Ramp: Revolutionizing Corporate Finance with Intelligent Solutions
How Ramp is changing the way companies spend with delightful products and intelligent solutions
Since its inception in 2019, Ramp has achieved an impressive feat: reaching Centaur status with a remarkable $100 million in ARR. Serving over 15,000 businesses, Ramp has become synonymous with helping fast-growing organizations save both time and money.
At the heart of Ramp’s success lies its innovative offering – a sophisticated corporate credit card coupled with a centralized platform tailored for modern finance teams. This system empowers businesses to manage bill payments, vendor contracts, and automate finance operations seamlessly helping them close books accurately and on time. Customers on the Ramp platform utilize Ramp-issued credit cards that run on the Visa network and have pre-programmed controls (like limits and pre-approved vendors) to make payments without worry about receipts and reimbursements. These transactions are aggregated and tracked alongside other vendor spend, seamlessly pulled in from accounting software and ERPs like Quickbooks and NetSuite.
What sets Ramp apart is its product vision, which from the outset, aims to make the best finance platform while amassing unique data assets that increase in value through natural corporate spending. By aggregating and anonymizing billions of dollars worth of transactions across its diverse customer base, Ramp has embarked on a journey to integrate intelligence derived from this treasure trove of data. The result? Ramp Intelligence, a suite of features such as vendor price transparency, receipt tagging, expense categorization, and a finance copilot that integrates with Large Language Models (LLMs) to add context to every dollar spent. Ramp is uniquely positioned to create these intelligence features because they are trained on the data lake of unstructured natural language collected by Ramp.
Consider the scenario of a CFO at an early-stage SaaS company grappling with questions like ‘how much are we spending on Looker’, ‘can we afford our upcoming company retreat’, or ‘when is our Zoom license up for renewal’. The challenge in answering these questions lies in the variability of structure and language found in spending data across disparate sources. Ramp Intelligence tackles this challenge by normalizing and benchmarking spend categories using its customer base as a reference. Unlike other benchmarking solutions on the market, since Ramp is the card issuing entity it has visibility into all employee spending in a company and can apply AI models to this data. As it slowly builds integrations with the rest of the finance and accounting stack (like Stripe, Quickbooks, Workday, etc) the Ramp platform will be able to digest all financial inflows and outflows of the company to suggest areas of cost savings.
To ensure the integrity and security of financial data, Ramp’s data science teams have implemented stringent measures, including guardrails, evaluation metrics, and continuous monitoring of their AI systems. This ensures that the data remains accurate and free from corruption, safeguarding the trust of Ramp’s growing customer base.
Ramp’s advantage is in its ability to integrate these intelligent features into a user-friendly workflow seamlessly. By delighting users, Ramp achieves higher retention rates, paving the way for robust future growth. While competitors like G2 and Vendr can offer price transparency features, neither can match Ramp’s unique capability of integrating these features into a unified platform.
As Ramp continues its upward trajectory, it raises intriguing questions about the future. How will Ramp’s data assets evolve, and will customers be willing to pay a premium solely for these intelligent features? If Ramp Intelligence can be sold as a separate product, Ramp could potentially pivot to a recurring subscription model? This could align Ramp’s business with its core mission of saving customers time and money.
To learn more about Ramp Intelligence and its partnership with OpenAI see here: https://ramp.com/intelligence
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Hi Ankit- great post! In terms of customers, do you see this as a solution mostly geared towards small and medium sized businesses or large enterprises? What’s stopping big banks (who have the resources and relationships in some cases) in copying this strategy?
Hey Ankit, great work! I’m curious to get your take on the price/cost benchmarking aspect of the analysis. When I worked with our IT department on procuring software, vendors never charged the list price it was entirely dependent on a negotiated price that factored in account size, license structure (seat/enterprise), how many products we bundled together, etc. Do you think Ramp will be able to develop analytics that are advanced enough to recognize those patterns in the data and suggest deal structures or will it likely only compare direct line item costs and first order groupings like segmenting by company size?
Thanks, Ankit! This was a great read. It’s impressive to see how Ramp is revolutionizing the way businesses handle their finances. The insights provided on spending categories, vendor pricing, and potential areas for cost-saving seem valuable.
I’m curious about their data security standards. In what ways do you think Ramp’s emphasis on data security and accuracy sets them apart from their competitors, and how might this commitment benefit their customers in the long run?
Thanks for writing this Ankit! Fun fact, one of the founders of Ramp was my manager at the consulting firm I used to work at.
Ramp’s product vision has always been one step ahead of its competitors, and I’m glad you are highlighting how powerful it is to have peer-based benchmarks for these SMBs that may typically lack insight into what their best practices should be. I imagine there must be a world down the road where Ramp can even provide loans based on a customer’s spending characteristics, and how close to “ideal” it might be. I hadn’t considered before how powerful ChatGPT could be when layered upon such an expansive dataset, but it really does yield some very compelling use cases. One concern would be since B2B contracts tend to have a variety of conditions (early exit, termination/ integration fees, etc.), just how accurate are Ramp’s benchmarked prices, and if they may be misrepresenting the nuances of some of these vendor prices.
Thanks for sharing Ankit. Do you see a scenario wherein Ramp can build its own LLM based on the transaction data that it has collected. How do you see them potentially utilizing generative AI in the future?