The next challenge in enterprise AI may not be access to data. It may be understanding it.
Companies have spent years building data warehouses, business intelligence systems, documentation platforms, and other tools that organize information across the business. But as AI agents begin operating across those systems, enterprises are finding that simply giving models more access can create another problem: too much information without enough understanding of what matters.
Modus is emerging from stealth with a $10 million seed round led by Insight Partners to address that gap, as first reported by Axios. The financing also 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 introducing the Context Warehouse, an infrastructure layer designed to continuously learn how a business actually operates and provide AI agents with the relevant context for each interaction.
When Access Isn’t Understanding
Today’s AI systems can connect to data warehouses, BI tools, documents, tickets, code repositories, and collaboration systems. But having access to those sources does not necessarily mean an AI agent understands how a company uses them.
An agent may not know which business definition is trusted, which dashboard employees actually rely on, why a metric changed, or which piece of business logic should take precedence. Without that understanding, agents can over-fetch information, repeatedly query enterprise systems, and consume unnecessary tokens.
Modus describes this as the “Context Gap”: the distance between what AI can access and how the business actually works.
The problem becomes particularly important as companies move AI from pilots into production. More connected systems can mean more complexity, higher costs, and slower responses without necessarily producing more reliable results.
“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 From the Business Itself
Modus is positioning the Context Warehouse as a new infrastructure layer for enterprise AI. The company’s comparison is straightforward: data warehouses became systems of record for enterprise data, while the Context Warehouse is intended to become a system of understanding for AI.
The platform learns from metadata and usage patterns across data warehouses, BI tools, pipelines, code repositories, documentation, and collaboration systems. It also incorporates signals from how employees actually work, including the queries analysts repeatedly use, dashboards teams depend on, and decision threads that reveal how the organization operates.
Rather than relying solely on documentation or manually maintained context, Modus says its platform learns from real usage and continuously updates its understanding as the business changes.
The system then composes only the relevant context needed for each AI interaction. Modus says this allows agents to reason on signal rather than noise and can reduce unnecessary retrieval and token consumption by up to 10x.
The Context Warehouse is designed to operate independently of any specific data warehouse, AI model, or application platform. It also works with the agents teams already use, including through MCP, allowing companies to change models and tools without rebuilding their approach to context management.
The Maintenance Burden
The company was founded by Daniel Shimoni, former VP of Product at Lusha, and Tomer Mesika, former Head of Architecture at Cyera, where he built infrastructure to classify, govern, and secure enterprise information at scale.
Their experience informed the company’s view that existing enterprise systems were not built for the way AI agents operate. It also shaped Modus’ focus on one of the less visible challenges of building company-specific AI infrastructure: maintenance.
Enterprises can already build context layers and company brains themselves. But keeping those systems accurate as the business changes can require continuous engineering work.
“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.”
A Foundation for Production AI
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.
Insight Partners sees the technology as part of a broader infrastructure shift as AI becomes embedded in enterprise operations.
“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 goal is to help enterprises deploy AI agents that are more accurate, efficient, secure, and easier to scale. Its longer-term vision is to create a continuously maintained understanding of the business that can support AI systems capable of surfacing important developments, detecting changes, and helping organizations move from trusted answers to trusted action.
