Do you get lots of spammy LinkedIn requests but some good ones? Pointed Codex w/ browser use on my 1k LI request queue. Reviewed batch of 30 by hand. Then hard cases (~20%) of next 200. Then made a skill for the rest + runs weekly. LI request approvals on autopilot ✅

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 🙂

Today we're thrilled to announce the @a16z FDE fellowship! A new 8-week cohort for world-class FDEs and Applied AI leaders at the forefront of deploying AI into real-world enterprises. We've already got an incredible lineup, including... - Perry Ha, VP, Agent Product at Decagon - Senta Knuth, Enterprise Product Deployment at ElevenLabs - Rohan Chandra, Regional Director, AI Deployment at Cursor - Isabel Gomez, Technical Deployment Lead at OpenAI, prev Palantir - @nikogrupen, Head of Applied Research at Harvey - @barrald, Cofounder & CEO at Hex, prev Palantir - @lkothari, GM, AI FDE at Snowflake, prev VP Product at Scale - @LeoMehr, director of engineering at Ramp - @zkevinbai, founding FDE at Rippling, prev Palantir - Gene Karshenboym, Director of Engineering, Operations and Strategy at Google, prev CEO at Phiar and many more. Join us! Apps went live today, and the fellowship kicks off in July 2026. Link in thread: