Rippling Release Notes

Today, we're launching Rippling Helpdesk, an IT support agent that resolves routine employee requests before they require manual IT work.

Requests like app access, password resets, device lockouts, and device replacements usually follow a known path: identify who is asking, apply the right policy, take the action, and record what happened. The answer may already be known, but an IT admin still has to repeat those steps across a message, a ticket, employee data, and the system where the work happens.

Helpdesk uses the employee, policy, app, and device context already in Rippling to do that work. It can answer questions, troubleshoot issues, route approvals, or take supported actions across Rippling IT. Admins decide what runs automatically, what needs approval, and what escalates. Every request and action remains visible and logged.

That means routine requests are resolved before they reach an admin, without losing the control or audit trail that IT relies on. Employees get help faster. IT spends less time repeating known steps and more time on issues that need judgment.

How Helpdesk works

Consider a familiar IT request: an employee tells Helpdesk, "My laptop is slow." Helpdesk doesn't begin with a generic troubleshooting script. Because it is built into Rippling, it can identify the employee, the device assigned to them, and the company policies that apply.

If the device is enrolled in Rippling MDM, Helpdesk can inspect details such as the laptop's age, its memory usage pattern, and which applications are running. It uses those signals to narrow down the cause and determine the next step.

If an application is consuming too much memory, Helpdesk can guide the employee to close it and restart the laptop. If troubleshooting doesn't resolve the problem and the device meets the company's replacement criteria, Helpdesk can start a replacement through Rippling Inventory Management. The replacement can proceed automatically or wait for approval, based on the company's policy for that role, tenure and device type. If the Helpdesk agent cannot resolve the issue, it escalates the request with the employee, device, and troubleshooting context already attached.

The employee gets help without waiting for an admin. IT keeps control over what runs automatically, what requires approval, and what escalates. The request and actions are recorded in an activity log.

Agentic support built into your IT stack

IT defines exactly what the agent is allowed to do and retains visibility with a full activity log covering every request, resolution, and escalation. There is no tradeoff between compliance and automation. You set the rules, the Helpdesk agent enforces them.

As recurring requests emerge, Helpdesk can suggest automations or reusable agent skills to handle the next one with less manual work.

Employees get help sooner, and admins can turn their attention from routine requests to strategic IT issues.

Start resolving tickets on arrival.

See Rippling Helpdesk in 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.

Today, we're introducing Rippling AI Governance

A suite of products that helps companies scale AI usage without losing track of spend, access, or accountability. It connects AI activity to the workforce context already in Rippling, so companies can move from reacting to AI sprawl after it happens to governing AI at the moment it's used.

Built on Rippling's employee graph, AI Governance gives IT leaders one place to understand AI usage and spend, control access to models and tools, and manage agents throughout their lifecycle, with policies that map to each employee's team and role.

AI adoption has outrun AI governance

Companies are under pressure to scale AI, but the systems to govern it haven't kept up. Leaders face three problems:

• They can't see or control AI usage and spend. Data is scattered across vendor consoles, invoices, API logs, spreadsheets, and unmanaged accounts. Finance sees the spend after the fact, while IT has to explain what changed without a reliable view of the people, teams, models, and workflows driving it.
• They can't reliably secure what employees and agents can access or do. Model access, tool permissions, and data security policies live across separate systems. Controls are often manual, vendor-specific, or applied only after access has spread.
• They can't reliably identify and manage agents. Agents often run through shared keys, generic service accounts, or repurposed employee credentials. That makes it difficult to determine who is responsible, what an agent can access, what it has done, and what should happen when its owner changes roles or leaves.

That leaves companies with two bad options: move quickly and accept rising costs and security risks, or lock AI down and miss out on productivity gains.

Govern AI usage, spend, access, and agents from one platform

For leaders tasked with efficiently scaling AI across their organization, Rippling AI Governance is a set of software tools that allows companies to measure and control AI usage. This is a suite of products that, taken together, give admins a comprehensive set of controls for managing the use of AI within their organization. Rippling routes employees' LLM traffic, governs tool access, surfaces unsanctioned AI usage, and manages agent identity, all through the organizational context that makes policies enforceable. Our promise is to help companies scale AI adoption without losing control of spend, access, or auditability.

Employees can activate an agent and delegate their identity. Agents working autonomously can also use their own identity without a human delegating permissions.

What we're launching

MCP Gateway

AI agents become useful when they can act on information, not simply answer questions. An AI agent decides how to complete a task. Model Context Protocol, or MCP, connects it to the tools, data, and systems it needs to do so. An MCP Gateway controls which of those connections and actions are allowed.

As employees connect AI tools to more business systems, every unmanaged connection creates another path to sensitive data that IT can't reliably see, control, or audit. IT may not know what is connected, who is using it, or what actions they can take once connected.

Rippling MCP Gateway gives IT a controlled entry point for supported MCP connections. The model is familiar: IAM governs which SaaS apps employees can access. MCP Gateway governs which company systems employees and agents can reach through AI, and what they can do once connected. Before a tool call runs, Rippling checks who is making the request and whether the relevant policy allows it. The call is then recorded for review.

That control goes deeper than deciding whether a connection can exist. An engineer may have broad access to GitHub, but an AI agent working on their behalf shouldn't automatically receive the same permissions. The agent might be allowed to read approved repositories and open pull requests, while being blocked from deleting branches, changing repository settings, modifying secrets, force-pushing code, or accessing security-sensitive repositories outside the engineer's team. MCP Gateway governs what an agent can do through a connection, not simply whether it can connect.

Because Rippling already knows each employee's role, team, and employment status, access updates automatically when employees join, change roles or teams, or leave the company. A standalone gateway has to import and maintain that context from external identity systems. Rippling starts with it.

With Rippling MCP Gateway, companies can connect AI to more of the business without losing control of who can access each system or what they can do within it.

AI Gateway*

AI spend is becoming one of the hardest software costs for companies to manage. Employees and agents can call OpenAI, Anthropic, Google, and other providers directly, often across multiple tools and subscriptions. Finance sees the bill after the fact. IT sees only part of the access picture. And leaders struggle to answer the basic questions: who is using which models, which teams are driving spend, and where should usage be limited or optimized?

Rippling AI Gateway gives companies one governed path for LLM traffic. It sits between employees, apps, agents, and model providers, so admins can control access to models, set budgets, enforce spend limits, and log usage with the employee and organization context already in Rippling.

That matters because AI governance is not just about saying yes or no to AI. It is making sure the right people have the right level of access, at the right cost. A security engineering team may need frontier models for complex work, while other teams can be steered toward faster, lower-cost models for everyday tasks. With AI Gateway, companies can route the right traffic to lower-cost models, set hard budget caps, and block usage when limits are reached.

The result is governance and spend management in the same system. Companies get visibility into model usage and cost by user, team, department, provider, and model, auditability for every request, and controls that apply at the moment AI is used, not weeks later when the invoice arrives.

Agent Identity Management

Agents are becoming a new class of worker. They can hold credentials, access apps, call tools, and take action across the business. But without a clear identity and owner, an agent can quickly become a security and audit risk: no one knows exactly what it can access, what it has done, or how to revoke it.

With Rippling Agent Identity Management, companies can create and manage agent records alongside employees and service accounts. Admins can assign owners, set permissions, and provision agents into third-party apps using the same access controls they already use in Rippling.

That means every agent can be known, owned, permissioned, auditable, and revocable from one system. Agent Identity Management gives companies the foundation to put agents to work safely, without letting them become invisible, shared, or unmanaged accounts.

An agent can act through permissions delegated by an employee or through its own managed identity. In either case, admins can see who owns it, what it can access, how much it spends, and what it does.

Shadow AI Detection*

Shadow IT has always been a problem for IT teams. Employees find a tool that helps them move faster, start using it before it has been reviewed, and suddenly the company has software with no clear owner, policy, or audit trail. Shadow AI raises the stakes. An unapproved AI tool may not just store data; it may read files, connect to apps, process sensitive information, call tools, or keep running as an agent.

Rippling Shadow AI Detection helps companies find AI usage they did not provision. It gives admins visibility into unapproved AI apps, third-party OAuth connections, browser extensions, coding assistants, local agents, and MCP servers, then connects that activity back to the employee, device, app, and organization context in Rippling.

From there, admins can decide what should happen next. They can approve a tool, restrict it, notify employees to move to an approved alternative, revoke risky access, or bring usage into a managed path through Rippling AI Gateway or MCP Gateway. The goal is not to slow AI adoption down. It is to make the approved path easier, safer, and more visible than the unmanaged one.

This completes the AI governance story. MCP Gateway governs how AI connects to business tools. AI Gateway governs model usage and spend. Agent Identity Management makes non-human actors known and accountable. Shadow AI Detection finds the activity happening outside those approved paths, so companies can scale AI without losing sight of what is actually being used.

Get started today

AI adoption isn't slowing down. The companies that scale it well won't be the ones that move fastest or impose blanket restrictions. They'll be the ones that build governance into the same infrastructure that already runs their business. That's what Rippling AI Governance is: visibility and control over AI, built on the employee graph that already powers everything else.

MCP Gateway and Agent Identity Management are live today. See them in action.

*Join the waitlist for AI Gateway and Shadow AI Detection.

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.

AI Spend Console

Today, we’re launching AI Spend Console. It gives CFOs and CTOs a clear view of AI spend, connects it to business outcomes, and governs the use of approved LLMs.

Unlike simple usage dashboards or point solutions, AI Spend Console ties AI spend to employee attributes, so you can understand which departments, teams, or roles are driving your AI bill. It also connects spend to key business metrics like performance ratings or pull request volume, so you can understand where token usage drives improved business outcomes.

Here’s the journey of why we built this product and how we got here.

AI token spend was growing 80% MoM

Like many tech companies, we were early to AI adoption. We turned on AI tools for all employees, and there were no limits. We encouraged experimentation. We hosted hackweeks, lunch and learns, and “ship shows”.

Very quickly, we saw a freight train of new expenses rolling our way. Our AI token spend was growing 80% MoM, and we were on a path to spend 40% of our R&D headcount budget on tokens.

At first, we didn't have the infrastructure to track, understand, and manage usage patterns and this new expense category. Our Finance team was manually collating data from multiple vendor dashboards and then running ad-hoc analyses. They could see total spend but not which teams or roles drove increases. More importantly, there was no way of connecting AI-related expenses to any measure of business impact.

We couldn’t answer questions like:

• Which models are used most frequently and by which teams?
• Which roles and levels are driving up our AI bill?
• How does AI spend per pull request differ between our top and bottom performers?
• Which engineers have high AI spend, whose peers frequently ask them to redo work in code reviews?

I'd seen a similar pattern before. In a previous role, I managed AWS infrastructure spend for one of the largest websites in the world. I knew exactly what unchecked cloud costs look like, and I knew this was going to get worse, so I started to tackle this problem in partnership with Catalina Zhao from our BizOps team. We quickly spun up a SWAT team of leaders from every department, including BizOps, HR, Product Sales, Engineering, Marketing, and more, to build our AI program at Rippling.

First, we did a “go and see”

One of our core values at Rippling is Go and See, so we grabbed time with our engineers, while diving into usage patterns (e.g. daily active users) and data on model costs, vendor spend, and usage patterns.

We worked with a few engineers to scrape this data directly into a centralized data lake, which was entirely manual and extremely time-intensive.

Here’s what we found:

• Concentration was extreme. Roughly 10–15% of our employees were driving about 60% of total AI spend. One engineer was spending $50,000 a month.
• Expensive defaults were the norm. The newest models were set to fast mode. That wasn’t because we made that decision internally but rather because nobody had ever looked into it and set best practices.
• The productivity signal was real, but not linear. Engineers heavily using AI tools like Cursor were shipping more PRs, but the biggest productivity improvements came from initial adoption, not from spending more on frontier models.
• Our engineering org needed to evolve. We saw which engineers were pushing the frontier of innovation with creative AI use cases, where those use cases stayed siloed instead of spreading org-wide, while identifying blockers to broader adoption.
• The trajectory was unsustainable. We built a model to forecast costs and discovered that we were on a path to spend 40% of our R&D headcount budget on AI tokens. The following year, we’d approach 90%.

To help control costs, we went into every AI provider and set a standard dollar amount per tool per month cap. It wasn't a permanent solution, but it gave us time to get our strategy in place.

Building our AI infrastructure

We needed clear visibility into token consumption, by whom and on what models, and then connect those costs to business metrics like code quality and number of PRs merged.

Step 1: Getting all the data in one place

We used our Data Cloud infrastructure to pull AI spend directly from our vendors (such as Cursor, OpenAI, and Anthropic) directly into Rippling.

When external data like token consumption gets ingested into Rippling, our 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 business users don’t have to configure those joins themselves. For example, a GitHub user name (coder3) gets mapped to the employee’s work email ([email protected]).

With Rippling’s data ingestion and mapping capabilities, we centralized our AI spend and usage data and mapped it to our built-in org context. A GitHub pull request is not treated as a flow row. It’s a record connected to an employee and attributes such as their team, role, or department. That means business data becomes easier to analyze, easier to govern, and easier to act on.

Step 2: Building dashboards

Now, with our AI spend and usage data in Rippling, we used Rippling AI’s ability to autonomously design and render dashboards based on any question, without any SQL work.

We started building dashboards to view weekly AI spend over time, model usage by teams, spend vs. GitHub PRs, spend vs. code rework, and more across our engineering team.

We created a dashboard for engineering known as the AI Scorecard. It connects data across Cursor, OpenAI suite (Codex), and Claude suite (Code) to highlight four key metrics:

• Adoption score (10 points): An employee’s rate of agentically prompting with a tool every day.
• Usage score (10 points): An employee's depth of engagement with AI tools.
• Productivity score (10 points): An employee’s output (PRs, lines of code).
• Cycle time (10 points): How long do the PRs take to merge?
• Efficiency score (7.5 points): Spend on AI vs. productivity.

All Rippling dashboards automatically obey the permissions inside of Rippling, so users can share dashboards scoped to each viewer. For example, an Engineering manager can see the dashboard as another manager can, but only scoped to their team.

Step 3: AI Gateway

Observing how much one person spends is only half the battle. Our team also needed a way to control it.

We built Rippling AI Gateway*: an internal routing layer that sits between our employees and approved models. All LLM traffic goes through it. This gives us one place to set and enforce spend limits and model access policies, route AI requests to the right models for the right job, and log every transaction.

It also means that we can implement new models without requiring the organization to retool their entire setup, implement routing logic, and keep security logs without vendor negotiations. Additionally, we can use our AI Gateway in our developer ecosystem to run agentic jobs and orchestrated workflows (such as Slackbots or JIRA ticket triage). Our tooling is no longer locked to any one vendor.

Step 4: AI captains

Then, we needed to fundamentally shift how we approached AI across the company, not just in R&D.

We stood up an AI Captains program. This is an internal team of Ripplers whose goal is to expand our use of AI intentionally in every Non-R&D org. They are the connective tissue between our company-wide AI direction and the day-to-day reality of each team. They help select the right tools for the right job, automate the highest value workflows, enable employees, and ensure that we link AI use cases to real ROI.

Each captain owns three things for their org:

• Impact: Building and validating AI solutions for their org tied to real ROI.
• Adoption: Enabling their org on the right AI tools.
• Governance: Maintaining platform hygiene, monitoring usage, reviewing and approving skills, and serving as Tier 1 support for AI issues.

20 AI Captains were nominated from across Rippling, and the results were significant. For R&D, we went from a forecast of spending 40% of our headcount budget on tokens to 10 to 15%. That’s tens of millions of dollars a year. And productivity continued to climb because constraints made engineers smarter, not less capable. We saw the same for other teams.

When employees had unlimited spend, they defaulted to the most expensive models to avoid any cognitive load. When they had a budget, they actually learned the tools. They figured out which models were good at what, which configurations were most efficient, which harnesses gave them the best results. The constraint created the innovation.

AI Spend Console: One place to track, understand, and control your AI spend

This work led us to productize our approach so other companies can see, understand, and control their AI spend too. With AI Spend Console, CFOs and CTOs can get a clear view of AI spend, connect it to business outcomes, and, soon, govern the use of approved LLMs.

Rippling AI generates permissioned dashboards across your connected data. You can even ask follow-up questions in natural language to drill down into spend and usage patterns or customize charts, without any SQL work.

AI Spend Console allows you to:

• Identify what’s driving AI spend. Break down costs by teams, roles, or departments.
• Understand the value of spend. Map AI spend to business metrics like performance ratings or pull request volume, so you can flag inefficient use.
• Control AI access and spend*. Enforce model access policies based on employee attributes, then automatically route AI requests to approved LLMs.

Starting today, you can try AI Spend Console for free with a 30 day trial. No Rippling subscription required.

A new era for AI token spend

AI isn’t just a line item. It's embedded in how every company works and how every product is built today. Just spending money on AI doesn’t mean anything. What matters is spending it intentionally.

Context is everything. Intentional spend starts with visibility. You need to see all your AI spend in one place, tied to the teams, roles, and departments driving it, and connected to the business outcomes it's producing. Without that org context, spend is just a number. Rippling AI Spend Console gives you all three.

We used it to go from zero visibility and a trajectory toward spending nearly half our R&D headcount budget on tokens, to a governed and measured AI program that’s making Rippling more productive than ever before.

If you’d like to try it out, you can get started here.

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.

Join the Rippling AI Gateway waitlist.

Today, we’re launching Rippling Procurement, an AI procurement platform that allows businesses to buy everything they need, from software to professional services, up to 5x faster while maximizing savings and minimizing risk on every purchase.

Employees get what they need faster, and Finance stays in control of every dollar.

Rippling Procurement at a glance

• One real-time view of all spend. Cards, bills, expenses, contracts, HR, IT live in one place, each dollar tied to the person, department, and business context behind it.
• Wasteful spend gets caught before it happens. Duplicate vendors, invoice mismatches, and costly renewals are flagged before money leaves the business.
• AI agents run procurement for you. Intake, legal review, vendor security, purchasing, and renewals are assisted by agents, reducing manual admin work by up to 90%. Use our prebuilt agents or build your own.
• Workflows that are always up to date. Approvals route automatically from the roles, departments, and live org chart already in Rippling.

Procurement is broken, but doesn't have to be

For most small and mid-sized businesses, slow procurement is treated as an unavoidable cost of doing business. Purchase requests disappear into email threads. Approvals sit in someone's inbox for two weeks. Employees give up and find workarounds. And Finance has no visibility until an invoice lands on their desk.

Eventually, companies hire a dedicated procurement person and implement a point solution for procurement. But even then, the core problems don't go away. They just become slightly easier to manage.

That is because traditional procurement software sits beside your finance and HRIS stack, leaving data siloed. No single system knows what's already been bought on a card, billed directly, or expensed by someone else. Renewals slip through the cracks, and by the time Finance notices you've bought the same thing twice, the money's already out the door.

With Rippling, all of that goes away.

Rippling unifies Procurement with the rest of your back-office stack (HR, IT, and Finance), giving you a single, real-time view of total spend across cards, bills, expenses, and vendor contracts, all tied directly to the people and departments behind it.

That unified foundation doesn't just give Finance better visibility; it gives Rippling AI the context to help you automate procurement.

Because your employee roles, departments, and live org chart already exist in Rippling, you can easily design custom approval workflows based on criteria such as department, seniority, risk level, vendor type, and more out of the box.

This same foundation empowers Rippling AI to not just answer questions about procurement. It actually runs procurement.

We built a team of specialized agents that help work every request in parallel, catching issues a human wouldn't see until much later, if at all.

Meet your team of AI agents

• Intake Assistant. Takes the first pass on every purchase request, checking that the requester provided enough detail, that it aligns with the attached documents, and that the business case is clear.
• Contract Reviewer. Reviews every contract attached to a purchase request for common red flags, such as auto-renewal clauses and logical inconsistencies, and flags them to the requester and legal reviewers before anyone signs.
• Vendor Security. The agent reviews every new vendor’s website against the request details to verify how they manage data and whether onboarding them carries risk.
• Buyer Assistant. Generates purchase orders, matches them to your invoices and receipts, and checks them against past POs and bill payments to ensure they're coded correctly, so every dollar is accounted for.
• Renewal Manager. Flags upcoming renewals 30, 60, and 90 days before the notice window closes, and surfaces the vendor's full history: past contracts, payments, SSO logins, allotted seats. That way, you know exactly what you're paying for and whether you still need it.

Together, they help automate up to 90% of the manual admin work in procurement.

Those agents come ready to use on day one. But because every business buys differently, Rippling AI is yours to direct.

You can build and customize your own agents to automate your process exactly how your team buys. Because it already holds every piece of data behind a purchase, there's almost nothing they can’t assist with.

The result? Procurement cycles are measured in days, not months.

One customer, Signalwire, reduced procurement approval times by 50%.

Our Agents stop wasteful spend before it happens

The value of AI in procurement isn't just accelerating your buying cycle. It's eliminating unnecessary spend before money ever leaves the business.

Companies rarely lose money due to a single catastrophic mistake. It leaks away quietly in the small decisions no one has time to review: an invoice that's higher than expected, a forgotten software subscription, a duplicate vendor, or a purchase that could have been avoided entirely.

Rippling Procurement catches these before they become costs. When your Anthropic bill starts climbing faster than you budgeted for, Rippling AI spots the drift early and flags it, so you catch the overage while you can still act on it instead of finding it at month-end.

Requests flow through Rippling before any money is committed, giving Finance visibility before it’s too late. Someone requests a new design tool? Set up Rippling AI to point them to the Figma seats you already own before adding another vendor.

Every purchase is evaluated before it's approved and every invoice is validated before it's paid.

That's the difference between buying faster and buying smarter. Rippling does both.

Unrivaled spend visibility, so you’re always in control

Buying smarter starts with seeing everything. Most tools show you what's being spent and who's spending it, but not the context behind the purchase: whether software is actually being used, who relies on it, how it connects across the business, or if another team already has the same tool.

In Rippling, no purchase stands alone. Because procurement already lives alongside your HR, IT, and finance data, every dollar is connected to the person, department, cost center, contract, approval, and system access behind it.

That unified data layer gives Rippling something other platforms don't – context.

Rippling connects every dollar to the people and business activity behind it, giving teams the full picture needed to make smarter buying decisions.

That means Rippling can uncover opportunities other platforms miss:

• Renew software based on actual usage Which software contracts are up for renewal, and how many assigned users logged in through SSO in the last 30, 60, or 90 days?
• Identify unmanaged vendor spend Which vendors are we paying even though there's no active contract, purchase request, or approved PO?
• Consolidate overlapping vendors Which departments have the most active contracts for the same use case, and where are we paying for overlapping vendors?
• Reduce renewal risk and unnecessary costs Which contracts are expiring soon, which include termination-for-convenience clauses, and how much could we save by exiting specific vendors?
• Fix procurement bottlenecks Which departments have the longest procurement cycles, and which approval steps are slowing down purchases?

And with Rippling Data Cloud, that picture can extend to the external systems your business still relies on. Procurement can connect vendor spend to operational data – such as engineering activity on GitHub or the direct mail impact on sales in Salesforce – so Finance can evaluate vendors based on actual business impact, not just spend.

The result is procurement that understands the full context behind every purchase. Instead of reacting to spend after the fact, Finance can make faster, smarter decisions before money leaves the business, helping teams buy what they need without overpaying.

Eliminate procurement surprises today

If you've been waiting for a procurement solution that lets you buy what your team needs faster without overpaying, the wait is over. Rippling Procurement is available today.

Request a demo or learn more to get started.

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.

Benefits administration is one of the most time-consuming parts of running HR

Every time an employee adds a family member, someone has to confirm they qualify. Every time an insurance company sends back coverage decisions, someone has to go through that file and update every record. And when employees have questions about their plans or make a mistake on a spending account claim, they go to HR and wait.
Today we are introducing AI-powered Benefits Administration, built directly into Rippling to handle benefits work using the employee, plan, and dependent data already in the system.

Verify dependents before coverage takes effect

When an employee gets married, has a child, or enters a domestic partnership, they can add that family member to their health plan. But not every dependent automatically qualifies. Companies have policies that determine who is eligible for coverage, and verifying that someone meets those requirements is how companies make sure they are not paying for coverage they should not be.
For most companies, that verification falls entirely on the admin. Every document, every request, reviewed by hand.
With Rippling AI, that review happens automatically. Rippling AI reads the documents the employee uploads, confirms the relationship, and approves coverage if the relationship is confirmed. If it cannot confirm the relationship, the benefits administrator gets a notification to review the request before coverage is approved. Coverage goes to the people who actually qualify, and the company stops paying for the ones who do not.

Apply carrier coverage decisions in bulk

When an employee enrolls in voluntary coverage like life or disability insurance, the carrier needs to approve their coverage before the policy takes effect. Once the carrier sends back its decisions, someone has to go through that file and update every record in Rippling.
During open enrollment, that file can have hundreds of rows. Each one has to be matched to the right employee, their coverage updated, their paycheck adjusted, and a notification sent. It is repetitive work, and it has to happen quickly before the new plan year begins.
Now, with Rippling AI, the admin uploads the file and Rippling AI does the rest. It matches each decision to the right employee, updates their coverage, adjusts their deductions, and notifies them of the outcome - automatically. What used to take hours of manual reconciliation now takes minutes.

Submit Flex claims correctly, every time

When an employee submits a Flex spending account claim with missing or incorrect information, they find out days later when it comes back rejected. By then they have to start over, and HR often gets pulled in to help sort it out.
Now when an employee has a Flex spending account expense, they upload a receipt to Rippling. Rippling AI reads the receipt, fills in the claim form, and flags anything that would cause the claim to be rejected before the employee hits submit.
Employees stop finding out about claim errors days after they submitted. HR stops fielding the questions and follow-ups that come after a denial.

Help employees pick a plan with confidence

Choosing a health plan is one of the most consequential financial decisions an employee makes, but employees rarely have enough information to make it well. Premiums, deductibles, and out-of-pocket maximums form a confusing maze, and the result is a flood of questions to HR.
Now employees can pull up their plan options side by side in Rippling AI and see what each plan would actually cost them for things like a doctor visit, urgent care, or a prescription. If they want to understand how their costs might change with a life event like getting married or having a child, they can ask Rippling AI directly and get an accurate answer the moment they need it, without HR having to step in.

Built on the data that benefits administration already runs on

Most benefits tools only manage benefits. Rippling is a single system for an employee's HR, payroll, and benefits data, so their dependents, their paycheck, and their plan elections all live in the same place. Because that data is already connected, Rippling AI can act on it accurately without anyone having to move data between systems or do the work by hand.

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.

IT admins have a challenging job

You have to keep your company secure in a constantly changing threat environment, while also making sure employees can easily access the devices, apps, and tools they need to do work without a hassle.

Most IT leaders rely on 7+ solutions to do this work, but each only solves one piece of the puzzle. Plus, these tools can be expensive and difficult to manage. This leaves you filling in gaps with tedious manual tasks, making it difficult to apply advanced security approaches like zero trust.

We firmly believe you don’t need a large team, massive budget, or multiple tools to scale your IT program. You deserve a unified system, with all the features you need, minus the complexity.

That’s why we built Rippling IT from scratch, to empower solo IT admins and lean IT teams to securely manage identity, access, devices, and physical device logistics, all from one platform.

End-to-end IT management

Rippling has understood the value of supporting IT professionals from day one. We’ve used our own IT solutions to efficiently grow to almost 4,000 employees with a very small IT team; shipping laptops, configuring devices, provisioning accounts, and securing offboarding. Running IT this way has helped us grow at a record pace while keeping our IT team agile.

With Rippling IT, you can manage the full user and device lifecycle end-to-end. It includes:

• Identity & Access Management: Easily implement SSO, MFA, conditional access policies, role-based access controls, and more, based on hundreds of real-time user and device attributes. Plus, eliminate time-consuming admin work like third-party group management in integrated applications, provisioning during on/offboarding, and more.
• Mobile Device Management: Manage and secure all of your endpoints with our unified, cross-OS MDM for laptops, tablets, and mobile devices. Enforce user-driven security policies, automate device monitoring, implement zero-touch enrollment for best-in-class endpoint protection, and more.
• Inventory Management: Automate manual tasks around device logistics globally with the ability to order, ship, retrieve, and store devices through Rippling—right from your desk. This helps avoid messy supply closets, missing devices, and last minute trips to Best Buy or FedEx.

All three solutions are powered by Rippling’s shared platform capabilities including a custom workflow builder, dynamic policies, permission profiles and membership lists, powerful analytics, audit-ready reports, and more.

These make it easy to configure Rippling exactly how you want, and everything works together seamlessly. You can be up and running out-of-the-box on day one.

Rippling IT works with your existing HRIS

One of the most common misconceptions about Rippling IT is that you need to be a Rippling HR or Payroll customer to use it. You don’t. Rippling IT can be used as a standalone product—covering identity, app access, device management, inventory, and lifecycle automation—while keeping your existing HR architecture exactly where it is.

Rippling IT integrates with BambooHR, Workday, ADP, Paylocity, Google Workspace, Microsoft Entra, and Okta as your source of truth. Rippling ingests employee data from whatever system you already use and turns that context into downstream IT automation: provisioning accounts, assigning apps, enrolling devices, enforcing policies, and removing access when people leave.

This matters because HRIS migrations are not quick projects. They involve data cleanup, payroll complexity, benefits changes, and months of cross-functional coordination. IT teams don’t have to wait for a migration to start solving urgent problems around app access, device management, or offboarding gaps. With Rippling IT Standalone, you can modernize IT without touching HR first.

Bolster security and streamline IT processes from one platform

This integrated approach to IT management is designed to streamline IT operations and strengthen security using best practices powered by default policies.

Automate the entire lifecycle

Because Rippling IT is a unified platform, it takes just a few clicks to layer in advanced security controls and implement frameworks like zero trust—without spending months configuring systems to work together. Because Rippling makes it easy to precisely target security policies based on user attributes, you can strengthen security without hindering employee productivity. The right users will always have the right level of access to company resources at the right time.

Let’s take a look at the classic example of onboarding and offboarding with Judy, a new sales hire.

• Streamlined onboarding: Judy has accepted her offer! Once she is added to Rippling and her start date is scheduled, a series of events are set into motion.
  • Devices and accessories are ordered from the Rippling store (an authorized reseller) and shipped straight to Judy’s home or new office.
  • At a specific date and time, Judy’s accounts are created and access is granted based on attributes like her role (Google Workspace, Slack, Jira, Salesforce).
  • SSO is enabled and Judy is given access to a password manager.
  • Judy is automatically enrolled in cybersecurity training and is asked to review relevant security policies.
• Secure offboarding: To avoid the stress and security risks associated with offboarding, Rippling IT automates every step of the process—down to device retrieval and storage.
  • Judy’s device is locked and wiped and her shared Google documents, inbox, and calendar are automatically transferred to her manager or other party.
  • Judy loses access to all apps and accounts.
  • SSO is disabled and the groups she was in have been updated (Slack channels, Google distribution lists, etc.).
  • Rippling sends a return box with a prepaid shipping label for swift device return.
  • Judy’s device is sent to a secure warehouse to be cleaned, tested, and either stored or configured for the next new hire.

Layer advanced security controls in a few clicks

Because Rippling IT is a unified platform, it takes just a few clicks to layer in advanced security controls and implement frameworks like zero trust—without spending months configuring systems to work together. Because Rippling makes it easy to precisely target security policies based on user attributes, you can strengthen security without hindering employee productivity. The right users will always have the right level of access to company resources at the right time.

Here is a high level view of advanced security measures available with Rippling IT:

• Streamlined setup: Customize and deploy protocols like device trust in minutes, not weeks, thanks to Rippling’s natively built IAM and MDM solutions.
• Layered security: Easily implement SSO, role-based access controls, role-based MFA requirements, password federation, and other custom policies to enforce least privilege.
• Precise targeting: Roll out precise security requirements and conditional access rules based on any combination of user and device attributes. As users join, transition, or leave, these dynamic membership lists will automatically update.
• Consistent compliance: Meet compliance standards like SOC 2, GDPR, and more with audit-ready reports.
• Endpoint security: Rippling has partnered with SentinelOne to provide zero-touch, built-in endpoint protection for threat detection and mitigation.
• Cross-OS device management: Easily enforce strong password policies, automated patch management, OS updates, device encryption, and more, to ensure user devices are secure—no matter their operating system.

A deep dive into device trust

Rippling’s platform makes it easier for you to strengthen security at any stage of your company’s growth. Let’s take device trust, a key component of zero trust. Device trust ensures that users are only accessing critical infrastructure from secure, managed devices. However, this can be difficult to implement because you need to configure disconnected IAM and MDM solutions (or even multiple MDMs) to stay in sync, plus the challenge of distributing certificates to devices. It can take weeks, or months, for even mature IT teams to put in place.

With Rippling, it only takes minutes. Our natively unified IAM and MDM work in tandem from the start, and Rippling acts as the certificate authority—so you have zero maintenance costs. You can easily define rules using user attributes to target device trust. For example, you could say all engineers accessing Jira, AWS, and Github have to use a managed device, otherwise access will be denied.

You can also layer on additional conditional rules, like blocking access if a user has traveled a large distance at an impossible velocity, or if they sign in from an unauthorized IP address.

But device trust is just one example of how Rippling’s unified IT platform makes it possible to quickly layer in SSO, role-based access controls, zero touch installation for endpoint protection, password federation, dynamic MFA policies, and more—all of which strengthen your company’s security at any scale.

Less IT busy work and time spent worrying about security

Companies of all sizes use Rippling IT to grow their business securely and manage IT in a way that best meets their needs.

Longevity Consulting, a professional services firm of about 70 employees, used Rippling IT to clean up a SOC 2 audit and eliminate an onboarding process that once took a week. “We probably turned four or five negative findings from our SOC audit into positives just by moving to Rippling. It was that simple,” says Andy Phelps, CTO at Longevity Consulting. “I’ve gone from a week’s worth of work to 10 minutes. I can push a button, add the people in, and the laptops automatically get ordered, shipped, delivered, access granted.”

Sandbox VR, a 600-person immersive entertainment company with locations worldwide, uses Rippling IT to automate device management and app provisioning across its distributed workforce. “We’re always trying to move quickly, and time is money. Without Rippling centralizing IT and HR, I’d be spending up to a full extra day on new hires, just on provisioning app access alone,” says Shannon Saunders, Manager of People Ops at Sandbox VR.

Go-Forth Pest Control, a 315-person company, manages 26 service locations and 300 devices with a lean IT function that once spent 20–30 hours a week on access and device issues. With Rippling IT, that’s down to five. “What used to be a 10–15 person scramble across spreadsheets and apps is now largely automated in Rippling—from account creation to app access—so onboarding actually happens on time without us herding cats,” says Josh Baker, Process Improvement Director at Go-Forth Pest Control.

How Rippling IT compares to point solutions

There are strong products in every corner of IT, but then you’re looking for a point solution and hopefully you are able to integrate the platforms. Rippling IT doesn’t try to win on any single capability in isolation. It is the combined nature of Rippling IT that differentiates it from other solutions on the market, especially with the ability to integrate your HCM data no matter what platform you are using. So, the real question most IT teams face isn’t whether my point solution handles identity well, but it’s whether their identity layer responds automatically when an employee’s role changes, whether their MDM knows to lock a device when someone is offboarded, and whether any of that happens without a manual trigger or a runbook someone has to remember to run. Point solutions leave that connective work to IT.

For teams already invested in specific tools, Rippling can also integrate alongside existing infrastructure rather than replace it. The right starting point depends on where the biggest operational gaps are.

Get started with better IT management today

You have the option to connect Rippling IT to your existing external HRIS/IdP, or you can use the full power of Rippling and combine our IT and HR solutions for maximum impact.

Unifying your HRIS and IdP creates a single source of truth for real-time employee data, enabling IT and HR to work in lockstep. A single source of truth for employee data makes it easy to navigate the entire employee lifecycle from onboarding to offboarding and everything in between together.

Sign up for a free trial or request a live tour to see how any part of your business can run better with Rippling, whether you start with our IT, HCM, or Payroll solutions.

Schedule a demo with Rippling IT today

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.