Build Monetization and Rev Rec as One – to Capture More Wallet Share and Never Leave Money on the Table
AI is redefining revenue operations—from front office to finance.
In this fireside chat, RightRev CEO Jagan Reddy and Product Manager Natalie share how automation and AI are transforming revenue recognition, compliance, and quote-to-cash strategy for modern enterprises.
💡 Learn how to:
- Automate complex revenue recognition with AI
- Eliminate leakage across pricing, billing, and compliance
- Adapt to usage-based and consumption pricing models
- Future-proof your revenue systems for the AI era
Transcript:
Natalie (Product Marketing Lead):
Jagan, it’s great to have you here. Let’s start at the beginning. Can you tell us about your journey, what inspired you to start RightRev, and how this whole space came to be?
Jagan Reddy (CEO):
Thank you, Natalie. I’m Jagan Reddy, Founder and CEO at RightRev. Before this, I founded RevPro, which I eventually sold and exited in 2017. That journey is really where the entire concept for this space originated.
I’m an accountant by education, but early in my career, I pivoted into tech. That background gave me a dual perspective—understanding both accounting principles and how technology can solve problems in finance. I’ve worked in the office of the CFO since day one, so I’ve always been serving CFOs and finance teams.
At the time, I was working at Juniper Networks, helping with an ERP implementation. During that project, Juniper’s business model began shifting—from selling large, high-value boxes to smaller, lower-value boxes sold through channels. That change disrupted everything: pricing, quoting, invoicing, and ultimately revenue recognition.
I was asked to find a system that could automate revenue recognition in the middle of all that complexity. This was back in 2003, and when I looked around the market, there was nothing. ERPs were doing the bare minimum. Even today, many still only handle the basics. So, we ended up building a custom revenue recognition system in-house.
That experience made me ask, “What are other companies doing?” Being in Silicon Valley, I reached out to several technology firms and asked their teams, “What are you using for rev rec?” And the answer everywhere was the same: spreadsheets.
That’s when I realized there was a massive opportunity to create an entire category—dedicated revenue recognition automation. That was the seed for what later became RevPro.
In 2009, we launched RevPro, and our first customer was Brocade Communications, a $2 billion public company. Around that time, a new accounting standard, EITF 08-1, was introduced, which redefined how tech companies recognized and allocated revenue. RevPro was the only product on the market that could handle that standard. That’s when it really took off.
Natalie (Product Marketing Lead):
That’s such an origin story. I remember being on the other side around that time. I was at a public company too—Responsys—where I owned pricing and packaging strategy. We were experimenting with all kinds of models: usage, tiers, bundles, everything that’s now standard but was brand new back then.
I remember our accounting team panicking. “We’ll need to build a homegrown system to handle rev rec because of the new rules.” And then, RevPro came to the rescue.
I was part of the team that rebuilt our entire SKU structure and product catalog so we could implement RevPro. Our accountant, JC Pousson, would literally say every day, ‘Thank God for this software.’
Later on, we partnered again at Zuora, helping with RevPro there. And now, here we are again—a third round with RightRev, as the monetization world continues to evolve.
So tell us about Salesforce. After RevPro was acquired and you left Zuora, Salesforce approached you and asked you to build another revenue recognition tool for their Revenue Cloud. How did that come about?
Jagan Reddy (CEO):
It was an incredible experience. I’ll never forget standing at the Salesforce keynote, watching Marc Benioff talk about transforming the business landscape. There I was, listening to one of the biggest software companies in the world discuss problems I had spent my whole career solving.
After I sold RevPro to Zuora and left, I was already well known in the ecosystem. Salesforce had built a powerful Revenue Cloud, complete with CPQ and Billing, but the last piece—revenue recognition—was missing. People knew I had exited Zuora, and Salesforce reached out.
When we sat down to talk, it became clear there was a huge gap in their ecosystem that needed to be filled. I always say “the last mile” of quote-to-revenue isn’t really the last mile—it’s part of every mile. Salesforce agreed.
They said, “Why don’t you build it on our platform? Let’s go to market together.” And that’s how it started. Salesforce became a strategic investor, and we’ve now been partners for five years. Together, we’re helping customers complete the entire quote-to-revenue process inside the Salesforce ecosystem.
Natalie (Product Marketing Lead):
That’s such a fascinating journey. Let’s talk about that quote-to-revenue process more broadly.
Pricing and packaging are how you open customer wallets. They determine how customers want to pay and engage with your products. That’s one anchor of the process. The other anchor is revenue recognition—what you report to Wall Street, investors, your board, and your employees. It determines compensation, forecasts, and company value.
When those two anchors—pricing and rev rec—aren’t aligned, everything falls apart. You leave revenue on the table, you experience leakage, and operationally, the whole system breaks down.
Jagan, can you share an example of that? Maybe a war story that illustrates what happens when this process isn’t right?
Jagan Reddy (CEO):
Absolutely. I’ll share something that happens in nearly every enterprise we talk to.
When you ask a sales rep how they sell, they’ll often say, “Our hands are tied. We can’t discount beyond X percent, or bundle this product with that one.” And you wonder, where do these rules come from? They come from the revenue team, trying to prevent downstream chaos in recognition.
Sales might collect the cash and send invoices, but what actually gets recorded as revenue is a completely different story if it’s not structured properly.
I’ll give you one example. There was a large tech company that literally monitored its books until the final minute of each quarter. The CFO sat in a room watching every deal to make sure they hit Wall Street targets.
A $20 million deal came through, and everyone celebrated. “We made it!” The next day, they closed the books—and only $10 million of that deal was recognized. The CFO panicked: “Did we only sell half the deal? What happened?”
Turns out, they did sell the full $20 million—but the timing of recognition was completely different. That’s when the CFO decided: forecasts could no longer come from sales pipelines or CRM data. They had to come from the revenue system itself. He said, “Don’t give me a bookings forecast, give me a revenue forecast.”
That shift—from pipeline to revenue-based forecasting—changed how the company operated.
Natalie (Product Marketing Lead):
That’s a great example. And I have one too.
When I was at a Fortune 100 company, we closed a $10 million deal and everyone celebrated. But when I looked at the numbers, that deal actually cost us $12 million—a $2 million revenue leakage.
A day later, the president sent out an email: “We’re cutting this product line—it’s not profitable.” I was running product marketing and pricing at the time and said, “No, this isn’t a product problem, this is a systems problem.”
They assigned me an entire engineering SCRUM team. We rebuilt everything—usage tracking, contracts, invoicing, billing, and revenue recognition—from the ground up. The first person I called was rev rec.
Once we had the systems aligned, we never had revenue leakage again. The product became consistently profitable, and some of the engineers even filed patents based on the work we did.
So Jagan, when you talk about ASC 606 and say that revenue recognition is the anchor, can you expand on that? You’ve said before that when you connect pricing, packaging, quoting, invoicing, billing, and revenue data, you can see the true health of a business. What do you mean by that?
Jagan Reddy (CEO):
That’s one of my favorite topics. Data quality is the backbone of everything. Everyone’s talking about AI right now, but AI is useless without clean data. The same goes for revenue recognition.
When a customer asks me, “How long will implementation take?” my first question is always, “How good is your data?” Most say it’s great—but then once we plug it into the system, we see how messy it actually is.
When we built RevPro and RightRev, we built data validation into the core of the architecture. We don’t let bad data in because bad data corrupts accounting, forecasts, and financial reporting.
I like to say that half of a revenue recognition system is the engine, and the other half is data governance. We surface every anomaly back to the user so they can fix upstream processes before they cascade into financial errors.
Customers often tell us, “We didn’t even know we had these process gaps until the system showed us.” That’s the real value of strong rev rec—it doesn’t just automate; it diagnoses.
Natalie (Product Marketing Lead):
That’s powerful. Let’s dig into that complexity side more. There are so many tools out there now claiming to “do rev rec,” but in reality, it can get incredibly complex very quickly, especially when you want to price and package in flexible ways.
Marc Benioff talked about changing pricing models to match how people want to buy. Why does that cause so much complexity downstream in revenue recognition?
Jagan Reddy (CEO):
We generally divide companies into two categories: simple or complex. And the truth is, 99.9% of companies fall into the complex category.
Even a retailer selling something off the shelf deals with complexity because they have to reserve for returns. But in SaaS, complexity multiplies.
The biggest driver is contract modification. Customers want flexibility—cancel anytime, upgrade anytime, downgrade anytime. Every modification changes the revenue schedule. Sometimes it’s prospective, and sometimes it’s retrospective, meaning you have to go back, reverse, and recalculate all prior revenue.
Then there’s bundling. When you discount different products by different percentages or include free items, you can’t just take the invoice as revenue. You have to reallocate based on standalone selling price.
Jagan Reddy (CEO):
On top of that, you have compliance requirements—you need to show how revenue was allocated, how it was recognized, and be able to reproduce those reports for auditors and Wall Street. That’s where the complexity really spikes.
Then comes the new wave of pricing and packaging. AI companies are experimenting with new pricing models every month. They’re trying usage-based, outcome-based, hybrid models, tiered credits—you name it. Systems need to adapt at the same pace, and most legacy platforms simply can’t.
Consumption-based models add another layer entirely. You have to track usage data, meter it, tie it to the right contract, and rate it properly. It’s not just “how much did we bill?” but “how much revenue can we actually recognize this month?”
Billing cycles can be quarterly or annual, but rev rec still needs to happen monthly. Billing and revenue recognition are no longer linked the way they used to be—they’re separate processes that must stay perfectly aligned.
And then, there’s the new EU act, which lets customers cancel long-term SaaS contracts with two months’ notice. That means you can no longer force customers into annual commitments. If they cancel mid-term, you can’t just refund what’s left. You have to reprice everything they’ve already used at the monthly rate, and then calculate refunds accordingly.
All of that feeds back into rev rec—billing has to issue credits, rev rec has to reverse portions of revenue, and accounting has to ensure it all reconciles. It’s an incredibly dynamic ecosystem now.
Natalie (Product Marketing Lead):
That’s such a good point. Everything you just described—the flexibility customers demand—is great for the front-end experience, but it creates a storm on the back end.
Flexibility upstream in pricing always means complexity downstream in revenue. And if your systems aren’t agile, you can’t keep up.
I’ve seen companies delay new pricing launches for months because they didn’t have a revenue recognition tool ready. You should never need to run an RFP just to change pricing.
So Jagan, can you give an example of a company that’s actually doing this right? Someone that’s handling this level of complexity well?
Jagan Reddy (CEO):
Absolutely. One of our flagship customers is a thought leader in the consumption-based business model.
They came to us four years ago looking for complete revenue automation. Their biggest challenge was managing contract modifications in a consumption context. They were adding new services, changing terms, offering bundles—it was chaos.
They chose RightRev because of our agility. We built a dedicated revenue engine for the consumption model, and today, they process millions of usage records daily through our system for automated revenue recognition.
Let me explain how it works: they sell credits—think of it like buying a Starbucks gift card. You buy credits, and then you spend them on whatever you want: coffee, food, anything. In their case, customers spend credits on compute, storage, or professional services.
On the order and billing side, you’ll see just one line item—credits. But in revenue recognition, we break that down to the actual services used.
Now, one day their sales team said, “Customers want to bring their own storage.” That meant they needed to apply discounts at the service level instead of the credit level.
That completely changes how you recognize revenue, because you have to reallocate based on the new performance obligations. We implemented logic that recalculates the standalone selling price (SSP) and dynamically reallocates revenue every time a discount or bundle changes.
That level of flexibility is what lets them experiment with new pricing models without breaking compliance. And now, we’re seeing a lot of AI companies adopting this same pattern—credits, usage, dynamic repricing, all supported by agile rev rec systems.
Natalie (Product Marketing Lead):
That’s such a great story, and it ties directly into what’s happening in the broader market. Usage-based, consumption-based, and outcome-based pricing are taking over, especially for AI and infrastructure companies.
But what people often forget is that those pricing models come with heavy COGS—GPU costs, compute costs, third-party fees, etc. You can’t just track top-line revenue; you need systems that prevent leakage and maintain profitability.
It actually reminds me of the mobile-first era 15 years ago. When we were building mobile pricing models, every SMS sent had multiple costs—aggregators, telecom providers, carriers like AT&T and Vodafone. Each transaction had hard costs, and if your systems weren’t tight, you’d lose money instantly.
We’re seeing the same thing now with AI. When you have variable, usage-based costs, revenue integrity and cost allocation become make-or-break factors.
Jagan Reddy (CEO):
Exactly. It’s history repeating itself in a new form. Back then, it was telephony and messaging; now it’s compute and tokens. The principle is the same.
When your COGS are high, the only way to stay profitable is through precise, automated tracking and revenue governance.
I always tell CFOs: your revenue system isn’t just for compliance—it’s a strategic asset. It helps you understand pricing performance, product profitability, and customer behavior.
We’re now entering a stage where revenue systems don’t just record what happened—they analyze, predict, and optimize how revenue flows. That’s where AI will play a huge role.
Natalie (Product Marketing Lead):
That’s a perfect segue. Let’s talk about compliance, because that’s where so many companies stumble—especially with usage-based models. Everyone knows ASC 606 and IFRS 15, but usage introduces new challenges.
Can you explain how compliance is evolving now that we have massive data volumes and interconnected systems?
Jagan Reddy (CEO):
Absolutely. Compliance used to mean one thing: making sure your journal entries matched your invoices and contracts. Today, it’s much more than that.
You have data flowing between multiple systems—CPQ, billing, cash collection, CRM, and rev rec. All of it needs to be perfectly reconciled.
Finance teams are now focused on data reconciliation and lineage—verifying that every order, invoice, and revenue record connects cleanly across systems.
With usage data, that challenge explodes. Some of our customers are ingesting hundreds of millions of usage records per month. Each one needs to be metered, rated, and mapped correctly to its contract and performance obligation.
We’re applying AI to automate that reconciliation process. Instead of finance teams running ten different reports and manually comparing them, AI surfaces mismatches automatically and highlights root causes.
That’s one of the biggest benefits of combining AI with a deterministic system. AI doesn’t make accounting decisions, but it helps identify issues faster and more intelligently.
Natalie (Product Marketing Lead):
That’s so critical. Automation and guardrails are what prevent human error and revenue leakage. And now, with usage-based and outcome-based models, customers also expect transparency—they want to log in and see their data in real time.
If your invoice doesn’t match what’s in the product, you lose trust instantly.
At the same time, pricing and packaging teams like mine are constantly experimenting—bundles, discounts, new billing tiers—and we rely on accurate data from rev rec to make those calls. Without clean downstream data, even pricing strategy becomes guesswork.
Jagan Reddy (CEO):
Exactly. And that’s why invoice alignment is becoming a major compliance requirement.
We had a customer with a $75 million contract. They had plenty of unused consumption left, but their usage data was accidentally duplicated three times in the billing system.
As a result, their credits were drawn down three times too fast. In one quarter, they lost $9 million in credits and over-recognized $9 million in revenue.
No one caught it until quarter close. There was no unique record ID on the usage data.
We implemented a checksum mechanism—a kind of digital fingerprint for each record—to prevent duplicates. That fixed it.
It’s small guardrails like that that make a huge difference. You can’t manually check millions of usage records. Automation and validation are non-negotiable in modern rev rec.
Natalie (Product Marketing Lead):
That’s a great example. And it really underscores how compliance and trust are intertwined. When systems are weak, you get revenue leakage, overbilling, and lost credibility with customers.
Before we close, I want to shift to AI again. Everyone’s talking about it right now. But as you’ve said before, revenue recognition has zero tolerance for error. How do you think about applying AI responsibly in such a deterministic space?
Jagan Reddy (CEO):
That’s a great question. In revenue, there’s no “close enough.” If I owe you $100 and give you $99, that’s wrong. There’s no rounding in this world.
That’s why we’re careful with how we deploy AI. We don’t replace the rules-based engine; we enhance it. The engine ensures determinism and compliance. AI layers on top to identify trends, surface issues, and recommend actions.
Right now, AI helps our customers work faster—it doesn’t make accounting decisions for them. Eventually, we’ll bring AI deeper into the process, but only once it can maintain audit-ready precision.
And I’ll echo something Marc Benioff said during the Salesforce keynote: AI only works when it understands the context of your data.
That’s been our philosophy at RightRev from day one. Our AI is built with domain context—it knows revenue, contracts, and performance obligations—so when it makes suggestions, they’re accurate, not guesses.
Natalie (Product Marketing Lead):
I love that. Context really is everything. When AI understands the meaning behind data, it becomes an accelerator. When it doesn’t, it creates noise.
You’ve built something incredibly powerful, Jagan. The story from spreadsheets to intelligent automation is such a great example of how finance and technology can evolve together.
Thank you for walking us through the journey—from RevPro to RightRev, from manual spreadsheets to AI-powered compliance.
Jagan Reddy (CEO):
Thank you, Natalie. It’s been great discussing this. We’ve come a long way from spreadsheets, but the mission remains the same: to empower finance teams with precision, insight, and agility.
