RamAIn Wants AI to Use Computers the Way Humans Do, but Faster

The Macro: Computer Use Agents Are the Future, but They Are Still Too Slow

The idea of AI agents that control computers the way humans do is compelling. Point them at a screen, tell them what to do, and they figure it out. No API integration. No custom code. If you can see it, the agent can do it. Anthropic demonstrated this with Claude’s computer use capability. OpenAI has their own version. The concept works.

The problem is speed. Current computer use agents follow the same decision loop: take a screenshot, send it to a vision-language model, get a decision, execute the action, repeat. Every action requires a full model inference. A task that takes a human 30 seconds might take a CUA two minutes because of the constant screenshot-think-act cycle. And each cycle costs money in API calls.

For simple demonstrations, this speed penalty is acceptable. For production automation where you need to process hundreds of tasks per day, it is a dealbreaker. Traditional RPA tools like UiPath and Automation Anywhere are faster because they use pre-programmed scripts. But they are brittle, require expensive specialists to set up, and break when the UI changes.

The ideal solution would combine the flexibility of computer use agents (they adapt to any interface) with the speed of RPA (they execute quickly). That means pre-training agents on specific interfaces so they do not need to figure out the UI from scratch every time.

The Micro: Pre-Trained Agents That Skip the Slow Loop

Shourya Vir Jain and Vansh Ramani cofounded RamAIn. Shourya has an EE degree from IIT Delhi, was a McKinsey consultant specializing in enterprise AI, and previously built and sold Genoshi, an AI studio generating six-figure revenue. He is also a competitive chess player with a FIDE rating of 2118. Vansh studied CS at IIT Delhi, published research at ICLR and ACS, and developed the PANORAMA algorithm that was integrated into Meta’s FAISS library. Both bring serious ML research credentials. They are a two-person team from San Francisco, part of YC Winter 2026 with Tyler Bosmeny.

RamAIn builds infrastructure to pre-train computer use agents on specific interfaces. Instead of the screenshot-VLM-decision-repeat loop that makes current CUAs slow and expensive, RamAIn’s agents already understand the target application. They read, write, and execute tasks on local applications at a speed that makes CUA-based automation practical for real business workflows.

The product deploys in days without RPA specialists or brittle scripts. It works on macOS and Windows. The pricing is listed as free at $0 USD, which suggests they are in growth mode and monetization will come later.

The Meta FAISS integration is a notable technical credential. FAISS is one of the most widely used similarity search libraries in production ML systems. Having a cofounder whose algorithm is integrated into it signals genuine ML research depth.

The Verdict

RamAIn is tackling the right bottleneck. Computer use agents are useful in theory but impractical in production because of speed and cost. Pre-training on specific interfaces is the obvious solution, and the team has the ML research depth to execute it.

The risk is that the major AI labs solve the speed problem at the model level. If the next generation of vision-language models can process screenshots fast enough that the CUA loop is no longer a bottleneck, the pre-training advantage diminishes. But model improvements tend to be incremental, and the 10x speed difference RamAIn is targeting is a gap that model improvements alone will not close quickly.

In 30 days, I want to see benchmarks. How fast is RamAIn compared to vanilla CUA approaches on the same task? In 60 days, the question is enterprise adoption. Are companies using RamAIn to automate real workflows? In 90 days, I want to know about the pre-training pipeline. How long does it take to pre-train an agent on a new application? If the answer is hours, not weeks, RamAIn can cover any enterprise software quickly.