Build your edge, block by block
Rippit is the growth data platform built to maximize the insights your people and agents can produce. Each part of it is a building block. Scroll to stack all twenty, from a secure foundation up to work that keeps running.
The blueprint. Scroll to lay the first block.
Security
Everything else stands on this.
Security and data handling
Rippit acts only within the signed-in user’s permissions and the connections an admin has authorized. The same rules hold whether you use it directly, through an AI assistant, or in a pipeline that runs with nobody watching.
- Rippit does not train any AI models. Your data is used for inference only
- Data integrations are read-only, and connected MCP servers start read-only
- Credentials are encrypted at rest with AES-256-GCM
Connect your data
Every conversation and the business data around it, stored in one place as tables.
Data integrations
A data integration is a read-only connection to a system that holds your customer conversations. Rippit pulls them in on a schedule and stores them as tables you can query, build reports from, and run pipelines on.
- 15 sources, including Zendesk, Intercom, Salesforce, Gong and Slack
- The first sync backfills up to 90 days by default, and further on several sources
- No storage limit. Hot and cold storage plus compute is $1 per GB per month
Salesforce and Snowflake tables
Bring in the records your conversations never carry: accounts, opportunities, orders and usage. Each Salesforce object and each Snowflake table becomes a Rippit table you can query and join to conversations.
- Salesforce: pick up to 50 objects at a time, and cases become conversations
- Snowflake: up to 100 tables per connection, synced hourly or daily
- Your source data is never changed
Bulk API and files
No built-in integration for your system? Send its data with the Bulk API, or upload a CSV. It lands as tables you can query and run pipelines on, the same as integrated data.
- Conversations as JSON Lines, tables as Parquet or JSON Lines
- Sending a record again updates it instead of duplicating it
rippit uploadsends a CSV with no API token
MCP connectors
Rippit’s in-app agent is an MCP client. Add MCP connectors and it can reach Slack, Notion, Salesforce, Snowflake or any MCP server, in the same conversation where it is analyzing your tickets.
- An admin chooses the exact tools the agent can use
- Every connection starts read-only
- Pipelines use the same connections to deliver results
Generate insights
Ask a question in plain language and keep what Rippit builds to answer it.
Ask Rippit questions
Chat is where most work in Rippit starts. Ask in plain language and Rippit picks the data, runs the analysis, and answers with the work attached. It reads whole conversations, so you get what happened in the thread, not how someone tagged it.
- Follow-ups keep their context: “now split that by channel”
- Drop in images, PDFs, text files and CSVs
- Can search the web and reach your connected MCP servers
Artifacts you keep
Ask for a breakdown, a report or a table and Rippit builds it as a real artifact in your workspace rather than a wall of text. Charts, worksheets, canvases and pipelines appear beside the conversation and stay afterward.
- Add a column that classifies every conversation by theme, urgency or root cause
- Sort, filter and chart that column later
- The side panel lists the sources an answer was based on
Build pipelines
The docs call these the building blocks: steps you combine so any analysis keeps running.
Data source
Every pipeline begins with data that is already in Rippit, so there is nothing to export or prepare: integrated conversations, business data, or another pipeline’s output.
- A row filter limits it to the rows that matter
- Rows that don’t match are never processed, so they cost nothing
- A pipeline can also start from a schedule alone
AI column
Creates the column your data is missing. A model reads each row and the full conversation behind it, applies the judgment you describe, and answers in a typed column you can filter, count and chart.
- Reads every message in order, not a subject line or a summary
- Columns defined together share one model call per row
- Choice, true or false, number, text and list outputs
Formula
Calculates a column from other columns on the same row: a duration from two timestamps, a flag from a score. Use one whenever the answer is arithmetic or logic.
- Exact, with no AI cost
- The same formula language as worksheets
- Recalculates when a column it reads changes
Lookup
A conversation knows who wrote in, but not what they pay you or who owns the account. A lookup adds columns from another table’s matching rows, so every later step can use them.
- Matches on a shared value, such as an account ID or an email
- Returns one row, or several up to a limit you choose
- Matching uses no AI
Rollup
Turns many rows into the few you want to act on. On a schedule, it folds a window of rows into one row per account, agent, topic or period.
- Runs hourly, daily, weekly or monthly
- Exact counts, averages and totals
- A threshold keeps an alert silent when there is nothing to report
Agent run
Puts an agent to work on each rolled-up row. Where an AI column answers one question, an agent can investigate: it reads the row, uses tools, and writes a result.
- One agent session per row
- Can read and aggregate your tables and run external research
- Up to 20 rows at a time by default
External research
Some answers aren’t in your data at all. An agent can search the web, find people, and ask AI models such as ChatGPT a question, and save every finding as a row.
- Web search uses Exa unless you ask for another provider
- Work email and phone lookups run only when you ask
- Agent search records each model’s answer and citations
Action
An insight nobody sees changes nothing. An action puts each result where the work happens: a Slack post, an email, a new ticket, a CRM update, or any tool on a connected MCP server.
- Runs once per row, so one row per account means one message per account
- No double sends: each row is recorded as soon as it is sent
- Can hold messages until a delivery time you choose
Build, test, and run
You build a pipeline by describing it, in Rippit chat or from your own AI assistant. Rippit drafts the steps, and nothing runs until you have tested it, checked the cost and put it live.
- A draft processes no rows and sends nothing
- A test runs every step once on a sample, 200 rows by default
- Changes to a live pipeline stay drafts until you make them live
Connect your AI agent
Use every block beneath this one from the assistant or terminal you already work in.
Rippit MCP server
Connects your AI assistant to Rippit. Rippit hosts and manages the server, so there is nothing to deploy, and your assistant can only reach what the signed-in user could already reach.
- One endpoint:
mcp.rippit.com/mcp - An admin adds it once, then each user signs in individually
- Tools to query, classify at scale and build pipelines
Supported AI agents
Rippit works with any client that speaks MCP over Streamable HTTP with OAuth. The docs have setup steps for each one that has been tested.
- Claude, ChatGPT, Codex, Copilot Studio, Gemini, Cursor, Notion and more
- Editors and terminals need no admin step
- An unlisted client can register itself with just the server URL
Connect data through your assistant
Authorize a data provider and start its first import from a conversation with your assistant, then carry on with your question once a table is available.
- Zendesk, Intercom, Salesforce, Slack and Gong
- Passwords and keys stay out of the conversation
- Authorization links work only for the person who asked
Rippit CLI
Connect integrations, upload files and query your data from a terminal or an assistant that can run commands. An MCP connection is not required.
- One-line install:
curl -fsSL https://rippit.sh | sh rippit startcreates a temporary account you claim later- Add AI columns to a worksheet with
rippit data enrich
Your Edge
Rippit reads the full content of every conversation, not a sample or just tags, to produce more accurate insights for your business. With pipelines built on top, any analysis becomes work that keeps running.