Enterprise AI is entering a new stage, with companies moving beyond pilots and looking for ways to deploy AI agents across their organizations. That shift is exposing a problem that has little to do with whether companies have enough data. Instead, the challenge is whether AI can understand which information actually matters.
Modus is emerging from stealth with a $10 million seed round led by Insight Partners to address that problem, according to a report first released by Axios. The financing includes Soma Capital, Bullet Ventures, and technology founders and operators including Eyal Kishon, Nadav Avrami of Wix and Dazl, the co-founders of Cyera, and the founders of Epsagon.
The Tel Aviv-based company is launching the Context Warehouse, an infrastructure layer designed to continuously learn how a business operates and provide AI agents with the relevant context for each interaction.
Closing the Context Gap
AI systems can already access enterprise information across data warehouses, BI tools, documents, tickets, code repositories, and collaboration platforms. But Modus argues that access does not equal understanding. An agent may retrieve multiple dashboards or definitions without knowing which ones the business actually trusts.
That can lead agents to over-fetch information, repeatedly query enterprise systems, and consume unnecessary tokens. As companies connect AI to more systems, the result can be higher costs, slower responses, and less reliable answers.
Modus calls this the “Context Gap”: the distance between what AI can access and how the business actually works. The company believes enterprises need a continuously maintained understanding of their operations to build effective company brains, context layers, and AI agents.
“Companies are no longer just trying to get their teams to use AI. They are asking how to scale it across the organization without accuracy dropping, governance breaking, or costs spiraling,” said Daniel Shimoni, CEO and co-founder of Modus. “Whether people call it a company brain, a context layer, or context engineering, they are all trying to solve the same problem. We believe every enterprise needs a continuously maintained understanding of how the business operates before it can build any of those things. That is what the Context Warehouse provides.”
Learning How Businesses Actually Work
Modus is positioning the Context Warehouse as a foundational layer for enterprise AI, similar to the role data warehouses play for enterprise data. Rather than simply making information available, the platform is designed to learn how organizations use that information.
It analyzes metadata and usage patterns across data warehouses, BI tools, pipelines, code repositories, documentation, and collaboration systems. It can also learn from recurring analyst queries, frequently used dashboards, pipelines, and decision threads that reveal how work happens in practice.
The platform then composes only the context relevant to each AI interaction. Modus says this approach can reduce unnecessary retrieval and token consumption by up to 10x, allowing agents to focus on relevant information rather than processing excessive amounts of data.
The Context Warehouse operates independently of any data warehouse, AI model, or application platform and works with agents teams already use, including through MCP. Modus says organizations can also keep sensitive customer data inside their own environments rather than centralizing it.
The Challenge of Keeping Context Current
Modus was founded by Daniel Shimoni, former VP of Product at Lusha, and Tomer Mesika, former Head of Architecture at Cyera. Their experience helped shape the company’s focus on a problem that can emerge after enterprises begin building their own context layers: keeping them accurate as the business changes.
“Building a context layer is not the hardest part,” said Tomer Mesika, CTO and co-founder of Modus. “Keeping it current is. Every change your business makes changes the context AI depends on. The real decision is no longer buy versus build. It is whether you want to own the ongoing cost of maintaining that understanding. We built the Context Warehouse so engineering teams can build what differentiates their business instead of maintaining the infrastructure underneath it.”
Modus says its platform is already deployed with enterprise customers across financial services, technology, and SaaS. According to the company, those organizations have used it to improve AI accuracy, strengthen governance, accelerate response times, and reduce the cost of operating AI at scale.
Building for Production-Scale AI
Insight Partners’ investment reflects its view that AI’s transition into production will require new enterprise infrastructure.
“Every major wave of enterprise software has required a new foundation,” said Ganesh Bell, Managing Director at Insight Partners. “Data warehouses became foundational infrastructure for enterprise data. As AI becomes production infrastructure, organizations need a system of understanding that every agent and application can build on. We believe Modus is defining that category with the Context Warehouse.”
Modus’ immediate focus is helping enterprises make AI agents more accurate, efficient, secure, and easier to scale. The company ultimately sees the Context Warehouse supporting a broader class of AI systems that can surface what matters and detect what has changed.
The longer-term ambition is to help organizations move beyond AI that simply provides trusted answers. By maintaining an evolving understanding of how a business operates, Modus believes its infrastructure could help enable a shift toward what it calls trusted action.
