Max Freeman

VP of Sales @tryramp 💳. Ya gotta want it. NYC.

[Austin Hughes @austinh___]

We built Claude for outbound sellers.

AEs & SDRs can harness GTM engineering through chat across 40+ data sources, no technical skills required.

We’ve had 57,548 queries in our first few weeks of beta, growing 45% w/w.

The teams winning outbound in 2026 are already on it. Self-serve now live here: https://t.co/pwdSGTG0Hq

[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.

[Veeral Patel @vral]

Your next finance hire is an agent.

We’re looking for a few design partners to shape what it becomes.

[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 🙂

[Max Freeman @max11free]

Best follow on x by far is @vral — dropping banger after banger

[Veeral Patel @vral]

the best product we built at ramp was the culture - this is the best articulation i've seen yet from krish @krrishd

[Max Freeman @max11free]

@steijnpelle Proud of you brother 👊👊

[Eric Glyman @eglyman]

Introducing Stack.

The AI operating system that lets accounting firms take on more clients without hiring. Learns your firm's process, runs the close, posts the journals. Fully auditable.

We’re living through the biggest shift in accounting since the spreadsheet.

[Joe Lonsdale @JTLonsdale]

Very clever expansion by Ramp.

So impressive to see the rate at which they’re building and solving problems, and making so many professionals’ lives easier.

[Jeb Bush @JebBush]

Accounting has a 300,000-person talent gap and a 20-year low in new graduates. @tryramp Stack doesn't help firms prompt their way through it. It does the work. This is what AI-native actually looks like.

[Max Freeman @max11free]

@jasonbgardner @eglyman Jason -- would love to give you a tour asap and see how this would work for your company -- feel free to send me an email at [email protected]

[Greg Huntoon @GregHuntoon]

Switched to @tryramp recently for the first time in a couple of years. Bruh. They've been puttin' in WORK. Such a delightful experience.

Who says "delightful" about expense management?

I do, now.

[Proby Shandilya @ProbyShandilya]

Ramp's evolution into a cutting edge AI company with experimentation that rivals even the most frontier companies is something that will have HBS case studies written about it in 15 years

[Eric Glyman @eglyman]

we trained a .35b-parameter model to navigate spreadsheets better than opus 4.6.

normal corporate card company stuff.

[Teddy Riker @teddy_riker]

Over the past three months, weekly active users on the Ramp MCP has grown 10x as more customers reach into the product through Claude, ChatGPT, and other agents.

Sharing some learnings as we’ve scaled this product below (thread 👇)

[Rayan Dabbagh @rayandabbagh]

Had a pretty interesting experience opening a new business bank account as a founder.

We’ve raised a decent amount (can’t disclose, but >$1M - this matters later).

Tried @mercury first.

Got rejected 3 times.
No explanation. No feedback. Nothing.
We have enough capital (and as far as I know that's what they require) and just… silence.

Says a lot about the company they’ve built.

A few days later, I tried @tryramp after seeing their announcement.

Within hours, the account was live.
2 days later, the physical card was at my door.

But the real difference:
Execution.

Ramp had people in my inbox 24/7 making sure everything was unblocked, fast.

No friction. No guessing. Just speed.

That gap is everything.

In the AI era, the winners won’t just have better tech -
they’ll execute better than anyone else.

For what it’s worth, this reset my bar.

I know I want to build a @tryramp when it comes to customer experience.

[Kaito @kaycidot]

AI labs:

- Ramp
- OpenAI
- Deepmind
- Anthropic
- XAi

[Karim Atiyeh @karimatiyeh]

The same force driving your growth is your least-governed cost. You can tell me your AWS bill to the penny. Break it down by service, by team, by environment. You've been doing it for a decade.
Your AI bill? You're guessing.

No tool in the market connects token-level usage, invoice data, and card-level spend. So we built one.
Ramp AI Spend Intelligence is one place to track, govern, understand, and most importantly → optimize your AI spend.
Try it today.

[Geoff Charles @geoffintech]

Introducing Ramp AI Spend Intelligence. A way to track and control spend all the way down to the token level across providers.

Tokenmaxxing requires tokentracking. This is how your business will get ahead (or fall behind) with AI.

The best companies are investing aggressively in AI. And it’s working. Our data proves it. Since January 2025, average monthly AI token spend across Ramp customers increased 13x. Not 13%. Thirteen *TIMES.

But visibility into that spend has remained virtually nonexistent. No one knows how fast it’s adding up, where it’s going, who’s purchasing what. It’s a financial mess.

And AI spend is fundamentally different from every other cost category. A single prompt template change can triple your bill overnight. An agent stuck in a loop can burn through $50k before anyone notices.

No tool in the market connects token-level usage, invoice data, and card-level spend. So we built one.

Ramp customers can get early access today!

https://t.co/fFQApIZ3Pz

[Max Freeman @max11free]

@damccormick13 👊👊