When the Soviet Union invaded Afghanistan in 1979, Thoras.ai founders Nilo and Jennifer Rahmani were never seen by their parents, who fled with their older siblings. I was forced to. Eventually, they immigrated to the United States and settled in Northern Virginia, where they gave birth to twin girls. They grew up to become engineers, working for Slack and the Department of Defense, respectively, helping her implement cloud-native solutions.
In their previous jobs, the Rahmanis sisters recognized that there was a problem with the way engineers procured Kubernetes workloads, relying too much on intuition and not enough on data. Inheriting some of their parents' courage, they decided to quit their comfortable jobs and start Thoras.ai to solve a problem.
Today, the company announced a pre-seed investment of $1.5 million.
“Thoras essentially integrates with cloud-based services and consistently monitors the usage of those services,” company CEO Nilo Rahmani told TechCrunch. “So the goal is not only to predict demand, but also to autonomously scale applications up or down in anticipation of increases or decreases in traffic.” There is also a feature that notifies engineers of performance issues to help them understand that they exist.
They launched the company just after the new year and closed on pre-seed funding just a few weeks ago. They have already released the first version of their product, working in a real customer environment and generating revenue, all positive signs for such an early stage startup.
The founders didn't want to know too much about what was happening on the backend, but security and privacy were key, so the application connected directly to the company's development environment without going through an API, and information was passed back and forth. There was no such thing. Design elements for them. Developers view dashboards that contain important information about the application's resources. Nilo says he spent a lot of time creating a visually appealing user experience with his dashboard.
Image credit: Thoras.ai
In terms of AI, the company currently uses task-based machine learning more than generative AI or large-scale language models (LLM). “Many of the problems we face are systemic problems and involve a lot of numbers. So we use traditional machine learning and AI to predict what consumption looks like. “You can,” she said. This doesn't mean she doesn't foresee using an LLM in the future, but for now she wants to be more proactive in looking for potential problems. They believe her LLM will be more helpful for troubleshooting after the fact at some point as they finalize the product.
“The roadmap definitely includes products that leverage LLM, but natural language processing is very useful in situations where there are a lot of words. We want to get to the root of the problem, rather than just looking at logs and understanding after the fact what happened and why it happened,” Nilo said.
Both are certainly aware that if their parents had remained in Afghanistan, they may not have had the same educational opportunities, not to mention the ability to start their own businesses. “There's not a day that goes by that I don't think about how blessed I am to be in a country where I can pursue my dreams. I talk about it all the time,” Niro said. Jennifer added, “I think it definitely motivates us to work hard and be successful.”
Today's pre-seed investment was co-led by Storytime Capital and Focal VC, with participation from Hustle Fund, Precursor Ventures, Pitch Fund, and several anonymous strategic angel investors.