Ramp

The AI finance platform that saves your business time and money. Trusted by 70,000+ teams.

[Ramp @tryramp]

chalk it up as yet another rampling dub.

[Ben Geist @b_geist]

Spoke at @cursor_ai’s conference, compile, yesterday hosted by @dwarkesh_sp on my research how introducing information in the latent space into LLMs can make the underlying models more efficient. Haven’t written on a chalkboard in years, was a great conference and experience 😄

[Antonio Bustamante @abustamante]

Last night, we almost became the "Company spent $500M in @AnthropicAI" story. We have an agent that hunts down real examples of files relevant to a prospect or customer for demos. The agent went haywire and Claude spun out 829 agents recursively, consuming about $1,000 in extra usage every 15 min.

I consider this a bug on Anthropic's side. Whether model-serving companies want to tell a different story is up to them, but the industry today is placing the onus for these catastrophic failures on the customer, and that's not right.

Just because the compute is stochastic doesn't mean the model labs get to wash their hands in the harness.

Back to the philosophy behind this: this is quite literally why we built bem. This is a simple task, with zero blast radius (other than our company card). What happens if you try to automate an actual critical workflow and you get one of these catastrophic cascading failures while connected to your DB? Who do you call? Good luck getting support. And the damage is done.

Shout out to @tryramp notifications. Quite literally saved us from waking up to a $40k bill from Anthropic.

[Ramp @tryramp]

@abustamante @AnthropicAI Always on the lookout 🫡 Leveraging AI shouldn’t mean blind spend…

[Ramp @tryramp]

Bet he expensed these

[Ramp @tryramp]

@karine_hsu run away, receipts

[Ramp @tryramp]

@eve_bouff @karine_hsu 🫶

[Ramp @tryramp]

official event review: cool

[sunshine vendetta @sunshinevndetta]

the @tryramp event last night in mexico city was cool

[Ramp @tryramp]

@JackHallock don't forget it

[Ramp @tryramp]

We shipped some absolute heaters this past quarter:

🥞 Ramp Stack
🛒 Purchasing Agents
💼 AI Token Spend Management
so much more.

See what’s New On Ramp:

https://t.co/C7gvaBkuMj

[Ramp @tryramp]

@pontusab @viktorhofte @diegozaks Remember this day

https://t.co/0LIDbfnOr1

[Ramp @tryramp]

peep this from your phone's charging port

[Brian Yip @brianjyip]

“Time is money, save both” is one of the best taglines I’ve ever seen @tryramp

[Ramp @tryramp]

Second biggest launch of the day

[Ramp Labs @RampLabs]

Today we’re releasing Ramp SWE-Bench: a private, production-grounded coding benchmark created from real engineering problems we've faced at Ramp.

[Ramp @tryramp]

@faxherbert Icons

[Ramp @tryramp]

We’ve seen the future and it is SaaS

[Leo Mehr @LeoMehr]

Services are the future. Today we launched Ramp’s AI services motion.

It's easy to buy an AI subscription. It's hard to transform your company to actually run on agents.

Here’s our entire strategy.

1) Why now

Services are the new software (Sequoia)

Human labor TAM >> software license TAM. The market is bearish on seats and subscriptions.

Every enterprise AI company is doing this -- the labs have poured billions into services partnerships and their own deployment functions. Superintelligent models alone are not enough.

Palantir proved this is a strong business model: deeply embed engineers, build on top of a powerful platform, and customize extensively.

2) The real problem

Companies want AI. But the gap between "we have AI tools" and "agents run our workflows and we spend way less time" is enormous.

What we've found across over 50 companies we engaged with: agents start replacing real work when there is: complete data, read/write access across systems, agent-friendly policies. Most big companies struggle because:
- processes live in operators' heads
- dozens of disconnected systems (legacy ERPs, endless one-off excel sheets, etc.)
- archaic software with poor or no API access

Good data in the right place is a hard prereq to working agents.

Also, vibing in localhost ≠ a production system your enterprise can rely on. You still need hosting, ci/cd, observability, feedback loops, good interfaces. And taste to know what's even worth automating.

Everyone has a bulldozer, but most jobs just need a shovel pointed at the right spot.

What companies usually need is to be made agent-friendly. That's exactly what we do.

3) What we do

We focus on what Ramp does best -- finance.

And we embed FDEs that:
-> understand your problems
-> identify high-leverage, high-impact workflows that fit agents
-> scope the solution
-> connect your data
-> capture your context
-> deploy agents and often bespoke software for humans to collaborate with them
-> drive the business metrics that matter

Discovery and scoping are crucial. Building is easier than ever and thus judgement about what to build is more important than ever.

We're not a generic AI services arm, we're finance domain experts. Across the spectrum of financial operations, we help companies find and frame the problems worth automating -- similar to the taste a founder has in choosing which problems are worth solving (ex-founders make great FDEs).

Here’s the stack we deliver:

- Production infrastructure. Shipping an index.html from Claude isn't the same as creating a repo, hosting in a cloud service, ci/cd, testing, setting up evals, managing memories and skills, adding feedback loops, ensuring uptime, incident management, etc. Agents don't one-shot production systems yet. Production software is hard -- we build, host, and run it for you in a single-tenant, dedicated cloud environment. Most operators don’t have the time, knowledge, or experience to do this e2e. We help abstract the low-leverage plumbing so they can focus on the essential parts of their jobs.

- Data connectivity. Most enterprises have data lakes, but data is often incorrect, stale, or entirely missing. And write interfaces vary dramatically. Ideally we can use MCPs or CLIs, but usually it’s poorly documented APIs, SFTP, manual uploads, and email.

- A context layer. Things people have done for years aren't written down, so an agent can't do them until we capture that context -- ranging from simple policies to complex decisions. This usually involves creating policy documents, shared agent memories, and skills.

- Evals and feedback loops. How you know an agent is doing a good job, and how it improves over time.

4) Why Ramp AI Solutions

We focus on finance because it’s the vertical we know deeply, have structural advantages, and are most differentiated:

- Data. 70k+ customers use our core product, over $200B in annual payments, years of vendor data, millions of transactions and bills monthly.

- Money-movement primitives and partnerships. Global money movement rails, partnerships with banks, Visa, Stripe, etc. You don’t want to vibecode international wires for bill payments.

- An intelligence layer on top: fraud detection from hundreds of millions of expenses, PO-to-invoice matching, state-of-the-art OCR, and fine-tuned models for accounting coding, spend routing, policy review, etc.

Unlike the labs, we’re not incentivized to sell tokens.

Ramp is an AI fiduciary and an impartial broker to deliver AI that is:
- model-agnostic -- we benchmark all the leading models (labs, open source) and fit the right one to each task
- and token-efficient by design

Our main incentive is business outcomes -- which is Ramp’s mission, to save our customers time and money.

I’m extremely bullish about our motion, and the broad industry growth of AI-native services.

If you're a finance leader trying to be more agent-native,

If you’re interested in joining our FDE team,

I’d love to talk 🙂

[Ramp @tryramp]

Can't spell finance without FIFA ⚽️

[Ramp @tryramp]

@josephpalbanese lfg

[Ramp @tryramp]

Chase the cheese
Get the cheddar
Build the copper
Finance forever

[jpa @josephpalbanese]

1. i joined @tryramp last november to work on ai

2. we’re building Penny — an agent for the finance work that lives across spreadsheets, inboxes, and late-night tabs

we’re looking for a few design partners to shape what it becomes. sign up to build together. (link in reply)

[Ramp @tryramp]

@litcapital @denk_tweets @beehiiv yellow and purple for life

[Ramp @tryramp]

@denk_tweets 🫶

[Ramp @tryramp]

@shamimai1 🙌

[Ramp @tryramp]

Our first launch geared specifically to support accountants is live, but how did Stack get built?

In this episode of Ramptables, Victor Pires spoke with Mike Liu, Krishnan Chandra, and Graeme Mounsey on what went into building the AI operating system for today's top accounting firms.

[Ramp @tryramp]

Say hello to the AI operating system that learns your accounting firm's routine work, then autonomously runs it.

Reconciliations, journal entries, transaction coding, the whole close. Your team only touches what needs a human.

This is Stack. Build it once, run it forever.