Rohan Shah

FDE Ops @ Ramp | prev. @ Aetion (Acq. by Datavant), @mbxcapital

[David Booth @davidbooth]

Excited to announce the inaugural class of @a16z Ops Engineer fellows!

~50 ops engineering leaders selected out of thousands of apps from talented people around the world.

They sit at the intersection of operations and engineering, scaling operational rigor within and across teams.. they are building the internal tools that consolidate the tooling stack, designing the data and context layers that agents draw on, deploying agentic workflows with human checkpoints, and driving adoption inside their companies.

We have folks like Ben Booi (@BenjaminBooi) and Rushaabh Saliya at Opendoor, Chris Jones (@chrisojones) at Shopify, Dilan Shah (@dilan_shah) from Cresta, Kevin Chu (@realKevinChu) from ElevenLabs, Saya Iwasaki (@iiiwokk) at Notion, Jordan Shapiro at Handshake, Christina Huang at Cognition, and Keith Gliksman (@KeithGliksman) at Tennr,

Others are are world-class COOs and chiefs of staff redefining how AI gets used across organizations. They’re deciding which workflows to automate first, the KPIs that prove it’s working, and are leading the teams that put it to work. Folks like Caroline Ingeborn (@caroingeborn) at Luma AI, Brandon Gell (@bran_don_gell) at Every, Rohan Shah (@RohanxShah) at Datavant, Gabriella Garcia (@reallygabriella) at Warp, Matt Stehle at Formation Bio, and Meera Shah at Glean.

Others are taking how software gets built to the next level, creating new products and leading the teams that ship them, like Claire Vo (@clairevo) at ChatPRD, Seb Goddijn (@sebgoddijn) at Yaklabs, TK Kong (@tkkong) at Concept, Matt Portnov (@MattnPortnov) at Decagon, Owen Williams (@ow) and Sharadh Krishnamurthy at Stripe, Andrew Qu (@andrewqu) at Vercel, and Maxwell Nardi (@maxwellnardi) at Runlayer, @davidgan at SpaceX/@bot, @matthew_busel at Basis.

And another ~25 awesome folks we can't squeeze into one post !! (see full list in comments)

Missed out on this but want to stay in the loop for future cohorts? sign-up at https://t.co/frfnA7R7If

[Rohan Shah @RohanxShah]

Time to buy some Slovenian domain names

[Aaron Rupar @atrupar]

Trump tells the UN he is officially changing the name of Artificial Intelligence to "Super Intelligence" and says all US government documents will now be changed to refer to "SI"

[Rohan Shah @RohanxShah]

@AnjneyMidha 👋

[Rohan Shah @RohanxShah]

@vercinyc Invite

[rohan @rohanrkumar]

ive been overhearing that people have just been in strategy meetings internally at companies with their idea as just "hire rohan"

[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 🙂

[Eric Glyman @eglyman]

Today, Ramp raised $750M at a $44B valuation.

Last time we grew this fast, we were 1/20th the size.

For 2000 years, business was built on two pillars. Today, a third: intelligence.

It’s your least governed cost. It’s also your single greatest opportunity.

[Rohan Shah @RohanxShah]

@Jonnyvandel factory

[Rohan Shah @RohanxShah]

Someone who just got a Mac mini is punching the air rn

[Claude @claudeai]

New in Cowork: scheduled tasks.

Claude can now complete recurring tasks at specific times automatically: a morning brief, weekly spreadsheet updates, Friday team presentations.

[Rohan Shah @RohanxShah]

@WorkflowWhisper RETAINER

[Rohan Shah @RohanxShah]

@namyakhann @claudeai @framer LANDING

[Rohan Shah @RohanxShah]

@aurexcoder0 UGC

[Rohan Shah @RohanxShah]

@TommiPedruzzi Guide

[Rohan Shah @RohanxShah]

@NickDiFabio1 KDP

[Rohan Shah @RohanxShah]

People skip doses bc getting a renewal is annoying. Eager to see how $4 and no appointment changes that. Epic makes me call my doc/pharmacy for regular prescriptions (get me off hold pls)

There isn’t a federal license for AI prescribing and medicine varies state-by-state. Other states may wait to see 12m of data in Utah’s sandbox, but starting with the “boring” meds is smart to approach regulators

[Matt Pavelle 🇺🇸 @mattpavelle]

Not SciFi anymore. @Doctronic is now the first AI in America (the world?) legally authorized to practice medicine.

We're live in Utah, renewing prescriptions with no doctor required.

@politico : https://t.co/bJ5CsmqBXf

[Rohan Shah @RohanxShah]

Our parents went to their doctor first. We check Reddit and the group chat. Why?

https://t.co/EauCGd3jyF

[Rohan Shah @RohanxShah]

@Duderichy this is a paid ad for @HingeHealth and @swordhq

[Rohan Shah @RohanxShah]

Found a novel tech enabled services model for day care. DMs are open

[Stuart Blitz @StuartBlitz]

Crazy new health tech funding round

[Rohan Shah @RohanxShah]

I just want my Aetna wrapped

- Claims denied then approved when asked again: 3

- Hours on hold: 14

- Prior auths that took longer than the actual treatment: 2

- My top artist was Hold Music

[Rohan Shah @RohanxShah]

@VibeMarketer_ PROPOSAL