Users and social media mentions highlight Ramp AI's strengths in automating procurement and finance tasks, saving significant time and resources for companies. Their partnerships, such as with Visa, suggest strong industry connections and growing influence. Pricing sentiment is generally positive due to the efficiency and cost savings Ramp AI offers, though some users hint at ongoing development and improvements needed in their systems. Overall, Ramp AI holds a solid reputation for innovation and practical application in business automation.
Mentions (30d)
60
13 this week
Reviews
0
Platforms
3
Sentiment
1%
2 positive
Users and social media mentions highlight Ramp AI's strengths in automating procurement and finance tasks, saving significant time and resources for companies. Their partnerships, such as with Visa, suggest strong industry connections and growing influence. Pricing sentiment is generally positive due to the efficiency and cost savings Ramp AI offers, though some users hint at ongoing development and improvements needed in their systems. Overall, Ramp AI holds a solid reputation for innovation and practical application in business automation.
Features
Use Cases
Industry
financial services
Employees
1,600
Funding Stage
Venture (Round not Specified)
Total Funding
$2.8B
Brilliant misfits --> https://t.co/VAQd0mXylc https://t.co/vhCuioAu2w
Brilliant misfits --> https://t.co/VAQd0mXylc https://t.co/vhCuioAu2w
View originalPricing found: $0/mo, $15/mo, $.65, $.65, $.65
LLM delegation - probing task handoff efficiency and economics
So I've been dabbling a bit with multi-LLM orchestration/delegation workflows lately (eg see [Using Claude code to delegate to mistral/deepseek](https://www.reddit.com/r/ClaudeAI/comments/1tjfyh0/i\_used\_claude\_code\_to\_build\_while\_delegating/)). The thread always being how to minimize Claude token usage while still benefiting from Claude's planning and overall code supervision. Offloading context scan and execution is a definite win already (notably against session/weekly quotas for Claude Pro users), so wanted to optimize further the handoff at interface level, beyond standard prompt engineering practice. I'm an electronics engineer by training so I naturally thought of 'black box tests' we run measuring output against different input signals (pulse, step, ramp etc) — this allows us engineers to characterize systemic signal loss (transfer function, impedance mismatch..). I offered the idea to Claude to apply these principles to code, and he came up with a battery of code tests. Setup is Orchestrator (Claude code) delegates tasks to another model (mistral or deepseek) via a cli (vibe or opencode). Orchestrator then receives output and evaluates it against functional tests. *Repo + methodology:* [*https://github.com/pcx-wave/handoff-probe\*\](https://github.com/pcx-wave/handoff-probe) *— if you want to dig in, start with Readme (the 3-layer setup), Methodology (signals), Results (scores), Economics (why delegation saves your session budget).* **Main takeaways :** \- cli/model differences : mainly on tooling and context management. Both CLIs are equally usable (i personally prefer Vibe), but models adapt their output format to task complexity — prose for simple tasks, file writes for complex ones — which creates an inconsistent interface for the orchestrator. Worth enforcing explicitly in the prompt rather than assuming. \- environment definition : critical. A lot of tests failed not because of model incapability, but because the measuring system wasn't reading output in the right way. So setting harness properly (I/O + reading) is critical, and Claude repeatedly failed at self-diagnosing. Almost philosophical : a model will struggle to self-evaluate, it NEEDS external review. Encoding sanity guards (eg 'if you see result score = 0, its likely an error') was one of the more useful things I did. \- don't trust the code looks right, run it. I measured at three levels : format compliance, structural checks, actual execution. Classic prompt engineering stops at the first two. On the hardest tasks, structural checks said 100% success while execution dropped to 58%. The gap between "looks right" and "works right" is where delegation actually fails. Example with async refactor: Structural check: is async def present -yes, 100%. Functional test: does await get\_data() actually run - 58%. Models refactored the signature but left the internals broken. Fix in next point. \- prompt engineering has a measurable impact, although i thought it would be higher. Adding the exact function signature and return type to the delegation prompt recovered about 15% of failures on complex tasks. It costs extra prompt overhead - but you recover costs in the long run by avoiding failures and repeated runs. \- how delegation actually saves your session budget : delegation costs more orchestrator tokens per task than doing it directly, the prompt overhead is real. But when Claude works directly it reads files, and those accumulate in context and get re-read silently on every subsequent turn. With delegation the sub-model handles all of that as none of it enters Claude's context. Savings : \~66% quota reduction on a 10-file codebase, 88% on 30-file one, vs direct. The crossover is simply about 4 source file of reads, below that, direct wins, above it delegation wins by a growing margin. I do not claim this as a benchmark (that would require way higher number of runs, and i'm not specifically trained in the llm field), it's rather a home-made eval tool that can be suited to others running orchestration setups and wanting to probe your delegation setup efficiency at each model interface. submitted by /u/pcx_wave [link] [comments]
View originalRamp launched an AI operating system for accounting firms
submitted by /u/ProfessorDeep8754 [link] [comments]
View originalCalm down, ChatGpt is down
https://preview.redd.it/3p21pyr69u4h1.png?width=1710&format=png&auto=webp&s=855ade35a19b7d22e2358b97cb5320201d4a9721 https://preview.redd.it/knddawx79u4h1.png?width=735&format=png&auto=webp&s=2071f2dea1fd46a7b301e8971cf63d3b7813d79d ChatGpt down, System Status OK? submitted by /u/DarKresnik [link] [comments]
View originalIs this tagline intentional?
submitted by /u/JoshMJohns [link] [comments]
View originalSpaceXAI locked Anthropic into paying them $1.25 billion per MONTH for compute
submitted by /u/Illustrious-King8421 [link] [comments]
View originalAnthropic is paying SpaceX $15 billion per year
According to SpaceX’s IPO filing, Anthropic is paying SpaceX $1.25 billion per month through May 2029 as part of the massive compute deal the two companies signed earlier this year. That works out to roughly $15 billion per year. The deal is huge for Anthropic because the company’s revenue is rapidly growing, but it has also been limited by a lack of available compute. More compute means more capacity to train and run its AI models. It is also a massive win for SpaceX. The company reportedly brings in around $18 billion in annual revenue, so a single customer paying $15 billion a year for compute is a serious boost. Anthropic and SpaceX announced the deal last month, but they did not give financial details at the time. The monthly payments were revealed in SpaceX’s IPO filing released Wednesday. SpaceX said the payments will be lower in May and June as the deal ramps up. Anthropic also announced just before the filing became public that it is expanding beyond SpaceX’s Colossus 1 facility and will also use Colossus 2. Tom Brown, Anthropic’s co-founder and chief compute officer, said the company is “expanding our partnership with SpaceX” and will be scaling up Nvidia GB200 capacity in Colossus 2 throughout June. SpaceX also made it clear this may not be the last deal of its kind. “We expect to enter into additional similar services contracts,” the company said in the filing. SpaceX also said it has enough capacity to support its own AI models while still meeting its obligations under these outside compute agreements. Source: https://www.axios.com/2026/05/20/anthropic-spacex-compute submitted by /u/Luka77GOATic [link] [comments]
View originalIt’s automation on the d̶a̶n̶c̶e̶ finance floor
It’s automation on the d̶a̶n̶c̶e̶ finance floor
View originalPublicis buys LiveRamp for $2.5 billion in agentic AI data play
submitted by /u/danie-l [link] [comments]
View originalRT @GregHuntoon: Switched to @tryramp recently for the first time in a couple of years. Bruh. They've been puttin' in WORK. Such a delightf…
RT @GregHuntoon: Switched to @tryramp recently for the first time in a couple of years. Bruh. They've been puttin' in WORK. Such a delightf…
View originalAntrophic is now the front runner of AI Boom
submitted by /u/AloneCoffee4538 [link] [comments]
View originalOpenAI's US business subscription fell behind Anthropic
https://preview.redd.it/jylmclk1q81h1.png?width=731&format=png&auto=webp&s=90eee669e48251c341e3781952926b60afd71676 https://ramp.com/leading-indicators/ai-index-may-2026 OpenAI's US business subscription appears to be shrinking, all in spite of offering 17.5% "guaranteed" return, giving away free months, aggressive discounts, and rather clear enshittification of Anthropic's service and token inefficient Opus 4.7 (noted in the article). submitted by /u/NandaVegg [link] [comments]
View originalRT @zack_field: Ramp Agent Cards is now self-serve! > ramp funds enroll ask your agent the next time you need to make a purchase! https:/…
RT @zack_field: Ramp Agent Cards is now self-serve! > ramp funds enroll ask your agent the next time you need to make a purchase! https:/…
View originalAmerican fintech exec PLEADS the fifth in the face of European regulatory COMPLIANCE https://t.co/C3FCFtNdje
American fintech exec PLEADS the fifth in the face of European regulatory COMPLIANCE https://t.co/C3FCFtNdje
View originalWe're giving away two tickets to a FIFA World Cup 2026 group stage match 🤙 entering is easy: 1. join r/Ramp on Reddit (https://t.co/x6wHUu8EXB) 2. drop a comment on our pinned giveaway post Enter b
We're giving away two tickets to a FIFA World Cup 2026 group stage match 🤙 entering is easy: 1. join r/Ramp on Reddit (https://t.co/x6wHUu8EXB) 2. drop a comment on our pinned giveaway post Enter by 5pm PT on 5/21. No purchase necessary. Open to US residents 21+, excluding NY, https://t.co/BQXE2PWRtv
View originalYes, Ramp AI offers a free tier. Pricing found: $0/mo, $15/mo, $.65, $.65, $.65
Key features include: Automated expense tracking, Real-time financial insights, Customizable spending limits, AI-driven budgeting recommendations, Seamless integration with accounting software, Expense report generation, Multi-currency support, Fraud detection and alerts.
Ramp AI is commonly used for: Streamlining expense management for small businesses, Enhancing financial visibility for remote teams, Automating approval workflows for expense reports, Providing insights for budget planning and forecasting, Facilitating compliance with corporate spending policies, Tracking travel expenses for employees.
Ramp AI integrates with: QuickBooks, Xero, NetSuite, Slack, Zapier, Expensify, Stripe, PayPal, Google Sheets, Microsoft Teams.
Based on user reviews and social mentions, the most common pain points are: token usage, spending limit.
Based on 168 social mentions analyzed, 1% of sentiment is positive, 98% neutral, and 1% negative.
