Rippit vs. Gumloop for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both
Short answer: Gumloop helps teams build general-purpose AI agents that use their tools and data to do real work,[1] across 300+ connectors like Slack, Gmail, Salesforce, HubSpot and Notion.[2]
Rippit is built for one job: analyzing and acting on every customer conversation at scale.[3] It reads every conversation, turns what it finds into structured data you can count and trend,[4] and runs AI agents that monitor, analyze and act on that data.[3]
Gumloop answers "what should happen next, and can an agent do it?" Rippit answers "how many customers, why, and is it getting worse?" Rippit is self-serve with a free plan.[5] Gumloop's Pro plan starts at $37/month, with a 14-day free trial.[6]
Where Gumloop and Rippit overlap is in GTM and Customer Experience analysis and Agents.
Rippit vs. Gumloop at a glance
| CRITERIA | Gumloop | Rippit |
|---|---|---|
| What is it? | AI agent platform: build agents that use your tools and data, then automate them with triggers, schedules and the API[1] | AI conversation analytics and agents for every team[3] |
| Core job | Automate work with agents that decide which tools to use and when[7] | Analyze every customer conversation and turn it into structured, countable data[4] |
| Starting point | Your company's tools and knowledge across 300+ connectors[2] | The conversations you already have: tickets, calls and chats,[8] plus the survey data tied to them |
| Best fit | Teams automating work across tools, such as support agents that triage tickets and roll up feature requests[9] | Large-scale analysis and agents across every conversation your company has, with one dataset serving product feedback, revenue, CX and support operations, customer success and churn risk, and AI agent monitoring[10][11] |
| Question type | "Triage this ticket and create the bug report." "Send this week's digest of recurring complaints." | "How many customers hit this issue, why, and is it getting worse?" |
| Output | Actions in your tools, plus answers and digests; Company Brain searches and cites what it finds[12] | Structured fields on every conversation, plus the counts and trends built from them[13] |
| Conversation analysis | Agents work with Zendesk tickets and views through connectors,[14] and Company Brain indexes Zendesk tickets for search[12] | The core product. AI analyzes every conversation, with nothing sampled[3] |
| How teams use the data | Triage and ticketing, weekly pattern digests and feature request roll-ups[9] | Describe the insight you want, and Rippit adds it as a field across every conversation (root cause, churn risk, resolution).[13] Reuse it in dashboards, reports and agents |
| Customization | Agent builder with 35+ models,[6] 300+ connectors[2] and your own remote MCP servers[15] | AI tags every conversation across 10+ dimensions, such as intent, root cause, churn signal and resolution[13] |
| What gets scanned, analyzed and acted on | Each agent task decides which tools to call, up to 200 steps per task;[7] Company Brain returns the most relevant snippets for a search,[12] 8 results by default and up to 50[16] | 100% of conversations in scope, with nothing sampled[5][3] |
| How it handles large datasets | Legacy workflows can loop an agent over a list, sending each item as a separate message, at 3 credits per run plus the agent's own cost.[17] A task can run up to 10 concurrent subagents,[7] and Pro allows 25 concurrent agent interactions[18] | Stores every conversation as structured, queryable data[4] that agents and people can enrich and aggregate across the full dataset |
| Traceability | Task history shows who started each task, where it came from and what it cost;[19] Brain answers cite their sources[12] | Every result is a field on a specific conversation,[4] and each analysis step is documented, so any answer can be audited |
| Repeatability | Scheduled, app and webhook triggers, including real-time Zendesk triggers[20] | Turn any analysis or action into a repeatable Agent App that runs itself[3] |
| Setup | Self-serve, with a 14-day free trial of Pro[6] | Self-serve. Connect Zendesk, Intercom, Gong and Granola in one click[8] and go live in minutes[3] |
| Works with Claude | Runs Claude among 35+ models,[2][6] and agents can be exposed as MCP servers[7] | MCP Server so Claude, Codex, Cursor, and more can query your conversation data directly |
| Using both | Keep Gumloop for agents that take action across your tools | Add Rippit for the metrics: how often each issue happens, why, and the trend |
| Public pricing | Pro from $37/month with 20k credits a month and unlimited seats; custom Enterprise[6] | Free (includes $100 in AI credits); Starter $185/mo; Growth $495/mo; custom Enterprise[5] |
| Best starting question | "Which task could an agent take off our team's plate?" | "What's actually driving our CSAT, including for customers who never answer a survey?" |
What's the difference between Rippit and Gumloop?
Gumloop builds agents that do work across your tools and is optimized for automation. Rippit measures what's happening across every customer conversation and is optimized for conversation data analytics and deploying agents on findings.
Gumloop is an AI agent platform.[1] Its agents decide which tools to use and when, adapting to the task in front of them,[7] and they can run in Slack, Microsoft Teams, email, hosted pages, the API or as MCP servers.[7] It works well when a team wants an agent to triage a ticket, file a bug or send a weekly digest.[9]
Rippit is AI conversation analytics and agents, built for scale.[3] It analyzes every sales, support, success and chatbot conversation[3] and turns what it finds (intent, root cause, sentiment, churn signal, resolution) into fields you can filter, combine, trend and build on.[13] Agents then monitor those fields, analyze them and act on what they find.[3] Product, CX, CS and leadership all work from the same data.
Take a spike in refund complaints. In Gumloop, an agent picks up each new refund ticket, tags it and posts a summary to Slack. In Rippit, a support leader sees how many refund contacts came in this month, whether that's rising, which policy change is driving them, and which customers are at risk. The first is an action. The second is a number you can put in front of a product team.
Can you use Rippit and Gumloop together?
Yes. They do different jobs, so they don't overlap much. Keep Gumloop for general-purpose agents to take action across your tools.[1] Add Rippit to turn customer conversations into metrics and agents that operate at scale.[4]
Connect Gumloop agents to Rippit via MCP. Gumloop lets you add your own remote MCP servers as connectors,[15] so a Gumloop agent can pull Rippit's conversation metrics into its tasks.[21]
Doesn't Gumloop already analyze support tickets?
Gumloop's Call Analysis Agent also clusters objections across calls and shares trend digests in Slack, Teams or email.[22] Scale is set by the agent's limits: up to 200 steps and 10 concurrent subagents per task,[7] and on Pro, requests beyond 25 concurrent interactions are rejected.[18] Gumloop doesn't document a place to store a label on every conversation and query it later.
That's the right design for automating work. A reliable rate needs something else. "What share of this month's tickets involved a billing error?" requires every ticket classified the same way, stored, then counted. That's what Rippit does.
How does Rippit analyze every conversation?
Rippit runs an AI engine built to label conversations in bulk: AI enriches every conversation[3] and stores the results as structured, queryable data,[4] so analysis and agents can work across all of it. Rippit says it can analyze 10,000 conversations in one minute for under $1.[3] Questions then run on those stored fields across the full dataset, instead of rereading transcripts each time. For open-ended questions no existing field answers, Rippit also runs ad hoc deep dives[4] on up to 10,000 conversations at a time, and what they find can feed new analysis and agents.
That changes three things:
Do Rippit and Gumloop work with Claude?
Yes, in different ways. Gumloop runs Claude among its 35+ models,[2][6] and its agents can be exposed as MCP servers for other AI tools.[7] Rippit supports MCP, so Claude can query your conversation data directly.[21]
Scale is why the analysis layer matters. A general-purpose assistant can only hold a limited amount of text in its context window at one time,[23] so it can't read thousands of tickets in one pass. Rippit runs the analysis across every conversation and gives Claude the results, so every number traces back to specific conversations.
How much do Rippit and Gumloop cost?
Rippit publishes its pricing. Paid plans bill AI credits at cost.[5]
Gumloop also publishes its pricing. Pro starts at $37/month with 20k credits a month, unlimited seats and a 14-day free trial, and Enterprise is custom.[6] Additional credits cost $0.005 each, and credits don't roll over month to month except on Enterprise plans.[24]
Because the products do different jobs, compare each against its own use case: Gumloop against the tasks you want agents to run, Rippit against the conversation volume you want analyzed.
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. When the CEO asked a late-night question, the team had a report by morning.[25]
- Brex replaced manual QA sampling with AI review of 100% of conversations and found churn risk in 3% of them. It uses custom AI classifiers for onboarding friction, product gaps and competitor mentions.[11]
- 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 and reports a productivity gain of about 40% in under 12 months.[10]
When should you choose Gumloop, Rippit, or both?
You want anyone at your company to build agents that take action across your tools, with a choice of models.
Your most important customer signal is in customer conversations, and you need counts, rates and trends rather than one-off actions.
Gumloop already automates your team's work, and your leaders still can't say how many customers hit a problem, why, or whether it's getting worse.
Frequently asked questions
Is Rippit a Gumloop alternative?
No. Gumloop is a platform for building agents that automate work.[1] Rippit is conversation analytics.[3] Most teams that need both use both.
Does Gumloop connect to Zendesk?
Yes. Gumloop agents work with Zendesk tickets and views,[14] Zendesk can trigger agents in real time,[20] and Company Brain can index Zendesk tickets for search.[12]
Can Gumloop tell me what percentage of tickets mention an issue?
You can build toward that by looping an agent over a list of tickets in a legacy workflow,[17] then storing and counting the results yourself. Rippit classifies every conversation and stores each result as a field, so percentages and trends come from the full dataset.[3][13]
Does Rippit analyze 100% of conversations?
Yes, every conversation in the scope you choose, with nothing sampled.[3]
Is Rippit cheaper than Gumloop?
Rippit has a free plan and paid plans from $185/month.[5] Gumloop's Pro plan starts at $37/month, with a 14-day free trial,[6] so compare against the scope you need.
Do I need keyword rules to use Rippit?
No. AI classifies conversations across 10+ dimensions, and new categories apply to past conversations in one run.[13]
Can a Gumloop agent use Rippit data?
Yes. Gumloop lets you connect your own remote MCP servers,[15] and Rippit supports MCP.[21]
Can I trace a number back to the conversations behind it?
Yes. In Rippit, every result is a field on a specific conversation,[4] 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.[26] 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).[26]
- Gumloop Docs, Introduction: https://docs.gumloop.com
- Gumloop, homepage: https://www.gumloop.com/
- Rippit, homepage: https://www.rippit.com/
- Rippit, How It Works: https://www.rippit.com/how-it-works
- Rippit, Pricing: https://www.rippit.com/pricing
- Gumloop, Pricing: https://www.gumloop.com/pricing
- Gumloop Docs, Agents: https://docs.gumloop.com/core-concepts/agents.md
- Rippit, Integrations: https://www.rippit.com/integrations
- Gumloop, Support agent use cases: https://www.gumloop.com/use-cases/support-agent
- Rippit, Klaviyo customer story: https://www.rippit.com/customer-story/klaviyo
- Rippit, Brex customer story: https://www.rippit.com/customer-story/brex
- Gumloop Docs, Company Brain: https://docs.gumloop.com/core-concepts/brain.md
- Rippit, Conversation Taxonomy playbook: https://www.rippit.com/playbooks/conversation-taxonomy
- Gumloop, Zendesk integration: https://www.gumloop.com/mcp/zendesk
- Gumloop Docs, Custom MCP servers: https://docs.gumloop.com/nodes/mcp/custom_mcp_servers.md
- Gumloop Docs, Brain search API: https://docs.gumloop.com/api-reference/brain/search.md
- Gumloop Docs, Agent node: https://docs.gumloop.com/core-concepts/agent_node
- Gumloop Docs, Rate limits: https://docs.gumloop.com/core-concepts/rate_limits.md
- Gumloop Docs, Agent performance: https://docs.gumloop.com/core-concepts/agent_performance.md
- Gumloop Docs, Agent triggers: https://docs.gumloop.com/core-concepts/agent_triggers.md
- Rippit, The Complete Guide to Conversation Analytics: https://www.rippit.com/playbooks/the-complete-guide-to-conversation-analytics
- Gumloop, Call Analysis Agent: https://gumloop.com/use-cases/call-analysis-agent
- Claude Docs, Context windows: https://platform.claude.com/docs/en/build-with-claude/context-windows
- Gumloop Docs, Credits: https://docs.gumloop.com/core-concepts/credits.md
- Rippit, Checkr customer story: https://www.rippit.com/customer-story/checkr
- Rippit, "MaestroQA is now Rippit": https://www.rippit.com/news/maestroqa-is-now-rippit
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