--- title: "Rippit vs. Clay for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both" description: "Rippit vs. Clay: Clay enriches accounts from 200+ data providers; Rippit analyzes every customer conversation. Differences, pricing and when to use both." canonical: https://www.rippit.com/comparisons/rippit-vs-clay type: Comparison datePublished: 2026-09-28 dateModified: 2026-11-13 --- # Rippit vs. Clay for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both **Short answer:** Clay and Rippit both fill spreadsheet-like tables with AI, but they look in opposite directions. Clay looks outward: it enriches companies and people with data from 200+ providers for outbound and GTM plays.[[1]](#src-1) Rippit looks inward. It's a Growth Data Platform for what your own customers and prospects say: it reads every call, chat and email you connect and turns what it finds (intent, root cause, churn risk) into structured data,[[3]](#src-3) kept current next to your CRM and warehouse data.[[2]](#src-2) Keep Clay for prospecting, and add Rippit to learn what customers actually say. The two meet in GTM agents: Rippit tells you which accounts to act on and why, and Clay finds and enriches the people to reach. By Lauren Alexander · Updated November 13, 2026 ## Rippit vs. Clay at a glance | CRITERIA | Clay | Rippit | | --- | --- | --- | | What is it? | GTM data and workflow platform for enriching accounts and leads[[1]](#src-1) | Growth Data Platform: your conversations and business data, structured and kept current, with agents your teams set up[[2]](#src-2) | | What gets enriched | Companies and people: contact info, company attributes,[[4]](#src-4) buying triggers, org changes[[5]](#src-5) | Conversations: every ticket, call and chat you connect,[[6]](#src-6) enriched with customizable parameters: root cause, sentiment, churn signal and resolution[[7]](#src-7) | | Data source | External: 200+ data providers, combined through waterfall enrichment,[[4]](#src-4) plus Claygent web research[[5]](#src-5) | First-party: your own conversations from your helpdesk, CRM and call tools,[[6]](#src-6) analyzed next to pipeline data, warehouse tables and your own research | | Starting point | A list of companies or people, from Clay's Find Companies and Find People, a CRM, a CSV or a webhook[[8]](#src-8) | The conversations you already have: tickets, calls and chats[[6]](#src-6) | | Primary team | GTM and RevOps teams[[1]](#src-1) | CX, support, GTM Ops, data engineering, product, sales and customer success[[2]](#src-2) | | Best fit | GTM data and engineering teams building target lists, enriching CRM records and automating outbound | RevOps, sales and CS teams that need to know which accounts and deals to act on, and why, from what customers and prospects said | | How AI fills the table | Claygent agents research companies and people on the web and return structured data into columns[[5]](#src-5) | Describe the insight you want, and Rippit adds it as a field across every conversation in scope (root cause, churn risk, resolution).[[7]](#src-7) Reuse it in dashboards and follow-up questions, and set up agents that send new findings to the owner | | Customization | Build columns with AI prompts, waterfalls and formulas[[9]](#src-9) | Define a field in plain English; it applies to past conversations in one run[[7]](#src-7) | | What gets scanned, analyzed and acted on | The companies and people in your tables; waterfalls search providers in sequence until one returns a valid match[[4]](#src-4) | 100% of conversations in scope, with nothing sampled[[10]](#src-10)[[2]](#src-2) | | How it handles large datasets | Built for high-volume plays: Account Agents run across every account in an Audience segment[[5]](#src-5) | Stores every conversation you connect as structured, queryable data[[3]](#src-3) that agents and people can enrich and aggregate across the full dataset | | Traceability | Every agent decision comes with a full reasoning trace[[5]](#src-5) | Every result is a field on a specific conversation,[[3]](#src-3) and each analysis step is documented, so any answer can be audited | | Repeatability | Claygent runs on demand or on a schedule; Account Agents update automatically as segments change[[5]](#src-5) | Agents you set up run hourly, daily, weekly or monthly on new data and send findings, with the evidence, to Slack or email[[22]](#src-22) | | Setup | Self-serve signup, with a free plan and a 14-day trial[[11]](#src-11) | Self-serve. Connect Zendesk, Intercom, Granola, Gong and more with pre-built integrations, without needing an engineer to start[[6]](#src-6) | | Works with Claude | Clay MCP connector for Claude and ChatGPT[[12]](#src-12) | Rippit's MCP server lets Claude, ChatGPT or Cursor query your Rippit data and open the conversations behind an answer[[13]](#src-13) | | Pricing model | Two meters: Data Credits (from $0.05 each) buy data and AI; Actions (under $0.01 each) cover platform usage[[11]](#src-11) | Usage-based: storage at $1/GB a month, AI models at list price[[10]](#src-10) | | Public pricing | Free (100 data credits, 500 actions/mo); Launch from $185/mo; Growth from $495/mo; custom Enterprise (10% less billed annually)[[11]](#src-11) | $10 in free credits to start, no card needed; then pay as you go: $1/GB a month for storage with compute included, AI models at list price, people and agents free; Enterprise by quote[[10]](#src-10) | | Best starting question | "Who should we reach out to, and what should we say?" | "Which open deals raised the objection our lost deals raised this quarter, and what did those buyers say?" | **01 · THE DIFFERENCE** ## What's the difference between Rippit and Clay? Clay enriches the accounts and people you want to reach. Rippit enriches the conversations you already have with customers. Clay calls itself "infrastructure to get any data, run agentic workflows, and launch GTM plays."[[1]](#src-1) Every Clay table starts with a source, such as a list of companies or people, a CRM or a CSV.[[8]](#src-8) Clay fills columns from 200+ data providers, using waterfall enrichment to try them in sequence,[[4]](#src-4) and from Claygent, an AI agent that researches companies and people on the web.[[5]](#src-5) Rippit is a Growth Data Platform built on first-party data.[[2]](#src-2) It connects to your helpdesk and call tools, reads every conversation you connect,[[2]](#src-2) and turns what it finds (intent, root cause, sentiment, churn signal, resolution) into fields you can filter, trend and build on.[[7]](#src-7) It analyzes the data around those conversations too, such as CRM pipeline, warehouse tables and your own research, so a signal sits next to the deal stage and account value. Your CRM and warehouse stay the source of truth; teams set up agents on the results. For RevOps, the difference is the source. Clay fills a row with what providers and the web know about an account. Rippit fills it with what that account told you, and keeps adding as new conversations arrive. **CLAY** **Looks outward** Target accounts · Prospects · New markets Enriches the accounts you want to reach **Your company** **RIPPIT** **Looks inward** Support tickets · Chats and emails · Customer calls Enriches the conversations you already have **02 · SCALE** ## How does Rippit analyze every conversation? 1,000 conversations or 1,000,000, every call, ticket and chat: Rippit reads every one you connect.[[2]](#src-2) Rippit enriches every conversation you connect[[2]](#src-2) and stores the results as structured, queryable data,[[3]](#src-3) so analysis and agents can work across all of it. Enriching every conversation doesn't have to be expensive. In Rippit's own coverage benchmark, it read all 1,000 transcripts in full for about six cents, the same full read that cost about $61 per question with a map-reduce approach.[[21]](#src-21) Questions then run on those stored fields across the full dataset, instead of rereading transcripts each time, so asking the same question again returns the same answer.[[21]](#src-21) For open-ended questions no existing field answers, Rippit also runs ad hoc worksheet analysis[[3]](#src-3) on up to 10,000 conversations at a time, and what they find can feed new analysis and agents. That changes three things: **1,000 conversations or 1,000,000. Rippit reads every one you connect.** **Counts are complete** "Which accounts showed churn risk in their conversations last quarter?" is computed across every conversation in scope, not a sample.[[10]](#src-10) **New questions reach the past** Describe a new field in plain English, and Rippit applies it to past conversations in a single run.[[7]](#src-7) **Every number is checkable** Each result is a field on a specific conversation, so you can open the conversations behind any count.[[3]](#src-3) **03 · USE BOTH** ## Can you use Rippit and Clay together? Yes. Keep Clay for prospecting and CRM enrichment, and add Rippit for what customers and prospects actually say. Take one RevOps question: *which customers asked about a product they don't own yet this quarter, and who else at those accounts should hear from us?* Rippit finds the accounts and the quotes behind each request; Clay finds and enriches the other contacts at each account. How the work divides and hands off: - **Rippit does the mass-scale enrichment.** It analyzes every conversation in scope[[2]](#src-2) and stores each judgment as a field on that conversation.[[7]](#src-7) - **Rippit runs worksheet analysis.** Ad hoc analysis on up to 10,000 conversations at a time, for questions no existing field answers yet.[[3]](#src-3) - **Churn risk to retention play.** Rippit flags churn signals across every conversation you connect.[[2]](#src-2) Brex feeds these signals into CSM workflows, so the team can act before surveys reveal a problem.[[14]](#src-14) - **Expansion profiling to targeting.** Klaviyo mines historical churn patterns and studies the customers who stayed and grew, by segment, to repeat what worked.[[15]](#src-15) Profiles like these tell a GTM team which lookalike accounts to build in Clay. - **Account list to enrichment.** Clay tables accept CSV imports, CRM records and webhooks,[[8]](#src-8) so a list of flagged accounts can become a Clay table for contact research and outreach. Save the Rippit question as an agent, and the list of newly flagged accounts reaches the team in Slack or email each week.[[22]](#src-22) **IN RIPPIT** **Analyze conversations for churn risk** Churn signal · Root cause · Account **RIPPIT AGENT** **Sends the flagged accounts to your team** **CLAY AGENT** **Finds all the contacts at that account** **OUTCOME** **Contact research and outreach** In Rippit's review of 2,345 of its own sales conversations from 2025–2026, a handful of prospects already used Clay on their GTM team.[[16]](#src-16) Some said Rippit's worksheet view felt familiar: rows of records, with AI filling in the columns.[[16]](#src-16) The difference is that Rippit's rows are your own customer conversations. Both tools also support MCP, so a team can work with each of them from Claude.[[12]](#src-12)[[13]](#src-13) Rippit can also connect to Clay as an MCP server, so the Rippit agent can search and enrich companies and contacts from Clay in the same analysis. Those searches spend Clay credits, so a Clay admin should set a credit limit first. **04 · ENRICHMENT** ## How is Rippit's AI enrichment different from Claygent? Claygent researches the open web. Rippit reads your own conversations. Clay says Claygent can "find unique data no provider has," such as niche company attributes, buying triggers and org changes, and it can draft outreach based on recent news and job postings.[[5]](#src-5) Rippit's AI works on data only you have. One pass tags each conversation with the fields you define, such as intent, root cause, sentiment, urgency, churn signal and resolution. When your business changes, a new category applies to past conversations in a single run.[[7]](#src-7) That matters because keyword rules often misfire. A rule looking for "cancel" flags "How do I cancel a duplicate order?" as churn, while AI reads the actual intent.[[7]](#src-7) **CLAYGENT** The open web **Finds data no provider has[[5]](#src-5)** Niche company attributes · Buying triggers · Org changes · Outreach drafts **RIPPIT AI** **Your own conversations** **One pass, every field you define[[7]](#src-7)** Intent · Root cause · Sentiment · Urgency · Churn signal · Resolution **CUSTOMER MESSAGE** **"How do I cancel a duplicate order?"** Keyword rule: churn · AI: reads the actual intent **05 · AI ASSISTANTS** ## Do Clay and Rippit have an MCP server? Yes. Rippit supports MCP, so AI assistants like Claude, ChatGPT or Cursor can query your Rippit data directly.[[13]](#src-13) You ask in Claude, and Rippit runs the analysis across your conversations and the data connected to them.[[17]](#src-17) This matters at scale. A general-purpose assistant works within a context window,[[18]](#src-18) so on large sets of conversations it retrieves, splits or summarizes. Rippit has already read every conversation you connect and saved the results as fields, so Claude starts from those, and every number traces back to specific conversations. Clay also has an MCP connector that brings its data providers, research agents and workflows into Claude and ChatGPT, so reps can find contacts and research accounts without opening Clay.[[12]](#src-12) **06 · PRICING** ## How much do Rippit and Clay cost? Rippit publishes its pricing. AI models and data tools are billed at each provider's list price, with no markup.[[10]](#src-10) - **Free to start:** $10 in credits, no card needed, plus $90 more when you add a card[[10]](#src-10) - **Storage:** $1 per GB a month, with the compute to sync, run jobs and query included[[10]](#src-10) - **AI models and data tools:** each provider's list price, no markup[[10]](#src-10) - **People and agents:** free[[10]](#src-10) - **Enterprise:** a personalized quote, with SSO, SCIM, audit logs, data region choice and custom terms[[10]](#src-10) Clay also publishes its pricing, on two meters. Data Credits buy data and AI from vendors in Clay's marketplace, starting at $0.05 each.[[11]](#src-11) Actions measure platform usage and cost less than $0.01 each.[[11]](#src-11) - **Free:** 100 Data Credits and 500 Actions/month[[11]](#src-11) - **Launch:** from $185/month ($167/month billed annually), starting at 15,000 Actions/month[[11]](#src-11) - **Growth:** from $495/month ($446/month billed annually), starting at 6,000 Data Credits and 40,000 Actions/month[[11]](#src-11) - **Enterprise:** custom, annual commitment[[11]](#src-11) The two budgets cover different work. Size Clay by the records you enrich and the providers each waterfall calls. Size Rippit by how much conversation data you store and which AI models you run on it. **07 · CUSTOMERS** ## What do teams use Rippit for? - **Brex** replaced manual QA sampling with AI review of 100% of conversations and found churn risk in 3% of them. Custom classifiers track onboarding friction, product gaps and competitor mentions.[[14]](#src-14) - **Klaviyo** went from reviewing fewer than 2% of its 600,000+ annual support incidents to analyzing 2.5 million conversations that had never been looked at. It analyzed a full month of product-launch conversations in two hours, built churn risk classifiers for new conversations, and reports a productivity gain of about 40% in under 12 months.[[15]](#src-15) - **Checkr** combined Rippit with Snowflake operational data to find churn signals, and says its time from insight to action went "from weeks to hours."[[19]](#src-19) ## When should you choose Clay, Rippit, or both? **CHOOSE** ### Clay You need to find and enrich companies and contacts, keep CRM records current, and automate outbound or GTM workflows. **CHOOSE** ### Rippit You want to know which accounts and deals to act on from what customers and prospects actually said, next to the pipeline and warehouse data you already trust. You also want agents your team sets up to bring each new signal to the account owner. **CHOOSE** ### Both You want conversation signals such as churn risk and the traits of your healthiest customers to decide which accounts your GTM team targets and how it approaches them. ## Frequently asked questions ### Is Rippit a Clay alternative? No. Clay enriches prospects and accounts with external data.[[1]](#src-1) Rippit turns what your customers and prospects say into structured data, next to your CRM and business data.[[2]](#src-2) Teams use them for different jobs. ### Does Rippit analyze 100% of conversations? Yes, every conversation in the scope you choose, with nothing sampled.[[2]](#src-2) ### Can Rippit find churn risk? Yes. Churn signal is one of the dimensions Rippit's AI classifies,[[7]](#src-7) and Brex found churn risk in 3% of its conversations.[[14]](#src-14) ### Is Rippit cheaper than Clay? They price differently: Clay sells monthly plans for data and actions,[[11]](#src-11) and Rippit bills usage from one credit balance, with storage at $1 per GB a month and AI models at list price.[[10]](#src-10) ### Do both work with Claude? Yes. Rippit supports MCP,[[13]](#src-13) and Clay offers an MCP connector for Claude and ChatGPT.[[12]](#src-12) ### Is Rippit only for support? No. Rippit's customers use it for product feedback, churn analysis,[[15]](#src-15) coaching[[14]](#src-14) and chatbot monitoring.[[15]](#src-15) ### Can I trace a number back to the conversations behind it? Yes. In Rippit, every result is a field on a specific conversation,[[3]](#src-3) so you can audit any answer by opening the conversations and fields behind it. ### 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, rubrics and integrations carried over.[[20]](#src-20) 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).[[20]](#src-20) 1. Clay, homepage: [https://www.clay.com/](https://www.clay.com/) 2. Rippit, homepage: [https://www.rippit.com/](https://www.rippit.com/) 3. Rippit, How It Works: [https://www.rippit.com/how-it-works](https://www.rippit.com/how-it-works) 4. Clay, Waterfall Enrichment: [https://www.clay.com/waterfall-enrichment](https://www.clay.com/waterfall-enrichment) 5. Clay, Claygent: [https://www.clay.com/claygent](https://www.clay.com/claygent) 6. Rippit, Integrations: [https://www.rippit.com/integrations](https://www.rippit.com/integrations) 7. Rippit, Conversation Taxonomy playbook: [https://www.rippit.com/playbooks/conversation-taxonomy](https://www.rippit.com/playbooks/conversation-taxonomy) 8. Clay Docs, Sources: [https://university.clay.com/docs/sources](https://university.clay.com/docs/sources) 9. Clay Docs, Table columns: [https://university.clay.com/docs/table-columns-overview](https://university.clay.com/docs/table-columns-overview) 10. Rippit, Pricing: [https://www.rippit.com/pricing](https://www.rippit.com/pricing) 11. Clay, Pricing: [https://www.clay.com/pricing](https://www.clay.com/pricing) 12. Clay, "What Is Clay MCP? Clay in Claude & ChatGPT (2026)": [https://www.clay.com/guides/clay-mcp](https://www.clay.com/guides/clay-mcp) 13. Rippit, The Complete Guide to Conversation Analytics: [https://www.rippit.com/playbooks/the-complete-guide-to-conversation-analytics](https://www.rippit.com/playbooks/the-complete-guide-to-conversation-analytics) 14. Rippit, Brex customer story: [https://www.rippit.com/customer-story/brex](https://www.rippit.com/customer-story/brex) 15. Rippit, Klaviyo customer story: [https://www.rippit.com/customer-story/klaviyo](https://www.rippit.com/customer-story/klaviyo) 16. Rippit first-party research: AI analysis of 2,345 Rippit sales conversations (2025–2026), run on Rippit's own conversation analytics platform. Clay was discussed as a tool in use by 5 prospects across 2,345 conversations. 17. Claude Help Center, Get started with custom connectors using remote MCP: [https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp](https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp) 18. Claude Docs, Context windows: [https://platform.claude.com/docs/en/build-with-claude/context-windows](https://platform.claude.com/docs/en/build-with-claude/context-windows) 19. Rippit, Checkr customer story: [https://www.rippit.com/customer-story/checkr](https://www.rippit.com/customer-story/checkr) 20. Rippit, "MaestroQA is now Rippit": [https://www.rippit.com/news/maestroqa-is-now-rippit](https://www.rippit.com/news/maestroqa-is-now-rippit) 21. Rippit, The Coverage Benchmark, Part 8: Rippit: [https://www.rippit.com/research/coverage-benchmark-rippit](https://www.rippit.com/research/coverage-benchmark-rippit) 22. Rippit docs, Pipelines: [https://docs.rippit.com/pipelines/overview](https://docs.rippit.com/pipelines/overview) Build your first agent on your customer and business data. ## Getting started from an AI agent There are two ways to connect. Either works. - MCP server: `https://mcp.rippit.com/mcp` (Streamable HTTP, OAuth). The person signs in or signs up in the browser that opens. No client ID, secret or API key is needed. - CLI: install with `curl -fsSL https://rippit.sh | sh` or `brew install adtribute/tap/rippit`, then get an account one of two ways: - `rippit login`: the person signs in or signs up in a browser. - `rippit start`: creates a temporary account with no email or browser, for a person with no Rippit account. `rippit claim --wait` lets them keep it; an unclaimed account is deleted after 14 days. Then bring in data: connect a provider such as Zendesk, Intercom, Gong or Salesforce, or upload a CSV. Full steps: https://www.rippit.com/setup.md