Software company Solid has raised $20 million in a Seed funding round. The company has built a platform that helps businesses turn their data into an automated system that supports artificial intelligence (AI).
Solid was created under Team8’s Venture Creation model. The funding round was led by Team8 along with U.S.-based investment firm SignalFire.
The company was founded in 2024 by Yoni Leitersdorf, who serves as CEO, and Tal Segalov, the CTO. Before this, Yoni had founded ideni, a company that was later acquired.
Yoni and Tal have known each other for almost 20 years. They first started working together during their military service in Unit 8200.
“Today, many organizations want to use artificial intelligence with their organizational data,” he said. “In sectors like healthcare, companies have vast amounts of data stored in large, often disorganized databases. The people managing day-to-day operations are often not technical and don’t know how to extract insights. When they have a question, they turn to an analyst, and it can take time to get an answer.”
“The problem is the accuracy of the data you can trust,” he said. “We decided to build a mechanism that organizes the data first and validates how AI systems interact with it. We create a highly accurate semantic layer, and on top of that we make organizational data accessible. Delivering that level of accuracy while maintaining strong security is a major challenge.”
“With the capital we raised, we plan to expand our sales team in the U.S., invest further in that market, and grow our R&D team in Israel,” Leitersdorf said. “We are being asked to provide a broad set of capabilities to help clients achieve meaningful results quickly.”
Even though companies spend a lot of money on data systems and AI tools, many still face a basic problem. Their business data and AI systems are not properly connected. The business meaning of the data often does not stay updated as the company grows or changes.
Because of this, different teams may use different definitions, numbers, or processes. This creates confusion and inconsistencies across the organization.
These gaps slow down AI adoption and reduce trust in AI results. In real situations, AI systems and automated agents often struggle to quickly access and correctly understand company data, making it harder to use them safely in important business workflows.
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