Roy Luo
84 posts
There are two non-negotiables in accounting: the books must be correct, and they must be ready on time. For decades, companies have satisfied those constraints through an extraordinary amount of manual effort. Highly trained professionals code transactions, re-approve familiar expenses, reconcile mismatches after the fact, and compress all of it into the ritual of month-end close. It works. But it is fundamentally retrospective. Today, @tryramp is introducing an Accounting Agent designed around a different premise: what if bookkeeping happened as the business operated, rather than after it? The agent captures, codes, reviews, validates, accrues, and reconciles spend continuously. It learns directly from the people who understand the nuances best, the accounting team itself, and applies that context in real time. At @perplexity_ai, where velocity is part of the company’s identity, this has allowed their team to stop choosing between speed and accuracy. The majority of transactions are now coded automatically while remaining audit-ready, enabling close to start on day one instead of day thirty. What’s been most striking is how the system learns the subtle, company-specific logic that historically lived only in human judgment. As Jim Romano, CFO at @statesidevodka, described it, the agent is already identifying patterns like when spend belongs in samples rather than travel and entertainment — the kinds of decisions that typically require institutional memory. As he put it, the goal is simple: finance teams should focus on exceptions, not the easy stuff. We’re also seeing the second-order effects emerge quickly. Teams report spending dramatically less time reviewing transactions and substantially more time on planning, analysis, and growth. As one CFO told us, “What used to take hours of manual review now happens automatically. I’m spending nearly all of my time thinking about where the business should go, not retracing where it’s already been.” There is a broader shift underway in accounting. The central question is moving from “what parts of close can be automated?” to “should close even be a discrete event at all?” One belief that increasingly guides our work at Ramp is that information latency inside companies is an invisible tax. When financial truth lags behind operational reality, organizations make slower and often worse decisions. As transaction data becomes inherently digital and systems become capable of learning institutional context, continuous close stops being aspirational and starts becoming inevitable. One thing that surprised us while building this: accounting isn’t constrained by a lack of rules — it’s constrained by how many of those rules are unwritten. Much of financial operations lives in patterns that experienced teams simply know. Seeing software begin to absorb and apply that tacit knowledge has been one of the clearest signals that accounting is entering a new phase. Accounting has always been the record for business reality. Our goal is to help it become something closer to real-time truth. Proud of the team, and grateful to the customers building this alongside us.
We’ve raised $30B in funding at a $380B post-money valuation. This investment will help us deepen our research, continue to innovate in products, and ensure we have the resources to power our infrastructure expansion as we make Claude available everywhere our customers are.
Exclusive data: startups are learning what AI profits look like 👀 A new survey of 300 AI startups found that they project gross margins to exceed 50% this year -- and use Google Gemini models more than Anthropic 👀 My breakdown in @UpstartsMediaCo 👇 upstartsmedia.com/p/data-ai-star…
Meet Brian. Brian’s been carrying accounting on his back for a long time. Super Bowl Sunday, he finally gets backup.
RIP 2026_Budget_v8_FINAL.xlsx. Laid to rest on 1/22/2026. Ramp Budgets is now live. Real-time visibility into every dollar your company spends.
At Ramp we ship a new major feature every day - it's impossible for leaders stay up to speed. To keep a high bar without slowing folks down, teams can ship to early access tier whenever they want but need review for general release. Crazy fact: 10% of customers opt into early access because they can't get enough. That's 5000+ businesses. Plenty to work with. To release to general public, teams need to prove this product works and get sign off for heads of eng, product and design. Here is our template: 1. What did we build and why 2. What's the demo in < 3mn (loom) 3. Did we meet our goals in early access (hex dash) 4. Are customers raving about this (LLM on Zendesk tickets, Sprig surveys, and Gong transcripts) 5. Will customers easily discover and start using it (first time user journey) 6. Is sales ready to sell, AM ready to activate, and support ready to troubleshoot 7. Do we have a clear rollout plan (launch tier, pricing, coms) This helps us document decisions, serves as a strong checklist, and feeds our release notes. Most importantly, it keeps the bar high (we expect at least 1 rev of feedback). The best part: most of this template is automated using AI connected to the rest of our business sytems. Leadership has 48h to review or it ships. We think it's a great way to balance speed & empowerment with process and quality.
Welcome to Secured—the Drata community 🔐 Secured is a global community for compliance and security advocates seeking to learn, connect with peers, and improve their organization’s security posture. See what Secured is all about 👇 community.drata.com
