Thoras.ai automatically allocates resources for Kubernetes workloads

When the Soviet Union invaded Afghanistan in 1979, Thoras.AI founders Nilo Rahamani and Jennifer Rahmani had no sparkle in their parents’ eyes; Forced to flee with his older siblings. Eventually they immigrated to the United States and settled in northern Virginia, where they gave birth to twin girls who grew up to become engineers, working for Slack and the Department of Defense, respectively, helping implement cloud native solutions.

In their previous job, the Rahmani sisters recognized the problem of engineers relying too much on intuition and not enough on data when acquiring Kubernetes workloads, and, inheriting some of their parents’ courage, decided to quit their comfortable jobs and launch Thoras.ai to solve the problem.

Today, the company announced $1.5 million in pre-seed funding.

“Thoras essentially integrates with cloud-based services and continuously monitors usage of that service,” company CEO Nilo Rahmani told TechCrunch. “So our goal is not only to predict demand, but also to automatically scale the application up or down based on expectations of increases or decreases in traffic.” It also notifies engineers of performance issues so they know they exist before they develop into something more serious.

They launched the company later this year and closed a pre-seed round a few weeks ago. They have released the first version of the product and are working in a live customer environment and generating revenue, which are all positive signs for an early-stage startup like this.

While the founders don’t want to know too much about what’s going on on the backend, the app connects directly to the company’s development environment, with no APIs involved and no information being transferred back and forth, as security and privacy are an important factor. their key design factors. Developers are presented with a dashboard that contains key information about the application’s resources, and she said they spent a lot of time ensuring a visually appealing user experience in the dashboard.

Image source: Thoras.AI

When it comes to artificial intelligence, the company is currently using more task-based machine learning rather than generative AI and large language models (LLMs). “A lot of the problems we face are systemic and there are a lot of numbers involved. So traditional machine learning and artificial intelligence can be used to predict consumption,” she said. That doesn’t mean they don’t foresee using the LL.M. in the future, but for now they want to be more proactive in looking for potential problems. They found that the LLM was more useful in troubleshooting at some point while filling out the product.

“We definitely have products in our roadmap that leverage the LLM, but natural language processing is very useful in situations where large numbers of words are involved, and now we want to get to the root of the problem before it actually happens, rather than just by Log to figure out what happened and why it happened,” she said.

They all certainly recognize that if their parents had stayed in Afghanistan, they might not have had the same educational opportunities, let alone the ability to start their own business. “I think about it every day, how privileged I am to be in a country where I can pursue my dreams. I talk about this all the time,” Niello said. “I would say it definitely helps push us to work as hard as we can and be successful,” Jennifer added.

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 unnamed strategic angel investors.