Solid Launches Seed Round To Tackle Untrustworthy AI
Solid, a software company based in New York City, just landed $20 million in Seed funding. Team8 and SignalFire led the round. With this fresh cash, Solid’s jumping right in—hiring more people, rolling out new products, and working hard to keep up with their growing customer base.
At the front of it all are co-founders Yoni Leitersdorf, the CEO, and Tal Segalov, the CTO. Here’s what they’re tackling: Enterprise AI has a messy problem. Most AI systems churn out insights you can’t really trust, mostly because they don’t “get” what business data actually means.
Companies juggle all kinds of data sources, dashboards, and tools, and each one has its own way of defining metrics. Business rules keep changing, too, so the context gets messy and sometimes outdated. Solid’s platform steps in to sort all that out.
“AI isn’t failing because it lacks intelligence,” Leitersdorf said. “It’s failing because it doesn’t understand how businesses actually work.”
How Solid Builds A Single Source Of Truth
Solid’s platform creates a single source of truth for business meaning. It applies semantic models that capture and automate the maintenance of business logic over time. AI systems, dashboards, and reports all start with the same core, so everything stays consistent and reliable.
The platform connects with big names like Snowflake, Databricks, and BigQuery. When someone tweaks a metric, the platform updates definitions on its own. That way, those AI-powered insights stay sharp and accurate, even as things shift and change.
Semantic agents act like “semantic engineers,” a new discipline Leitersdorf describes. They systematically teach AI the meaning of business data. This reduces manual work for data analysts while increasing the reliability of automated workflows.
According to Solid, AI accuracy on real-world data rises from roughly 20–30% to over 85% after deployment. Manual efforts to maintain and test business semantics drop by 50–70%. The system allows enterprises to deploy AI faster. Projects that once took one to two years can now be operational in under six months.
Addressing Complex Enterprise Data
Enterprise data is fragmented, complex, and constantly changing. Business definitions vary across teams and departments. Without a single semantic layer, AI systems struggle to provide trustworthy answers.
Solid addresses this by interpreting the business logic embedded in dashboards, documentation, and human knowledge. It captures nuances unique to each organization. The platform continually validates and updates definitions to reflect operational changes.
Leitersdorf said the system enables companies to automate workflows and make confident decisions without replacing existing data systems. This ensures AI can act reliably in mission-critical areas like finance, healthcare, and operations.
SignalFire Principal Ryan Wexler highlighted the challenge. “AI needs consistent business definitions to work. Historically, those definitions were manual, brittle, and impossible to maintain at scale. Solid automates the creation and ongoing maintenance of business meaning.”
Immediate Benefits And Customer Access
The startup is offering all new customers a free, 30-day trial of its platform. This allows enterprises to see how much AI reliability can improve. Early adopters report significant reductions in errors and faster deployment of AI-driven workflows.
Solid’s semantic layer is built for the heavy hitters—banks, hospitals, companies with years and years of data piling up. These organizations see the upside right away. Once you nail down your definitions and let AI keep everything up to date, the system doesn’t stumble over messy or conflicting info anymore. It just runs smoothly.
Since the platform connects with the databases companies already use, there’s no need for expensive migrations or ripping out old systems. So, customers end up with AI that’s not just efficient, but reliable too.
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Scaling Operations And Global Impact
Solid plans to use the new funding to hire more salespeople in the U.S. and grow its R&D team back in Israel. The company wants to help enterprise clients move faster with AI-ready semantic models.
This investment shows that investors really believe more and more big organizations want reliable AI solutions. Solid’s platform is designed to reduce human effort while enabling AI systems to operate with trust and accuracy.
Leitersdorf predicts that semantic engineering will become a critical role in enterprises. Today’s data analysts will spend more time teaching AI business meaning as AI automates routine analytics work.
Its focus remains on organizations that require reliable AI across multiple datasets and business systems.
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