RamAIn Teaches AI to Use the Ugly Software Nobody Wants to Touch

Anyone who has worked inside a large company knows the moment. A number needs to move from one screen to another. The first system has no export. The second has no API. So a person opens two windows, reads the field on the left, and types it into the box on the right. Then does it again. And again. RamAIn, a Y Combinator Winter 2026 company based in San Francisco, was built for exactly that moment - and its bet is that the software doing the copying does not have to be a human.

RamAIn builds computer-use AI agents that operate software the way people do: by looking at the screen and acting on it. The agents read interface elements, click buttons, fill forms, and move through menus across web portals, desktop applications and the aging internal tools that run so much of the enterprise world. The company's line for it is short. "If you can see it, RamAIn can do it." The practical translation is that a workflow does not need a clean integration or an API to be automated. It only needs to be visible.

01 / THE PROBLEMThe last mile that APIs never reached

The automation industry has spent years selling a clean story: connect system A to system B through an interface built for machines. It works beautifully when both systems cooperate. The trouble is that a large share of enterprise work happens on software that never got the memo - carrier portals, hospital record systems, procurement tools, county government sites, the internal app a vendor stopped updating in 2014. There is no API to call. There is only a screen and a login.

Robotic process automation, or RPA, tried to fill that gap by recording a sequence of clicks and replaying it. For repetitive tasks it helped. But the recordings were brittle. Move a button, rename a field, add a pop-up, and the script fell over. Teams ended up maintaining armies of fragile bots. RamAIn's founders looked at that failure and reached a plain conclusion: the automation should understand the interface, not just memorize coordinates on it.

02 / HOW IT WORKSTeach it once, publish an API

The workflow is designed to feel less like programming and more like showing a new hire the ropes. A user demonstrates a task while the system watches the browser. That demonstration becomes a reusable workflow graph. The user edits the steps, defines the inputs and the exceptions, and then publishes it. What comes out the other side is a callable API with configurable triggers - a UI task that has been turned into something a machine can invoke on schedule or on an event.

Under that surface sits the infrastructure the enterprise actually asks about. Managed, isolated browsers with live view and replay. Browser profiles that hold login state and cookies so the agent stays signed in. Credential vaults for secrets. Handling for CAPTCHAs and blockers, with a path to escalate to a person when a machine should not decide. A knowledge base that carries the business rules an agent needs to follow. And, importantly for the people who sign off on these purchases, a complete audit trail with session recordings for every action taken.

03 / THE DIFFERENCELearn the interface, then move fast

Speed is where RamAIn draws its sharpest line. General computer-use agents from the major labs tend to deliberate over every action, reasoning through each click as if seeing the page for the first time. RamAIn's approach is to learn a system's UI-policies and interface structure up front, so that at run time the agent is executing a known route rather than rediscovering it. The company puts the result at roughly ten times the speed of those general models. It is a claim worth testing, but the logic is intuitive: a driver who knows the road beats one reading the map at every turn.

The second difference is durability. Because the agents adapt to interface changes rather than following fixed coordinates, RamAIn describes them as self-healing - able to absorb the moved button and the surprise pop-up that would have broken a classic RPA script. Reliability, not novelty, is the pitch. The third difference is human judgment. When context or a decision is genuinely required, the agent hands off to a person in real time, which keeps the automation from doing something confidently wrong.

04 / WHO IT'S FORThe unglamorous back office

RamAIn is aiming at the parts of a company that rarely make the keynote. Procurement teams onboarding vendors, creating purchase orders and matching invoices. Insurance brokers submitting to carrier portals and pulling quotes. Healthcare operations moving patient data and chasing prior authorizations. Revenue cycle staff running eligibility checks and filing claims. Finance groups reconciling AP and AR. Retail, pharmacy and logistics teams tracking inventory, transactions and shipments. The common thread is work that is high-volume, rule-bound, and stubbornly manual because the underlying systems were never meant to cooperate.

05 / THE FOUNDERSA chess player and a FAISS contributor

RamAIn was started by Shourya Vir Jain, its chief executive, and Vansh Ramani, its chief technology officer. The two met at IIT Delhi and left to build the company. Jain studied electrical engineering and later worked at McKinsey, where he watched enterprise teams spend their days fighting legacy systems - the direct exposure that tends to make a founder pick an unglamorous problem on purpose. He is also a FIDE-rated chess player, around 2118, who once ranked among the top players globally for his age.

Ramani comes from the research side. A computer scientist by training, he worked on scalable machine learning and representation learning, with published work at ICLR. His vector-search algorithm, Panorama, was merged into Meta's FAISS library - the kind of contribution that signals comfort with the parts of a system where performance is measured in milliseconds. The pairing reads clearly in the product: one founder who has felt the pain of the back office, and one who has spent time making retrieval fast.

06 / THE BUSINESSAgents sold as managed work

RamAIn sells to businesses, positioning its agents as a managed service that converts UI tasks into APIs rather than a tool a customer wires up alone. The company frames the value against the incumbent it wants to replace, citing roughly 70 percent lower automation cost than traditional RPA and deployment measured in minutes. Go-to-market runs through design partners and demos, with the team working alongside clients from scoping through go-live, and no custom code required of the customer.

On the trust questions that decide enterprise deals, RamAIn points to a compliance posture that includes ISO 27001, HIPAA and GDPR alignment, SOC 2-ready processes, encryption in transit and at rest, and a commitment that customer data is not used to train models. Those are table stakes for selling into insurance and healthcare, and the session-recording audit trail doubles as a sales argument: the buyer can see exactly what the agent did, and when.

The company is early. It is part of Y Combinator's Winter 2026 batch, working with primary partner Tyler Bosmeny, and has been hiring founding engineers across research, go-to-market and operations in San Francisco. Public funding is modest - a $130,000 seed tied to the batch - and the customer roster is not disclosed. Where RamAIn fits in the market is clearer than how big it will get: it sits between the general computer-use models from OpenAI, Google and Anthropic on one side and the entrenched RPA vendors on the other, betting that speed and self-healing reliability on real enterprise interfaces is a lane wide enough to build in.

The name itself hides the thesis. Read it slowly - RamAIn - and the "AI" sits inside a nod to RPA and to co-founder Ramani. The founders announced the company on LinkedIn with three bowls of ramen and the phrase "super-fast computer-use." It is a small joke about a serious wager: that the most valuable AI in the enterprise might be the kind willing to do the tedious clicking everyone else has been trying to route around.