Rippit vs. Tethr (Now Capacity) for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both
Short answer: Tethr, now part of Capacity,[1] is a contact center platform that combines conversation intelligence, automated QA, real-time agent assist and AI virtual agents in one suite.[2] Tethr and Rippit overlap in one area: analyzing conversation data. Tethr analyzes every customer interaction across voice and digital channels and organizes it into 1,200+ prebuilt insight categories that contact center teams use to find insights.[3]
Rippit is built for one job: analyzing and acting on every customer conversation at scale.[4] It reads every conversation customers have with your company, across sales, support, success and chatbots, turns what it finds into structured data,[5] and runs AI agents that monitor, analyze and act on that data.[4] And it goes many layers deeper: any team can define a new field in plain English, combine it with others and keep drilling until it reaches the root cause.[6]
Rippit is self-serve with public pricing.[7] Tethr prices through a demo.[3]
Rippit vs. Tethr at a glance
| CRITERIA | Tethr | Rippit |
|---|---|---|
| What is it? | Conversation intelligence and agent assist for the contact center, now sold under the Capacity brand[2] | AI conversation analytics and agents for every team[4] |
| Primary user | Contact center teams[2] | Product, CX, support, sales and customer success teams, plus leadership, all working from the same conversation data |
| Starting point | Voice and digital interactions in the contact center[3] | The conversations you already have across every channel: tickets, calls and chats,[8] plus the survey data tied to them[9] |
| Best fit | Contact centers that want prebuilt insight categories across voice and digital interactions[3] | Large-scale analysis and agents across every conversation your company has, with one dataset serving every team: product feedback, CX and support operations, customer success and churn risk, and AI agent monitoring[10][11] |
| Feedback sources | Voice and digital channels, with integrations for major CCaaS providers and 250+ prebuilt integrations[3] | Anywhere customers talk to you or about you: support, sales, success and chatbot conversations across tickets, chat, email, calls and messaging, plus surveys, reviews and any data in your warehouse.[8][9] Survey scores and account data link to each conversation |
| Conversation analysis | The core product. Analyzes every interaction across voice and digital channels, with sentiment, emotion detection and predictive CSAT[3] | The core product. AI analyzes every conversation, with nothing sampled[4] |
| Taxonomy approach | 1,200+ prebuilt insight categories organized by vertical, plus custom categories from a no-code builder[3] | No fixed hierarchy. Give Rippit your taxonomy and it will automatically create it and dig multiple levels deeper or let Rippit dynamically generate it for you within an hour[6] |
| Depth of analysis | Categories, predictive CSAT and sentiment scores, with drill-down to calls and transcripts[3] | Many layers deep. Combine fields like root cause, product area, resolution and churn risk, filter to any segment, and ask the next question on the same data[6][12] |
| How teams use the data | Real-time dashboards on performance, deflection and agent assist effectiveness[3] | Describe the insight you want, and Rippit adds it as a field across every conversation (root cause, churn risk, resolution).[6] Reuse it in dashboards, reports and agents - leverage MCP connections to send to Linear, Jira, Slack, and any other MCP destination |
| What gets scanned, analyzed and acted on | Every voice and digital conversation, analyzed automatically[3] | 100% of conversations in scope, with nothing sampled[7][4] |
| How it handles large datasets | Analyzes all conversations automatically against its category library[3] | Stores every conversation as structured, queryable data[5] that agents and people can enrich and aggregate across the full dataset |
| Traceability | Flagged categories link directly to specific moments in calls and transcripts[3] | Every result is a field on a specific conversation,[5] and each analysis step is documented, so any answer can be audited |
| Repeatability | Categories run automatically on every new conversation[3] | Turn any analysis or action into a repeatable Agent App that runs itself[4] |
| Setup | Deploys with major CCaaS providers; the site routes buyers to a demo[3] | Self-serve. Connect Zendesk, Intercom, Granola, Gong and more in one click[8] and go live in minutes[4] |
| Works with Claude | 250+ prebuilt integrations[3] | MCP Server so Claude, Codex, Cursor, and more can query your conversation data directly |
| Public pricing | Not published; the site routes buyers to a demo[3] | Free (includes $100 in AI credits); Starter $185/mo; Growth $495/mo; custom Enterprise[7] |
| Best starting question | "What are customers asking us to build or fix, across every channel?" | "Which customers hit this issue, why, and what else do they have in common?" |
What's the difference between Rippit and Tethr?
The three biggest differences are pricing model, time to value, and focus.
Pricing model. Rippit publishes its pricing, from a free plan to $185/month and $495/month plans.[7] Tethr doesn't publish prices; Capacity's site routes buyers to a demo.[3]
Time to value. Rippit is self-serve: sign up on the website, connect Zendesk, Intercom, Granola or Gong in one click and go live in minutes, with no engineering required.[7][8][4] Tethr deploys with major CCaaS providers, and Capacity's site routes buyers to a demo.[3]
Focus. Tethr, now part of Capacity, is built for the contact center, with conversation intelligence, auto-QA, agent assist and voice solutions.[2] Capacity acquired Creovai, the company formed in 2024 when Tethr merged with Awaken Intelligence, in October 2025.[1] Tethr analyzes every interaction across voice and digital channels and tags it against 1,200+ prebuilt insight categories.[3] Rippit is AI conversation analytics and agents, built for scale.[4] It analyzes every conversation from any source, including support, sales and success calls,[8] and stores what it finds as fields any team can query and keep drilling into.[6] Agents then monitor those fields, analyze them and act on what they find.[4] Tethr answers "What's happening in our contact center?" Rippit answers that, then keeps going: which customers, which plan, what happened before they got stuck, and whether it's getting worse.
How deep can Rippit's analysis go?
As deep as the question needs. Every field Rippit adds lives on each conversation, so fields combine. You can filter to billing conversations, narrow to ones that stayed unresolved, split by customer segment, then add a new field to explain why, all on the same data.[6]
Checkr shows what that looks like. Instead of stopping at broad categories, it used AI classifiers to break dissatisfaction into specific "atomic problems." One billing cluster showed 47% predicted CSAT and a 58% unresolved rate, specific enough to take straight to the Chief Product Officer.[12]
Each new question builds on the last. A new field applies to past conversations in a single run, so a question you think of today gets answered for last quarter too, without rebuilding a taxonomy.[6] When Checkr's CEO asked a late-night question about background check issues in one state, the team had a detailed report by the next morning.[12]
How is Rippit's classification different from Tethr's categories?
Tethr starts from a library of 1,200+ prebuilt insight categories organized by vertical, covering contact reasons, compliance behaviors and customer effort signals, and teams can add custom categories with a no-code builder.[3] For a team that wants ready-made categories for contact center behaviors, that structure is a real strength.
Rippit doesn't require a predefined hierarchy. AI classifies each conversation across 10+ dimensions in one pass, including intent, theme, root cause, sentiment, urgency, churn signal and resolution.[6] When you need a new cut, you describe the insight you want,[5] and Rippit adds it as a field across every conversation, including past ones, in a single enrichment run.[6] Any team can add its own fields, so the analysis follows the question instead of a fixed tree.
How does Rippit analyze every conversation?
Rippit runs an AI engine built to label conversations in bulk: AI enriches every conversation[4] and stores the results as structured, queryable data,[5] so analysis and agents can work across all of it. Rippit says it can analyze 10,000 conversations in one minute for under $1.[4] Questions then run on those stored fields across the full dataset, instead of rereading transcripts each time. For open-ended questions with no existing field answers, Rippit also runs ad hoc deep dives[5] on up to 10,000 conversations at a time, and what they find can feed new analysis and agents.
That changes three things:
Tethr also analyzes every conversation automatically,[3] so coverage isn't what separates them. Tethr rolls conversations up into categories and scores.[3] Rippit's first difference is depth: every conversation carries many fields you define, so you can slice the full dataset as far as the question goes. Rippit's second difference is time to value being within an hour.[6]
Can you use Rippit and Tethr together?
You can, but it would be wasteful - this happens when different departments are buying solutions independently. Ultimately, you should have a single source of truth for conversation data that is enriched and organized into an ontology specific to your business.
How much do Rippit and Tethr cost?
Rippit publishes its pricing. Paid plans bill AI credits at cost.[7]
Tethr doesn't publish prices. Capacity's site routes buyers to a demo.[3]
To compare fairly, give both vendors the same sources, volume and history, and ask for total cost including onboarding.
What do teams use Rippit for?
- Checkr broke dissatisfaction down into specific "atomic problems." One billing cluster showed 47% predicted CSAT and a 58% unresolved rate, which the team took to its Chief Product Officer. Checkr says its time from insight to action went "from weeks to hours."[12]
- Brex replaced manual sampling with AI review of 100% of conversations and found churn risk in 3% of them. Custom AI classifiers tag onboarding friction, product gaps and competitor mentions.[11]
- Klaviyo analyzed a full month of product-launch conversations in two hours and reports a productivity gain of about 40% in under 12 months.[10]
When should you choose Tethr, Rippit, or both?
Your contact center wants prebuilt categories for contact reasons, compliance and customer effort across voice and digital channels, plus real-time agent assist.
You want to go many layers deep on what customers say across sales, support, success and chatbot conversations, with fields any team can define and combine, for product, CX, CS and leadership.
Rarely, and not recommended. You should have one source of truth for conversation data that is used across all teams and departments for analysis.
Frequently asked questions
Is Rippit a Tethr alternative?
Yes. Both analyze customer conversations. Rippit goes more granular on conversations, with fields any team can define and combine, and puts the results to work across product, CX, CS and leadership.[4][6]
How granular can Rippit's analysis get?
As granular as the question needs. Checkr used Rippit to break dissatisfaction into specific "atomic problems" rather than broad categories.[12] Fields combine, so you can keep narrowing to any segment and cause.[6]
Does Rippit analyze 100% of conversations?
Yes, every conversation in the scope you choose, with nothing sampled.[4]
Is Rippit cheaper than Tethr?
Rippit has a free plan and paid plans from $185/month.[7] Tethr doesn't publish pricing,[3] so compare equivalent scope.
Which is faster to set up?
Rippit is self-serve: connect Zendesk or Intercom in one click[8] and go live in minutes.[4] Tethr starts from 1,200+ prebuilt categories and deploys with major CCaaS providers.[3]
Does Rippit work with Claude?
Yes. Rippit supports MCP, so Claude can query your conversation data directly.[13] Tethr, now Capacity, offers 250+ prebuilt integrations.[3]
Can I trace a number back to the conversations behind it?
Yes. In Rippit, every result is a field on a specific conversation,[5] so you can audit any answer by opening the conversations and fields behind it. Tethr also links flagged categories to specific moments in calls and transcripts.[3]
Is Rippit the same company as MaestroQA?
Yes. MaestroQA is now Rippit: same founders, with a rebuilt AI-first platform. The company moved from maestroqa.com to rippit.com, and existing customer data and integrations carried over.[14] Some Rippit help center articles still appear under the MaestroQA name and domain.
Sources
Rippit was formerly MaestroQA. Some sources below are hosted on the MaestroQA help center (help.maestroqa.com).[14]
- Capacity, Capacity acquires Creovai (October 2025): https://capacity.com/blog/capacity-acquires-creovai/
- Capacity, Tethr is now Capacity: https://capacity.com/tethr/
- Capacity, Conversation Intelligence: https://capacity.com/conversation-intelligence/
- Rippit, homepage: https://www.rippit.com/
- Rippit, How It Works: https://www.rippit.com/how-it-works
- Rippit, Conversation Taxonomy playbook: https://www.rippit.com/playbooks/conversation-taxonomy
- Rippit, Pricing: https://www.rippit.com/pricing
- Rippit, Integrations: https://www.rippit.com/integrations
- Rippit Help Center, Qualtrics Integration: https://help.maestroqa.com/en/articles/5724156-qualtrics-integration
- Rippit, Klaviyo customer story: https://www.rippit.com/customer-story/klaviyo
- Rippit, Brex customer story: https://www.rippit.com/customer-story/brex
- Rippit, Checkr customer story: https://www.rippit.com/customer-story/checkr
- Rippit, The Complete Guide to Conversation Analytics: https://www.rippit.com/playbooks/the-complete-guide-to-conversation-analytics
- Rippit, "MaestroQA is now Rippit": https://www.rippit.com/news/maestroqa-is-now-rippit
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