to democratize years of user research across our entire organization with a single MCP connection.
What do you dislike about the product?
One thing that’s been frustrating is the connection experience when trying to connect Dovetail to our various internal tools. We’ve gone back and forth between Dovetail and our internal IT/Security team, but it’s never quite been fully resolved. That said, once the connection issues are sorted out, it works extremely well.
What problems is the product solving and how is that benefiting you?
Our biggest pain point has been finding a frictionless way to aggregate research insights across the organization and then carry them through into the product designs we actually execute on as a team. Dovetail has addressed that really well for us, and the MCP is queried frequently by nearly all of the designers on our design team.