Rippling Release Notes

AI-assisted scheduling that reads your workforce data

The schedule is one of the main ways hourly businesses control labor costs. Every shift decision affects coverage, overtime, employee experience, and payroll. The problem is that building an accurate schedule requires data that lives in multiple systems. Availability and time-off requests live in HCM. Overtime rules and labor costs live in payroll. Managers manually pull that information together before they can publish schedules, and by the time they publish, some of the data is already out of date. Today we're releasing rebuilt Rippling Time: AI-powered Scheduling, Timesheets, and mobile, all built on the same employee, job, policy, and payroll data that powers the rest of Rippling.

Rippling generates an AI-assisted schedule draft from your connected employee, job, and payroll data. Input a plain language prompt, a spreadsheet, or a photo of last week's schedule and get a publishable draft. This is not pattern-matching on last week's shifts. It is a schedule built from the availability, time-off, job codes, overtime rules, and labor costs that drive your staffing decisions.

One mobile app for employees and managers

Employees see their next shift, clock in with one tap, see schedule changes, swap shifts, and message managers. Managers see real-time attendance, handle approvals, and edit the schedule from their phone. One app for everyone on the team.

Timesheets that surface exceptions before the payroll deadline

Scheduled shifts and hours worked sit side by side, with missed clock-ins, overtime flags, wrong job codes, and break issues surfaced during review, when there's still time to fix them before payroll runs. Managers edit inline, approve in place, and see a full change history for each time entry. Approved time flows directly into Rippling Payroll, no export required.

One view of the full week

Rippling Time brings together who's scheduled, who's clocked in, who's on break, which approvals are pending, and which time entries need review, all in one place on both desktop and mobile. The decisions that shape hourly labor happen week by week, shift by shift. Rippling Time connects the scheduling, attendance, and payroll data managers need to build accurate schedules, run the week, and send time to payroll with fewer corrections.

See what's new in Rippling Time

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

Every business question starts with “what,” but the next one almost always starts with “who.”

Who booked the most deals last quarter? Which support managers have the fastest resolution times? Which engineering teams have the greatest code velocity? Which stores are drifting into overtime? Which employees were on this team when customer complaints spiked?

At first, these sound like questions about bookings, tickets, pull requests, store sales, time tracking, or CSAT. But the answer depends on knowing who the data is about: the employee, their manager, their team, their location, their role, their permissions, and what that all looked like at the time something took place.

This is where traditional BI infrastructure breaks down. Data warehouses are good at storing and querying data, and BI tools are good at visualizing it. But none of that solves the harder problem: connecting business data to the worker identities, org structures, permissions, and historical context needed to produce an accurate answer to a question about your company.

This is nothing new: people have always struggled to work with data that lacks a sturdy wrapper of context. But this issue is newly important because AI is essentially helpless without it.

For years, we've watched our customers struggle to marry the identity data from Rippling with their operational data using systems like Fivetran and Tableau in an attempt to enable useful analysis. But they often failed. First, it's inherently hard to export data while retaining its referential integrity; once flattened, it's just rows in a spreadsheet. As a result, it's hard to produce an analysis that matches the nuance of a given question, so in the end, customers settle for answers to simpler questions than the ones they began with.

Nearly three years ago, we set out to solve this problem for our customers, and today's launch is the result.

Introducing Rippling Data Cloud

Rippling Data Cloud is a new suite of products that aggregates data from across your company into Rippling, connects it to worker identity, and makes it available for analysis, visualization and action. It preserves and enriches data context to enable precise and accurate answers to your most important and nuanced business questions.

It's a complete data stack including data connectors, transformations, visualizations, AI-powered analytics, and even inbound Zero-Copy. It understands how all of that data relates to employees, managers, departments, locations, cost centers, permissions, and historical changes in your ever-changing business. That makes it possible to ask questions that traditional BI systems struggle to answer correctly.

Other options fall short

There are many ways to put AI on top of business data. Most fail because they do not understand the business context behind the data.

To illustrate the concept, consider this business question: how long has it taken new sales reps to close their first deal in each segment over the past four quarters? To answer it correctly, a system needs more than sales data. It needs to know when each rep joined, when they entered a quota-carrying role, which segment they belonged to at the time, who managed them, and which opportunities should count.

Short of asking a data scientist to do the heavy lifting, business users have a few options to answer this question.

Approach | Where it falls short

• General-purpose AI tools, like Claude or ChatGPT | MCPs are slow and usually restricted, and CSVs are always forked from the system of record. Because they lack governed definitions and field history, there's simply no way to compute an answer to the sales rep question. (They might confidently provide a wrong one, however.)
• AI inside a single vendor system, like Salesforce | These tools better understand their own data, but lack interfaces to third-party systems that expose the full picture around the organization. In the sales rep question, the AI likely treats the current org chart as static in time; this yields a misleading answer, because orgs always change.
• AI inside a data warehouse, like Snowflake AI | Configured correctly, a warehouse AI can query tables from across the business, but it has no privileged view of any particular class of data (sales, HR, etc). Those have to be modeled manually before the AI can answer people-related business questions correctly, including in our sales rep example. It's possible, but the juice isn't usually worth the data science squeeze.

Beyond the challenges of joining and interpreting data, these systems struggle with permissions and governance both in their ability to access data, and their ability to share their output.

Everything starts from worker identity

Rippling started as an HCM, which makes it uniquely capable of understanding identity data: who works at the company, whom they report to, what they can access, what team they belong to, where they are located, what they do, and how all of that changes over time.

But this data is useful far beyond HR. A GitHub pull request has an author. A Salesforce opportunity has an owner. A helpdesk ticket has an assignee. A point-of-sale transaction has a cashier. A device has an employee. A payroll run has workers, departments, locations, and managers attached to it. Once those records are connected to worker identity, business data becomes easier to analyze, easier to govern, and easier to act on.

Rippling Data Cloud uses that identity layer across the entire stack: data ingestion, cataloging, transformations, history, dashboards, AI, and custom applications.

What we're launching

Rippling Data Cloud includes every component needed to run a complete AI-powered BI stack from managed connectors up to visualization and collaboration. It's a just-add-water approach that simplifies data analysis for every user in your company.

Dashboards

Rippling AI generates charts and dashboards with trusted, reusable components and inspectable SQL from natural-language prompts. Users can also build classic dashboards with charts, filters, pivots, calculated fields, and saved views. BI is different inside Rippling because dashboards inherit the context of the platform. For example, a manager can see the same dashboard as another manager, but automatically scoped to their own team. A user can drill from a chart into the employees, devices, opportunities, tickets, or other records behind the number. Unlike dashboards in standalone BI tools, which are disconnected reporting artifacts, Rippling Dashboards become a live navigation layer over the business. Read the full article on BI and Dashboards.

Data Connectors

Data Connectors bring third-party business data into Rippling, preserving and enriching the context that makes it useful. Traditional ETL tools move data from one system to another, but leave teams to rebuild joins, permissions, metadata, object relationships, and worker identity mappings by hand. Rippling Data Connectors do that work automatically: they import data from systems like CRMs, support tools, finance systems, and other warehouses, then map that data into Rippling Custom Objects. That means a GitHub pull request, support ticket, sales opportunity, or point-of-sale transaction lands already connected to the right employee, manager, team, permissions model, and business context. The result is data that is immediately easier to analyze with AI, govern through Data Catalog, reuse in Transformations, and put to work in dashboards, workflows, and Custom Apps. Read the full article on Data Connectors.

Transformations

Transformations turns raw business data into governed, reusable datasets. Instead of letting every dashboard, SQL query, spreadsheet, or AI prompt define metrics slightly differently, Transformations gives companies a central place to encode the logic behind the metrics you use, like revenue, margin, store performance, customer risk, or whatever else matters to a given operation. Analysts can write SQL directly, business users can use Rippling AI to help define and refine logic, and the resulting datasets can be reused across Dashboards, AI answers, workflows, and Custom Apps. Read the full article on Transformations.

Data Catalog and Lineage

Data Catalog gives Rippling Data Cloud and Rippling AI a map of your business data. It is the central inventory for every data object in Rippling, including native Rippling data, data from Data Connectors, Transformations, and external warehouse data. For analysts, it makes data easier to find, understand, trust, and govern, with searchable documentation, lineage, usage metadata, and field-level permissions. For AI, it is even more important: the Catalog gives Rippling the context it needs to choose the right objects, fields, joins, filters, and business definitions when answering questions. Read the full article on Data Catalog and Lineage.

History

Object History lets Rippling Data Cloud answer historical business questions without projecting today's org chart backward. Most business analysis is really asking what was true at a specific point in time: whom someone reported to, what team they were on, when their role changed, what workflow ran, who approved a change, or which org structure applied when a metric moved. Object History makes that context queryable across Rippling, so reports, dashboards, Transformations, workflows, Custom Apps, and Rippling AI can reason from the actual historical state of the business. Although many business questions look like they're about revenue, payroll, support volume, or headcount, they're really questions about people in time: who did what, when did they do it, and what was true about the business around them at that moment. Read the full article on History.

Custom Apps

Custom Apps let teams build company-specific software on top of the data inside Rippling. Dashboards show you what's happening, but most business problems still require a process: an approval, an exception review, a payroll adjustment, a remediation workflow, or a record that someone needs to update. Custom Apps use the same data, permissions, workflows, and object model that power the rest of Rippling. That means a Salesforce opportunity, Brivo badge-in, Mindbody class record, or Litmos certification can become part of an application inside Rippling, not just a row in a report. Data can trigger workflows, route for approval, update records, stage payroll changes, and give teams a structured interface for the process itself. Read the full article on Custom Apps.

Snowflake Zero Copy

Zero Copy for Snowflake lets companies use warehouse data inside Rippling Data Cloud without building custom pipelines. Data from Snowflake can appear in Rippling as external objects, where it can be joined to worker identity, governed by Rippling permissions, surfaced in the Data Catalog, and used by Rippling AI, Dashboards, and Transformations. Your warehouse remains the source of truth, but Rippling adds the worker identity, org context, permissions, and history needed to answer business questions that depend on who did what, when, and where they sat in the business. Read the full article on Snowflake Zero Copy.

A new era for business intelligence

Rippling Data Cloud, together with Rippling AI, unlocks a new frontier of analytical capabilities. Answer questions about the who behind every what. Better understand your company's performance dynamics across sales, engineering, and operations using only a conversational interface. Rippling Data Cloud will instantly become a mainstay of every data-driven leader.

If you'd like to try it out — even if you're not a Rippling customer today — please contact us.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

What is Data Catalog?

As part of today’s Rippling Data Cloud announcement, we launched Data Catalog, which allows users to inspect, govern, and understand every data object in Rippling Data Cloud, across native Rippling data, connected apps, Transformations, and external warehouse data. This gives both analysts and Rippling AI the context they need to find the right data, understand what it means, and trace where and how it is used.

Data Catalog is the central inventory of every data object in Rippling Data Cloud, across native Rippling data, Custom Objects, Transformations, and connected external data. It is a critical component to enabling AI-driven data analysis, as it answers the questions:

• What data do we have?
• What does the data mean?
• Who is allowed to use the data?
• Where did the data come from?
• What workflows, views, dashboards, or derived datasets depend on the data?

Data Catalog turns Rippling Data Cloud into a governed, navigable data system for human users and AI.

Rippling AI needs a map of your data

When a user asks a question like “how much did we spend on recruiting this month?”, the hard part is not just generating a query. The hard part is knowing which version of “spend” the question refers to, which expense objects are authoritative, which vendors or categories count as recruiting, whether the answer should include invoices, card spend, reimbursements, purchase orders, or payroll allocations, and which fields the user is allowed to access.

In AI analytics, the first step often is mapping a natural-language question to the right objects, fields, joins, filters, grain, and business definitions. Field names alone are not enough. There may be many plausibly useful columns called amount, category, vendor, department, status, or date, each with different meanings. Rippling’s Data Catalog gives AI the context required to make the right selection: object descriptions, field definitions, usage patterns, lineage, relationships, permissions, and business semantics. That context is what allows Rippling AI to stop guessing around a warehouse schema, and instead reason over a governed map of your business.

More data, more problems

As your data estate grows across native Rippling data, third-party systems, and your warehouse, the challenge of simply locating, interpreting, and navigating permissions restrictions becomes massive. In addition to the overloaded column titles, multiple Transformations may exist, adding another dimension of confusion. Some of these Transformations may have been created by employees who have since left the company. Are they still running? How will you interpret what they’re calculating, if you can’t ask the creator? What reports or workflows depend on them? There are many problems that a well-architected central registry like Data Catalog can help solve.

One place for every object

The Data Catalog in Rippling Data Cloud is where every dataset in the system lives, regardless of origin. This includes native Rippling objects, Custom Objects from third-party systems, Transformations, and external objects from Snowflake via Zero Copy Query Federation. All are searchable and organized in a single interface. Human users can search and browse datasets by name, category, or keyword. They can pin frequently-used objects for quick access or browse by logical category, such as Finance, or Devices, or Store Locations, to discover what's available.

Every object and field can include plain-English documentation: what it means, when to use it, and how it relates to the rest of the business. For native Rippling data, many are populated out of the box — a huge leg up for AI analysis on Rippling. For custom data, it is pulled from the source, generated by AI, and can be added/adjusted by admins.

Usage metadata helps the Catalog track which objects are actually being queried in reports, dashboards, and workflows. This identifies what your org relies on versus what was created once and forgotten, which is useful for governance and prioritization.

Lineage from source to use

Data Catalog lets you click any object and see its complete lineage, from end to end: where the data originates (such as a pipeline or connector), how it's been transformed, and where it surfaces in reports or apps. Rippling AI can also directly answer questions about Lineage.

Lineage shows the end-to-end data flow and makes it easy to spot and fix issues

In many data stacks, metadata, lineage, permissions and usage are split across the warehouse, transformation layer, BI tool, and governance system. To consolidate them often requires purchasing yet another tool from yet another vendor. And you still have to traverse through multiple tools to eventually fix the issue. In Rippling, the complete data path is in one view and changing or fixing a pipeline doesn’t require leaving the system.

Data Catalog becomes the working surface for all data management:

• View and edit SQL: For objects derived from Transformations, you can view and edit the underlying SQL directly from the Catalog.
• Unified across every data type: Most platforms have separate schema browsers or catalog interfaces for different data types. In Rippling, native objects, custom objects, transformed objects, zero copy objects, and managed connector objects all live in the Data Catalog.
• Granular permissions management: From the Data Catalog, you can manage who has access to each dataset down to the field level. Permissions are tied to Rippling's role-based model and update automatically.
• Automated metadata enrichment: As the data estate grows, Rippling AI generates plain-English descriptions, surfaces representative sample values, and computes basic field statistics. For example, when a Data Connector brings in Salesforce data, the Catalog surfaces readable descriptions of each field, rather than a wall of cryptic API names.
• Tagging: Objects can be added to favorites or marked as verified to guide other users and AI on how best to answer a question.

The Catalog as a control panel

For the person responsible for the data estate, the Catalog is the operational interface for all of it. From a single object's Catalog entry, you can navigate directly to the pipeline feeding it, the Transformation shaping it, the reports consuming it, and the permission profiles governing it. It provides full lifecycle visibility without switching tools.

When a data change is planned, such as a new connector or schema update, the Catalog tells you what would break downstream before you make the change. When an audit requires demonstrating data access controls, the Catalog surfaces that directly.

The Catalog and AI make each other better

Data Catalog enables sophisticated field selection for Rippling AI. But it works the other way, too. As the data estate grows, AI automatically generates descriptions for new objects. This means the Catalog gets richer without requiring manual curation for every new dataset. Richer metadata leads to more accurate AI field selection, which leads to more correct answers and drives more usage, improving the Catalog further.

While the Data Catalog is a powerful data discovery and management tool, it’s also the layer that makes every other Rippling Platform capability more intelligent over time.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

What is Zero Copy for Snowflake?

As part of today’s Rippling Data Cloud announcement, we’re launching Zero Copy Query Federation for Snowflake, giving companies a direct way to bring business data into Rippling Data Cloud without having to replicate it. With Zero Copy, you can connect data you have already replicated from systems like sales, finance, support, product, and engineering to the employee data already in Rippling.

Zero Copy is a native data connection between Snowflake and Rippling Data Cloud. Data is continuously available as first-class objects inside Rippling, without the overhead of ETL pipelines.

Zero Copy is an alternative to using traditional Data Connectors, which offer different benefits. Whereas Data Connectors can bring in rich metadata from the source system itself, Zero Copy connections build context directly from the warehouse by analyzing query history, identifying which fields are actively maintained (or deprecated), and sampling real values from each table and field.

The benefits of Rippling-on-top

Using Rippling AI to analyze data via Zero Copy is superior to analyzing that data directly inside Snowflake. A generic BI/AI layer can query data, but it lacks the critical context of Rippling’s worker identities, permissions, historical employment context, and the semantic understanding of workforce fields. Marrying this with sales, finance, or support data allows you to answer real business questions that begin with “who.”

It also democratizes and governs access to data in your warehouse. HRBPs, finance partners, managers, and executives can ask natural-language questions that combine warehouse data with Rippling data without needing Snowflake credentials, schema knowledge, or to make a data-team request.

Other reasons this approach might make sense for your business include:

• Rippling can now connect to warehouse data in-place. With a zero copy connection to Snowflake, Rippling can query existing warehouse data without duplicating, migrating, or re-platforming it. These objects appear in the Data Catalog and can be used in reports, dashboards, transformations, and AI queries, just like native Rippling data.

• Business data is joined to the worker identities. Companies can connect revenue, product usage, support, finance, or engineering data to employees, teams, managers, departments, locations, and cost centers, which is foundational to good analysis.

• Context is enriched. Although Data Connectors do more to enhance the context of data inside Rippling, data available via Zero Copy still gets query-history context, sample values, and information about which fields are most or least populated.

• Rippling applies org-aware permissions to warehouse data. Access can be scoped by role, department, reporting line, or permission profile, and updates automatically as people change jobs or managers, even though the data remains in Snowflake.

The result is a new way to use the business data you already have: not as isolated tables in a warehouse, but as employee-aware, permission-aware objects inside Rippling Data Cloud. Your warehouse continues to be the source of truth for business activity; Rippling adds the worker context needed to understand the “who” questions.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

Custom Applications have been available on Rippling since 2025, but as part of today’s Rippling Data Cloud announcement, we’ve enhanced their capabilities by marrying them with Rippling Data Cloud.

What are Custom Apps?

Custom Apps are business applications built inside Rippling for company-specific processes. They use Rippling’s data platform, permissions, workflows, and third-party data as the foundation. A Custom App typically helps customers manage and automate business processes that might otherwise end up in spreadsheets, such as compliance management, employee attendance tracking, or vendor onboarding.

Your data shouldn’t stop at dashboards

Most data platforms are built to answer questions. Snowflake lets you store and query data. Looker and Tableau turn it into charts. Add an AI layer, and you can ask questions in natural language. But once you have the answer, what happens next?

Usually, the work leaves the data platform. You spot a problem in a dashboard, then switch to another system to fix it: HRIS, payroll, IT, finance, ticketing, procurement. The insight lives in one place, but the action happens in another.

That’s why Rippling Data Cloud is worth more in combination with Custom Apps. Once your business data is unified in Rippling, you can do more than analyze it. You can build applications on top of it: approval flows, exception queues, remediation workflows, audit processes, and operational systems that use the same data, permissions, worker identities, and automation as the rest of Rippling.

The building blocks of a Custom App

• Custom Objects are custom data models, defined by you. They come out-of-the-box with all of our platform features, like Workflows, REST APIs, Permissions, and Reports.
• Canvas Pages are drag-and-drop Custom App interfaces that use Rippling’s component library.
• Functions are code that runs on Rippling’s platform. Functions can be run from Custom App UIs, Workflows, or even our REST APIs. Call APIs, process data, or implement custom logic as needed to complete tasks.
• Workflows allow you to trigger multi-step processes from a data change, on a schedule, or manually. You can route approvals, send notifications, and update records. When external data lands in Rippling as Custom Objects, those objects are first-class workflow participants; a change in a connected system can now trigger a process in Rippling.
• Embedded dashboards put analytics inside the app rather than somewhere else in the system. See data as you do your work in context, not hours later after it syncs to another system.

How Data Cloud makes Custom Apps better

External data can trigger operational processes

When a deal closes in Salesforce, that data gets synced to Rippling via a Data Connector. From there, a commission is automatically calculated based on the Employee’s tenure, the amount gets staged in payroll, the manager gets notified, and finance approves it. Nobody copies data between systems. The workflow executes end-to-end, with human approval at every step that requires it.

Improved dashboards and reporting

Even Custom Apps made entirely on top of operational data you already have in Rippling benefit from Data Cloud’s new analytics capabilities. Observability and reporting are important pieces of any business process, and the new generation of BI and Dashboards improve this. You can use Transformations and Dashboards to add interactive analytics directly to your Custom App UIs.

What real companies have built

A national behavioral therapy provider replaced an entire process involving hours of daily manual work with a live compliance app in Rippling. Custom Data Connectors pull all training data from the Litmos LMS into Rippling, while transformations consolidate 30+ objects into a single record per employee, complete with certifications, training progress, and more. When an employee falls behind on training or certifications, a workflow inside the app flags it automatically. What previously required daily manual checks is now automated and always current.

An international fitness studio built a commission calculation app with class attendance data from Mindbody. Instructor commissions are auto-calculated based on class attendance, factoring in the correct pay rules, and staged directly in payroll. A manual, error-prone process that consumed hours every pay period is now automatic.

Come build more powerful Custom Apps with Data Cloud

Data Cloud plus Custom Apps makes Rippling a more complete platform for builders.

• Data Connectors let you bring in the external data your apps need.
• Transformations helps shape that data into usable application models.
• Dashboards add richer BI directly into the app experience.

And because all of it runs on Rippling’s workflows, permissions, identity, and automation, you can turn connected data into real operational applications—not just reports about the work happening elsewhere.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

As part of today’s Rippling Data Cloud announcement, we launched Transformations, which allows a company to standardize and control data definitions and reuse these components across their business.

What is Transformations?

Rippling Transformations is a set of tools that allows users to create governed datasets from source business data. Analysts can write SQL directly, and less technical users can work with Rippling AI to ask questions, refine definitions and logic, and save the results. Results can be shared to drive consistent data interpretation within your organization. This capability relies on Data Cloud’s underlying data management capabilities, like Data Connectors and History, and it can be accessed anywhere you do work with your data, like Dashboards, Custom Apps, or Rippling AI.

This capability is especially valuable for comparative analyses, such as comparing the bookings of two or more sales teams, or how their win rate changed over time. Analyses like these are only reliable if bookings and win rate are measured consistently over time. Transformations makes this easy by “locking in” a clear definition, and ensures that these definitions show up everywhere that analysts and business users are working with your data.

Transformations save business-specific definitions to ensure everyone gets a consistent answer

Transformations is more important than ever in the era of AI, because users are increasingly “free feeding” on analysis themselves. Without governed definitions, users will unwittingly run calculations with definitions that differ slightly from the prior run, because the AI quietly changed its opinion on how to calculate a particular value. And when two team members get in front of their boss to debate a decision, they’re arguing from calculations that are inconsistent. (Awkward.)

Data governance is better inside Rippling

Because Rippling Data Cloud is an all-in-one analytical environment, Transformations is more capable, and more readily available to all users. For these reasons, it enjoys broader adoption, which is the toughest part of driving data governance in your business.

Rippling-specific SQL functions in Transformations

Transformations support the standard SQL patterns analysts expect: joins, unions, window functions, conditional logic, and post-aggregation calculations. Rippling then extends SQL with functions that would be painful to recreate in a standalone warehouse or BI tool. And because all business questions eventually ask “who,” primitives about your organization turn out to be useful in almost every analysis.

The ORG() function lets you query reporting chains and org hierarchies directly. History functions like VALUEASOF() and DATEOFCHANGE() let you evaluate employee and org context as it existed at a point in time. Rippling even handles currency normalization, so analysts and AI agents do not accidentally aggregate values across currencies without the right conversion logic.

Consistent, automatic permissions

In a traditional stack, transformation logic and access control often live in different places. You model the data in one system, then recreate permissions in a BI tool or data warehouse. In Rippling, Transformations is governed by the same permissions model as the rest of the platform, so reusable datasets can respect the entitlements of a given user.

Outputs that can become operational

A Transformation does not have to stop as a table for analytics. It can write back to the warehouse, which means the result can trigger workflows, appear on data detail pages, and become the data layer for custom apps.

For example, some Rippling customers have set up their own logic for how restaurant tips should get pooled via Transformations, and then they are able to give every employee visibility into their own tip earnings in a Custom Application. That exact same data is used to automatically include those amounts in the next payroll run. Another customer used a Transformation to combine CRM, support, product usage, and account-owner data, then publish a Customer Risk object that drives dashboards, renewal workflows, and account review pages.

Practical applications of Transformations

A retail district manager can create a daily store performance dataset that combines point-of-sale data, scheduled labor, clock-ins, overtime, returns, and inventory exceptions. Without a Transformation, each district manager might calculate “sales per labor hour” slightly differently: one includes returns, another excludes manager hours, another forgets to adjust for missed clock-ins. With Transformations, those choices are defined once, so that every store and district looks at the same definition. Store managers can track performance for their own locations, district managers can compare across stores, and Rippling AI can answer questions from the same governed dataset. Because it lives in Rippling, the dataset can also trigger workflows when a store is trending toward overtime or missed-break exposure.

A telemedicine provider can create a capacity dataset that combines patient volume, provider schedules, credentialing status, and state licensure data managed in a Rippling Custom App. Without a Transformation, capacity planning often becomes a spreadsheet exercise where someone manually reconciles the supply of licensed providers against the needs of people in a given location. With Transformations, the rules are captured once: which appointment types count toward demand, which providers are eligible in each location, how cancellations affect capacity, and when a region should be considered under-covered. The resulting dataset can power dashboards, AI answers, and workflows that alert operations when new headcount needs to be opened or schedules need to be adjusted.

A professional services firm can create a project staffing and margin dataset that combines time tracking, billing rates, project budgets, employee skills, utilization targets, and PTO. Without a Transformation, every project review risks using a different definition of margin or availability: one team includes subcontractor costs, another ignores non-billable management time, another treats someone as available even though they are on PTO next week. With Transformations, those assumptions become shared logic. Leaders can inspect project health consistently, AI can answer staffing questions using the same definitions, and the output can feed a custom staffing app that helps managers assign the right people before projects fall behind.

Write the logic once

The definitions that matter most to your business should not live in a spreadsheet, a one-off SQL query, a dashboard formula, or a prompt that someone has to remember to reuse. Transformations give that logic a governed home: written with SQL, assisted by AI, enriched by Rippling’s employee graph and history, and available everywhere the business needs it.

That is the larger promise of Rippling Data Cloud. It does not just bring business data together. It gives teams a way to define what that data means, reuse those definitions consistently, and turn the result into action.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

What is Object History?

As part of today’s Rippling Data Cloud announcement, we enhanced the object history capability across Rippling, which makes AI-powered analysis of business data faster and more accurate. Some business questions are impossible to answer correctly from current-state data. Object History makes those questions answerable.

Object History is the ability to query the value of an object at any point in time, as well as when a given change occurred, who made it, who approved it, and why. When combined with Rippling Audit Logs and Rippling Workflow execution history, it provides a complete picture of how data has changed over time.

Today, Rippling Object History supports changes made to the Employee record, including custom fields. When combined with Data Connectors and Transformations, this allows analysts to evaluate connected business data, like sales, support, or engineering data, on the dimension of how the people associated with that data have changed through time.

What Object History enables

The most important business questions require an analysis of data over time. Although point-in-time analysis can be useful (“how much revenue was booked yesterday”), more valuable questions require accurate historical data across a set of fields (“show the bookings performance of each sales team over the past four quarters”). The latter requires the historical team membership for each salesperson who was active during the period.

These types of business questions abound:

• How long after first becoming an account executive did each rep close their first deal?
• Which managers are best at keeping support volume low, accounting for all the org shifts that have happened over time?
• Which managers in my org have the highest attrition rate?
• Which job sites have trended best and worst for late clock-ins so far this year?
• What are the average PRs per engineer over the past eight quarters, grouped by job level?
• Which employees were members of this team when we received the most customer complaints?
• How did headcount change by department, location, or manager over the last year?
• Which employees were included in this payroll run, and what department, location, or manager context applied at the time?

Without Object History, BI systems can return wildly inaccurate answers to these kinds of questions. The solution has been to pay a data scientist to model history data for a given query or report using manual snapshots of the data that attempt to mimic this capability. With Rippling, you get a bulletproof version of this capability out of the box, without rebuilding history models for every analysis.

History everywhere

Rippling treats each supported field as a time series, and saves metadata every time a field’s value changes. It then exposes history through functions that can be used where users actually put their data to work: reports, dashboards, transformations, AI answers, workflows, and custom apps.

The power is in the simplicity:

• VALUEASOF(field, date) returns the value of a field as it existed on a specific date.
• DATEOFCHANGE(field, "FIRST" | "LAST" | n, optional_filter) returns the date of a given change.

That makes history available in the same analytical layer where people build metrics. You do not need a special report type, a separate API call, or a hand-built history table for every analysis.

Critically, access is governed by the same permissions model that protects the underlying data. Historical analysis will not become a backdoor to sensitive data, such as employee compensation.

When history is exported from your business systems for processing, it quickly becomes fragmented. One team keeps Salesforce snapshots in a warehouse. Another exports org changes to CSV. A data scientist builds a custom, slowly-changing dimension for one dashboard. Each version may answer one question, but none becomes a governed, reusable understanding of what the business looked like over time. AI is then left to guess which version of history to trust, how it relates to current data, and whether the user is allowed to see it. Rippling makes history part of the platform instead, so people and AI can reason from the same permissioned record of what changed, when it changed, and what the business looked like at the time.

A different approach to the problem

When a manager is reorganized under a new VP, all employees under that manager get a new skip-level manager. But nothing in the records of the individual employees changes; only the reporting line of their manager. So when the time comes to ask the question, “who reported to whom before that change happened,” the answer isn’t an easy thing to compute.

Rippling solves this by centralizing history at the platform layer. Changes to supported employee fields and org relationships are captured with effective dates, then turned into queryable historical state. For hierarchy fields like manager, department, and team, Rippling can maintain point-in-time hierarchy paths, so reports and AI do not have to rebuild old org trees from raw change logs every time someone asks a historical question.

Where this changes the analysis

One very simple example of this issue is in evaluating the performance of newly-hired sales reps, something every sales team measures continuously. How would you calculate time to productivity?

We can define time to productivity as follows:

time_to_productivity = DATEDIFF(first_closed_won_date, rep_start_date)

The formula looks simple, but many reps started out as sales development reps, and later became account executives. In that case, hire date is the wrong starting point. The right question is how long it took to close the first qualifying deal after becoming an AE.

DATEOFCHANGE(job_title) gives you the role-change anchor. From there, a transformation can find the first qualifying opportunity after that date, attribute it to the correct manager at the time, and compute the ramp period.

Why this matters for AI context

AI is only useful if it can answer the question you actually asked. When a user asks “who was on this team when attrition spiked?” an AI layer on top of current-state data will answer quickly and confidently, but incorrectly. When a user asks “why did this metric suddenly change?”, an AI layer that cannot see business events, workflow activity, and audit logs can only guess.

Rippling AI can use the same history-aware functions, business events, and audit-log context available in Data Cloud. It can understand not just the row, but the context around the row: whom the employee reported to at the time, which department they belonged to, what workflow fired, what changed, who changed it, and what permissions apply. The answer is traceable to actual historical state and operational events, not inferred from today’s org chart or reconstructed from table names alone.

Data history should not be a research project

If reconstructing the facts from six months ago requires exports, old org announcements from Slack, workflow screenshots, audit-log exports, ETL logs, and a few colleagues with institutional memory, the analysis will either be skipped or quietly approximated. Rippling Data Cloud makes history part of the platform: point-in-time where the object supports it, change-aware where workflows need to react, event-native where the business record already represents what happened, and audit-aware where the question is who changed what and when. And it is available to reports, transformations, dashboards, AI, and workflows through the same system.

It improves the trustworthiness of any analysis you do with your business data.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

What are Rippling Data Connectors?

As part of today's Rippling Data Cloud announcement, we launched Data Connectors, which provide data import capabilities that exceed the standard of standalone ETL products. They automatically preserve and enrich data context, and they wrap your data in the powerful primitives of the Rippling platform. Let's take a look at what makes Data Connectors unique inside Rippling.

Rippling Data Connectors are integrations to third-party systems that move data into Rippling. Managed Connectors are integrations built and maintained by Rippling on a customer's behalf. Custom Connectors can be built and maintained by customers to integrate to any system that has a supported interface. Rippling Data Connectors also include Zero Copy, for easy integration with Snowflake, with support for Iceberg, BigQuery and other systems forthcoming. Lastly, customers can also manually import CSVs.

At launch, Rippling provides managed connectors to Salesforce, GitHub, Square, Greenhouse, and many more.

Automatically preserve and enrich context

Traditional ETL products lift and shift data from a business system into a data warehouse. But data models, joins, permissions, and metadata must be handled manually on the other side before the data becomes useful for analysis.

Rippling Data Cloud automatically preserves worker identity associations across a customer’s data. When external data lands in Rippling, the software identifies fields that reference users: email addresses, employee IDs, usernames, and display names. It joins them to the corresponding Rippling identity profile, so that analysts and business users don’t have to configure those joins themselves. It uses the same identity-resolution technology that powers Rippling IT’s Identity and Access Management software across the hundreds of business software systems our customers use.

Raw data is enriched with additional context about the domain, the source, and the actual data that is imported per customer account

Data Cloud similarly maintains references between objects within the third-party system, like the link between a support case and its comments, or a sales opportunity and its parent account.

Permissions are automatic, even as your organization changes

Permissions are powered by identities and the relationships among those identities: who reports to whom and who is a member of what department. Almost all business data has a worker identity association: GitHub PRs have an author, point-of-sale transactions have a cashier, helpdesk tickets have an assignee.

Inside Rippling, employees can see their own pull requests, transactions, or tickets, and all managers can automatically see that same data for their team. When teams inevitably reorganize, permissions automatically adjust so the right people have access to the right data. In essence, the system manages data permissions “for free.”

Data arrives with context

When data crosses system boundaries, it almost always sheds context. A CRM opportunity becomes a row. A Jira ticket becomes a row. A candidate record from an ATS becomes a row. The data may arrive, but the meaning around it often gets stripped away: what the object represents, how it relates to other objects, which fields matter, and how the source system expects the data to be used. But that context is exactly what AI needs to answer questions correctly.

Rippling Data Connectors are designed to bring in source-system context, not just rows of data. For a HubSpot connector, for example, Rippling can read the HubSpot API docs, understand the data contract, and derive how HubSpot objects relate to each other and how they should map into Rippling before the connector is generated. That means a Deal is not treated as a flat row. It can be understood as something connected to Companies, Contacts, Orders, Products, and lifecycle events, so Rippling can preserve the commercial context around the deal rather than importing only its raw data.

Once the data lands in Rippling, that source-specific context is enriched with what Rippling learns from the customer’s own data: the fields they import, custom fields and objects, known relationships, field descriptions, usage patterns, and signals like sparse or stale fields. So when someone asks why one segment has higher win rates than another, Rippling AI starts with connected sales objects, not just a pile of column names.

This context also flows into Data Catalog, where teams can discover connected objects, inspect field descriptions, understand relationships, and see how data is used across Dashboards, Reports, AI, and Transformations.

From rows in a table to highly capable data objects

Data imported into Rippling benefits from the capabilities we’ve built around first-party application data for the last ten years. It works with Custom Applications, can be analyzed in reports and dashboards, and can trigger workflows. For example:

• A sales manager can browse Salesforce opportunities inside Rippling, and open related records like the owner, account, and related activity without jumping between systems.
• An Engineering manager can analyze the cost in AI tokens per GitHub pull request across their team, compare across employees, and identify opportunities to cut costs.
• When a Zendesk case for a strategic customer is escalated to Sev-1, Rippling can notify the support agent’s manager, alert the account owner, and create a follow-up task for the product team.

Every custom object gets a fully customizable detail page view for a richer app experience

Includes the full foundations of ETL

No data solution would be complete without delivering on the fundamental capabilities of ETL. Data Connectors allow you to:

• Configure exactly which tables and fields you want to import into Rippling
• Perform incremental syncs with automatic rate limit throttling
• Adjust sync schedules, so you can control how fresh your data is
• View a detailed history of every data sync, including what succeeded or failed down to the record and field
• Store credentials securely
• Automate schema updates so that when fields change or new ones get created in your source system they’re available in Rippling on the next sync

Because Rippling provides end-to-end Lineage, tracing issues to specific connectors, syncs, and owners is a tractable problem.

Custom Connectors

In addition to the Managed Connector library provided by Rippling, Custom Connectors allow any customer to import data from APIs for any business system they use. Custom Connectors run on Rippling infrastructure, so no third-party service is necessary. They include standardized concepts like pagination and incremental syncs to make the system efficient, with observability that’s on par with Managed Connectors. Customers can set them up directly in the UI or partner with a Forward Deployed Engineer to build out an entire Rippling Solution.

Conclusion

Rippling Data Connectors make it easy to bring your operational data into Rippling and join it on worker identity, which unlocks the power of Rippling AI and Rippling Dashboards. They simplify data integrations, enhance data context, and preserve lineage. The system delivers automatic, hierarchy-based permission management that updates as your team evolves, while enabling Rippling AI to intelligently interpret and join data without manual configuration. They’re the backbone of upgraded analytical insights for your business with Rippling Data Cloud.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

As part of today's Rippling Data Cloud announcement, we launched Business Intelligence (BI) and Dashboarding that connects your operational data to your people data to unlock new analytical capabilities. Let's take a look at what makes BI and Dashboards unique inside Rippling.

What is it?

Rippling BI and Dashboards are the analytical charting and dashboarding capabilities that allow users to visualize data that is available to Rippling, either because it originated there, is accessible via Zero Copy, or was imported using Rippling Data Connectors and Transformations. It also includes Rippling AI’s ability to autonomously design and render Dashboards based on your questions.

Traditional BI and dashboarding systems start with a blank canvas and ask users to assemble charts manually. Rippling supports the dashboarding components experienced users expect, but adds an AI-first path: describe the analysis you want, iterate conversationally, and turn the result into a dashboard your team can keep using. All Rippling dashboards automatically obey the permissions inside Rippling, which means that users can share dashboards that are automatically scoped to the viewer. Each manager sees the dashboard with data for only their own team.

AI-generated, but with verifiable SQL

Rippling AI generates a sophisticated SQL query in response to each data query. It understands the Rippling schema, with org-aware and history-aware functions to make it far more capable. Every result is traceable to the exact query it ran, and which records it touched. If an answer looks wrong, you can inspect the SQL.

Rippling AI can handle queries that normally require a data analyst: complex joins across multiple datasets, conditional aggregations, and time-series analyses with historical context. Based on your prompts, it generates the query, configures the chart, and places it on a dashboard. For teams with SQL expertise, the generated query is inspectable and editable. This is a critical capability: when a dashboard influences a personnel decision, payment authorization, or device action, “the AI said so” is not enough.

Rippling also offers some time-saving tricks. For example, you can upload a screenshot of a chart from anywhere (a board deck, or a report PDF) and Rippling AI will recreate it using your own data. Or, you can ask what you should be paying attention to (e.g., “What belongs on a quarterly board dashboard for a 300-person company?”) and it recommends and then builds the reports for your review.

[fig. 3] 2 AI Gen Dashboard V499
Dashboard generated by combining revenue data from POS and Employee/business data from Rippling. Actual product experience. Sped up for brevity.

The Rippling architecture transforms the BI experience

Unlike standalone BI tools, a Rippling Dashboard is a navigation layer over your operational data, not just a reporting artifact that’s disconnected from it. That’s because data in Rippling benefits from full worker identity around your data. Permissions are therefore governed by your org chart. You can, for example, share reports that inherit permissions scope for the users viewing them — all the way down to the individual records.

Org-Aware Filters

Filter any dashboard to “my direct and indirect reports” and it works, adjusting automatically as your org changes. Each manager who views the same dashboard sees their own data. Sunburst charts and hierarchy visualizations map to your actual department and manager structure, adding or removing layers as the org grows or contracts. Use it to view resolution times in Jira, to study trends in NPS for your support department, or to see retail sales by team and location.

Org-Aware Permissions

In Looker or Tableau, permissions are managed manually. When someone changes roles, increases their scope, or otherwise needs their permissions updated, it’s done by the dashboard’s owner.

In Rippling, permissions are derived. Build the dashboard once, and every viewer gets a correctly-scoped, personalized version that updates automatically as the org changes. Rolling out dashboards to any number of managers requires no per-user access review.

The impact is that your data team no longer needs to manually administer a fleet of constantly-changing dashboards, and their permissions configurations. Managers can rely on their data access to remain reliable.

[fig. 10] OrgAwarePermissions V499
Build a dashboard once and share it with multiple people. Each store manager only sees the data for their location.

Drill-throughs to actionable records

Because Rippling BI is referring to data that maintains its context, users can drill down to the raw underlying data. And it doesn’t stop at a table of rows. Every record links directly back to the source: an employee profile, a field device asset, or a sales record.

For example, if a chart shows that pipeline coverage is slipping in the West region, it’s painful to then export the opportunity rows and rebuild the context somewhere else just to understand why it’s slipping. Instead, simply drill into the underlying opportunities, see the account owners, understand the manager and territory context, and inspect the records that explain the movement. This is why BI inside an operational software system like Rippling is different from BI inside a BI system like Tableau: the chart is connected to records that still have their context and meaning, which is available to the user.

[fig. 4] Drilldown V499
See the underlying records of any chart, and continue exploring through customizable object pages. Actual product experience.

It’s hard to overstate the value of data that remains actionable, traceable and inspectable, even after it has been processed by your BI tool.

Analysis and action, in one system

Because so many business processes across HR, IT and Finance live inside Rippling, analysis done inside Rippling Data Cloud is immediately actionable in many cases. In a standalone BI system, results might drive actions, but those actions must be taken manually. Often, that means looking up records, copying and pasting them into another system, and then taking some action, which is cumbersome and error-prone.

For example, a user has run an analysis of devices in the field, and identified some that are compromised or orphaned. She wants to immediately remote-wipe those devices. Rather than exporting that list and moving it to another system, with Rippling she clicks directly into those devices and takes the appropriate actions. She can see data about related entities, like the device’s owner. Her analysis and her actions live in a single system, with a closed loop.

Comes with a classic BI foundation

Rippling BI is designed to stand on its own as a general-purpose dashboarding product, not just a set of prebuilt reports. Teams can build dashboards with the core visualization and analysis primitives they expect from modern BI:

• bar, line, area, and scatter charts
• pivot tables
• hierarchy views like sunburst charts
• conditional formatting
• global and conditional filters
• calculated fields and custom metrics
• saved views
• rich text widgets for context
• caching for fast loading and drilldowns
• exports for sharing visuals

One more reason to care: when you have all of this on top of Data Cloud, you no longer need to pay for separate BI seats. Managers across your business can access this tooling directly. And Rippling automatically joins all of this data to worker identities, a critical pivot point in any analysis.

Easy to try alongside your existing stack

Rippling BI combines full business intelligence capabilities with the operational context behind the data, making analysis easier to trust and act on, while giving teams a faster path to dashboards that drive decisions.

If you’ve already invested in a mature data stack that serves multiple business functions, Rippling Data Cloud doesn’t require dismantling it. Snowflake Zero Copy means your warehouse data participates in the same analytics layer, joined with Rippling operational data, governed by the same permission model, and queryable by Rippling AI with full org context.

If you’re maintaining ETL pipelines into a BI tool primarily to get dashboards that pivot on worker identity, or relying on spreadsheet exports because proper analytics setup was never prioritized, try Rippling Data Cloud instead. The permissions problem doesn’t exist when the analytics live where the data originates. And the insight-to-action gap closes when the dashboard and the operational system are the same thing.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

Today, we’re launching Business Banking

It includes a high-yield checking account¹ that earns you 32x² more on your operating cash and makes same-day payroll possible through Rippling Payroll. Plus, it gives you everything you need to manage your money, safely and easily.

Already have a bank account?

Keep it. You can use ours¹ alongside it, then simply consolidate later if it makes sense. Either way, you'll enable same-day payroll and earn more on your operating cash — no more running payroll days before it's due, no more off-cycle runs when something changes at the last minute, and no more waiting days to correct a payroll mistake.

Don’t have a bank account yet?

Rippling makes it easy to set one up and earn industry-leading yield, in less than 10 minutes.

Payroll and cash, together at last

Why same-day payroll is so important

Most payroll systems make you process payroll 2-4 days in advance — which means by the time payday arrives, you've been managing it for nearly a week.

Business Banking changes that by enabling same-day payroll for your domestic employees through Rippling Payroll. Here's why that matters more than you might think:

Run payroll on your schedule

Running payroll on a 2+ day delay means you're always operating on two timelines — today's, and the one that needs to be locked in 48+ hours before payday. It's a constant low-grade tax on your brain. Picture it: it's 4pm Wednesday, you hired someone that morning, and the cutoff to pay them Friday has already passed. With same-day payroll, that mental overhead disappears — because the day you run payroll is the day your employees get paid.

Make changes right up to 1PM EST on payday

With 2+ day payroll, everything has to be locked before the window closes — and once it does, you're stuck. If something pops up after the cutoff — a last-minute spot bonus, an unexpected termination, a contribution change — you're looking at an off-cycle run or waiting until the next cycle. Same-day payroll gives you a longer window, so more changes make it in on time and fewer get pushed to the next run.

Correct payroll errors same day

If someone catches a mistake after payroll has already run — a wrong salary, a missed deduction — employees normally have to wait another 2+ days to be made whole. With same-day payroll, you can fix it the same day.

Earn up to 32x more from your money²

Most checking accounts pay you nothing on your operating cash. From legacy banks to modern fintechs, like Mercury and Brex—0% on operating cash. But why should you earn 0% just because you need cash on hand for day-to-day expenses, like payroll and bill payments?

With Business Banking, you’ll automatically earn 2.25% APY² on the cash in your Checking Account — 32x higher than the national average of 0.07% APY. On $500K, that's roughly $11,250/year in interest instead of ~$350.

For idle cash, your Investment Account can earn up to 3.50%³ through the JPMorgan U.S. Treasury Plus money market fund. No transfer limits. No gotchas.

You might be wondering how we can offer rates this good. The truth is, Business Banking is a loss leader for us. Most of our revenue comes from non-financial products, like our HR and IT software, so we can afford to subsidize the financial ones. We want businesses to start and grow on Rippling. But even if you never use another Rippling product besides Business Banking, that’s OK. You win either way.

Get up to $200M in FDIC-insured coverage⁴

Standard FDIC insurance covers $250K per depositor — fine for an individual, not fine for a startup or large business sitting on a year or more’s worth of runway.

With Business Banking, you get up to $200M in FDIC-insured deposit coverage on funds in your checking account.⁴ Put simply, a much better place to park your cash.

We also built controls that make it easy to manage your money safely: like money sweeps, custom approval workflows, and granular permissions. Plus essential accounting integrations, with ERPs like Quickbooks and NetSuite, so your month-end doesn't look like a crime scene.

If you’re a brand new startup, you can set up everything you need to run a business in minutes — all in one place

On Day 1, every founder needs the basics: a bank account and a corporate card. Rippling makes it easy to set up both in minutes, along with everything else you need to get up and running – including payroll, benefits, and PEO or EOR services to offload compliance.

And that’s just the start.

Within 12 months of opening a bank account, the average business has bolted on 7+ separate tools — like ADP, Ramp, BambooHR, Deel, and Okta.

With Rippling, it's all one platform: every Finance, HR, and IT app you'll ever need, connected to a single source of truth.

You can just start with Business Banking and Corporate Cards⁵, then turn on Payroll, Benefits, and more as you need them. No new vendors, no migrations — just click to add the next piece of your Finance & HR stack.

Getting started

You can learn more here or open your free account in minutes.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

(1) Checking Account: Rippling is a fintech company, not a bank or FDIC-insured depository institution. Checking Account and banking services provided by Column N.A., Member FDIC.

(2) The Annual Percentage Yield (APY) as advertised is accurate as of 6/15/2026. Interest rate and APY are subject to change at any time before and after the Checking Account is opened. Earnings compared and calculated based on the national average rate on interest checking accounts of .07% published by the FDIC as of 6/15/26.

(3) Investment Account: Rippling’s provision of access to securities products and brokerage services are offered by Apex Clearing Corporation. Apex Clearing Corporation is an SEC registered broker dealer, a member of FINRA and SIPC, and is licensed in 53 states and territories. FINRA BrokerCheck reports for Apex Clearing Corporation are available at BrokerCheck.

The Investment Account is not a deposit product, not insured by the FDIC, and may lose value. This is not an offer, solicitation, or recommendation to buy or sell any security. Rippling is not an investment advisor or broker-dealer and does not evaluate whether an investment in the fund is appropriate for you. You are making a self-directed decision regarding your investment activities. Before investing, carefully consider the fund’s investment objectives, risks, charges, and expenses in the fund’s prospectus here. Yield rate shown is JPMorgan U.S. Treasury Plus Money Market Fund (IJTXX)'s current 7-day effective annualized rate as of 6/15/2026.

(4) Deposits in Checking Accounts are FDIC-insured through Column, N.A., Member FDIC and Column’s Sweep Program Network Banks. FDIC deposit insurance covers the failure of an insured depository institution. Certain conditions must be satisfied for pass-through FDIC insurance to apply.

(5) Corporate Card: The Rippling Corporate Card is issued by Fifth Third Bank, N.A. Member FDIC, subject to approval. Cards are issued pursuant to a license from Visa® U.S.A. Inc. Visa is a trademark owned by Visa International Service Association and used under license. All trademarks are the property of their respective owners. Rippling Payments, Inc.’s (NMLS No. 1931820) California loans made or arranged pursuant to a California Financing Law License.

Today, we're launching Rippling Automated Compliance, starting with SOC 2.

Most SOC 2 tools simply tell you what's wrong, but they can't help you fix it. They're detection systems bolted on top of tools they don't control, so every gap becomes a distracting side quest.

Rippling is different. We're not a reporting layer on top of your tools — we are the tools. Device management, identity and access, HR, performance management. So, most of your evidence is collected before you start. And when we find a compliance gap, we can actually close it.

That’s how Rippling helps you get a SOC 2 report, fast, without cutting corners.

Your SOC 2 evidence is already in Rippling

Most SOC 2 projects start with months of groundwork before a single piece of evidence is collected — standing up dozens of tools like an IdP, an MDM, a performance management system, then wiring them all into a compliance tool. That's dozens of vendors before you're even at the starting line.

If you're already on Rippling, you're already most of the way to being SOC 2 ready before you even begin. Nikolas Huebecker, a second-time founder who recently got his SOC 2 through Rippling, saw this first-hand.

“We were already compliant because of the way Rippling had us configure our systems. We just had to confirm it.

Nikolas Huebecker
Founder at stealth startup

The foundational data — employee device encryption, app access, security training, document signatures— already lived in one platform. Where a traditional compliance tool would require dozens of integrations, Rippling only needed three.

Rippling doesn't just flag issues, it fixes them

Every other SOC 2 vendor works the same way: detect a problem, alert your team, fix it elsewhere. That's not a product flaw. It's a structural limitation of any system that doesn't control the underlying tools.

When Rippling flags an issue, it takes you right to the fix. Unencrypted device? Encrypt it. Wrong app access after an access review? De-provision it automatically. Security training incomplete? Send a reminder and gate the employee's system access until it's done.

Maintaining compliance goes from a recurring scramble to something you knock out between meetings.

Audits don’t have to be a yearly fire drill

Staying compliant as your company changes — as people join, move, or leave — is where most tools fall apart. Rippling handles it automatically.

“You change one policy, and it ripples across the entire org right away. That's what it means to have compliance embedded into the systems you already run your business on. Can't believe I'm saying this, but I can't wait for next year's audit.

Wayne Hamilton
Founder at Payment Box

When you offboard an employee, Rippling revokes their access, wipes their device, and generates a certificate of data destruction for your auditors all in one system. When you onboard a new hire, their device arrives pre-configured with the right settings. And now with Rippling Automated Compliance, your SOC 2 evidence is collected automatically as your workforce evolves.

Once your evidence is collected, you’re connected with an independent CPA firm and pen testing partners. You can plan the audit, approve and export evidence, and respond to auditor requests all in one centralized portal. The auditor reviews evidence independently and uploads your final SOC 2 report once done. You can get back to running your business.

Get started

Rippling Automated Compliance is available today for SOC 2 Type 1 and Type 2, with more frameworks on the way.

No tickets to chase. No fire drills when the auditor shows up. Book a demo and see it for yourself.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

Why Rippling AI is different

Today we’re introducing Rippling AI. It’s AI that you can trust to answer deeply specific questions about your company using live data, and then take real action across HR, IT, and Finance.

Most enterprise AI products today are built as thin conversational layers on top of existing software. They may be able to retrieve information or generate a plausible response, but they are not deeply integrated with the underlying data model, business logic, permissions, and workflows of the system itself. When dealing with critical business information, that level of integration is not optional, it’s required.

It returns exact answers, not educated guesses. When you ask Rippling AI a question, it will express the action you want to take, in code. It writes a query. It generates precise SQL, or writes a formula, or builds a report. This approach creates inherent protections against hallucinations, and means that Rippling’s AI rides the tidal wave of investment the underlying foundation model companies have made in code generation. It also means the answers you get back are auditable and deterministic. Rippling’s AI shows its work, and you can verify the approach it’s taking.

It understands your organization. References like “my team” or “last quarter” resolve against your actual data: specific people, specific numbers, specific records. Ambiguous references to departments, candidates, work locations, and names are resolved up-front (“Which John do you mean?”)

Permissions are automatic. Rippling AI inherits your existing roles and access permissions. A manager sees their team. An HR admin sees more. You cannot trust LLMs to filter or redact data correctly, based on your company’s permissions model – but with Rippling, you don’t have to, because the LLM can only access information that the user it’s chatting with can access themselves. If you instruct the LLM to make changes on your behalf, it will tee them up for your review. And these changes will go through whatever approvals your company requires, the same as if they were made by a human.

You’ll probably never build a report yourself again

Rippling AI can build reports in one-shot, and run SQL analysis off of your data in Rippling. For example, let's say you want to create a report of voluntary terminations over the last 6 years. Simply write a prompt, and instead of hours of manual analysis, you get a detailed answer, plus the underlying reports to inspect yourself.

Spot attrition risks by team and manager.

It can also work from images or documents that you upload. Snap a picture of a chart you drew on a whiteboard, and Rippling AI will build it, directly in our BI system.

It takes the tedious work off your plate

There are many cases where AI can take action in the system on your behalf.

Drop in a messy spreadsheet of spot bonuses for your employees and say: “Add these bonuses to payroll. Convert currencies for my review.” Rippling AI maps columns to employee records, validates the data, flags missing information, converts currencies, and stages the payroll run for your approval. What used to be hours of cleanup becomes minutes of review.

Tell the AI to grant someone (or multiple someones) access to key business systems. “Give Jane access to Salesforce”, “Add our new consultant to our corporate travel system”

Or say you need to restructure a team: “Reassign all of David’s direct reports to Jamie effective Monday.” Instead of manually updating profiles one by one, Rippling AI stages the change in one move so you can review and confirm.

It can handle employee questions instantly

Rippling AI gives employees immediate, detailed answers to questions that would otherwise go to HR. Employees can ask about:

• Benefits: “How much does my medical insurance cover for an MRI?” “Why was my FSA claim rejected?”
• Payroll: “My paycheck was lower this pay period than last pay period, it seems wrong, what happened?”
• Policies: “How much parental leave am I eligible for?”, “What’s our expense policy while traveling?”
• Reimbursements: “What’s the status of my reimbursement and when will it get paid out?”

And more.

Employees don’t need to open a case or search for help articles. When an employee doesn’t understand why their paycheck changed, Rippling AI can trace the root cause across payroll, benefits, expenses, and more, and explain exactly what happened.

Get access to Rippling AI

For years, there has been a gap between what leaders want to know and the answers they can get quickly. Teams wait for reports. They chase information across disconnected systems. They make calls without the full picture. Then they spend hours doing manual work to carry out the decision.

Rippling AI closes those gaps. It gives you answers you can verify and actions you can control, all in one place. It helps you understand what is happening, decide what to do next, and move the work forward.

Rippling AI is available today. If you’re already on Rippling, request access to use it with your own data. If you’re not, we’d love to show you what it looks like for your business.

Companies that run on Rippling perform better.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.

When standard software doesn’t fit your business, critical workflows end up in spreadsheets and manual workarounds. Rippling Solutions turns them into custom apps and automations — built in Rippling, by Rippling engineers, with your data, permissions, and policies.

Rippling already automates the most critical parts of running your company, from onboarding to payroll to device management and spend controls. But every business has unique processes that don’t fit neatly into a standard system.

Maybe you need a payroll model that calculates dynamic rates based on real-time sales data. Or a report that merges payroll and overhead data to show true department costs. Or a tool that tracks special equipment and vehicles assigned to your employees. These are the kinds of workflows off-the-shelf apps weren’t built to handle.

And when standard software doesn’t fit, most teams end up with two options: hold the process together with spreadsheets and manual workarounds, or hire third-party consultants for one-off builds that are brittle and expensive to maintain.

Last year, we introduced App Studio, so customers could build custom apps in Rippling, self-serve, to replace simpler spreadsheet processes with something auditable and scalable.

Since then, customers have come to us with more advanced needs: processes that require data from multiple systems, use custom logic, and must hold up over time.

That’s why we’re launching Rippling Solutions

Rippling’s Forward Deployed Engineers (FDEs) work directly with you to build custom, production-grade apps and automations inside Rippling. That means no handoffs, no workaround tools, and no compromises. Rippling Solutions fits your company perfectly because it's built for your company.

With Rippling Solutions, Rippling doesn’t just support your core HR operations. It can also power your most unique processes through custom apps and automations that run on your data, permissions, and policies.

Built by engineers who understand your unique problems

Rippling FDEs are full-stack engineers who build Rippling Solutions. And they don’t operate in the background. They meet directly with customers, identify the job to be done, and build exactly what’s needed.

That means no translation layer. The person who understands your use case is the same person writing the code. As Kevin Bai, a Rippling FDE, puts it:

“There is no handoff. And that not only drives efficiency. It drives alignment.”

Built in Rippling. Connected to everything.

Unlike standalone apps or external builders, every FDE-built solution lives inside of Rippling. That means:

• Your data is already there, and you can augment it with third-party data. No more manual transfers or consolidation.
• Your employees are already there. No new logins or systems to adopt.
• Your governance is already there. Every app inherits Rippling’s permissions and policies.

Whether you’re solving complex pay structures, compliance, asset tracking, scheduling, or cross-system reporting, the building blocks are already in place. That’s what makes these solutions fast to ship, easy to maintain, and ready to scale.

What Rippling is building for customers

Rippling Solutions is already changing how teams run critical workflows. Here are a few examples of what we’ve built for our customers:

• When Athena needed a way to track licenses and credentials for over 250 clinicians, they worked with a Rippling engineer to build and launch a solution in a single session. “The support was amazing,” said HR Director Charlene Feliciano. “I’m leaving feeling confident I can manage this on my own. And if I need help, I know I’ll get it.” Read more.
• Sequoia Riverlands replaced spreadsheet-based project cost calculations with an app that combines employee pay, benefits, per-employee overhead, and other third-party business data to forecast total project costs. “The impact that this app will have will be pretty incredible,” said Phil Daubenspeck, Chief Investments and Partnerships Officer. Read more.
• A fast-growing fitness studio replaced spreadsheet-based instructor pay calculations with an app that pulls class data from Mindbody and automatically calculates pay and bonuses. Now payroll runs accurately and on time, without manual updates or spreadsheet maintenance.

These are the kinds of operational workflows that most software ignores. With Rippling Solutions, they can be codified directly in Rippling, instead of scattered across spreadsheets.

Rippling fits your business, and evolves with it

Every company has its own logic and processes. Most software can’t accommodate that without months of customization, brittle integrations, or painful compromises.

Rippling can.

When your system of record is programmable, and you can work directly with a Rippling engineer, there are fewer limits. Instead of duct-taped tools, you get custom apps and automations built directly in Rippling, delivered quickly and designed to scale as your business evolves. And unlike a one-off build from a third-party consultant, maintenance doesn't fall solely on your team. Rippling helps keep your solution running reliably with ongoing maintenance and support.

Explore Rippling Solutions with an expert

How many manual processes are slowing your business? Connect with an expert to see what we can build for your team.

Learn more.

DISCLAIMER

Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions.