Modus Raises $10M Seed Led by Insight Partners to Build the Missing Infrastructure Layer for Enterprise AI

New York, USA, July 29th, 2026, FinanceWire

Enterprise AI has a data problem that is not necessarily about data availability. Most large organizations already have vast amounts of information distributed across warehouses, dashboards, applications, code repositories, documents, and collaboration tools. The harder problem is helping AI understand which pieces of that information are relevant, trusted, and meaningful in the context of how a company actually operates.

That is the premise behind Modus, which is emerging from stealth with a $10 million seed round led by Insight Partners. First reported by Axios, the financing also includes Soma Capital, Bullet Ventures, and prominent technology founders and operators, including Eyal Kishon, Nadav Avrami of Wix and Dazl, the co-founders of Cyera, and the founders of Epsagon.

Alongside the funding, Modus is launching the Context Warehouse, a new infrastructure layer intended to give enterprise AI systems a continuously maintained understanding of the businesses they operate within.

The Context Gap Behind Enterprise AI

AI models can increasingly access the systems where enterprise information lives. Yet access alone does not give an AI agent the ability to distinguish between information that is merely available and information that is actually useful.

An agent may be able to retrieve a company's dashboards, documents, tickets, code, and data. But it may not know which definition of a metric the business considers authoritative, which dashboard employees actually use, why a number changed, or which piece of business logic should take priority.

Modus refers to the distance between AI's access to information and its understanding of the business as the "Context Gap."

The issue is becoming more pronounced as enterprises attempt to move AI agents from pilot projects into production environments. Connecting an agent to additional systems can create more retrieval, more queries, and more tokens without necessarily producing better results. Modus argues that the problem is often not insufficient context, but an excess of context that lacks relevance.

"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 How Companies Actually Work

Modus is positioning its Context Warehouse as a foundational component for enterprise AI, comparing its intended role to that of a data warehouse for enterprise data.

The difference is that the platform is designed to understand the relationships and patterns surrounding information rather than simply making the information accessible. It learns from metadata and usage patterns across data warehouses, BI tools, pipelines, code repositories, documentation, and collaboration systems.

The system can also learn from the activity that reflects how employees operate in practice. That includes the queries analysts repeatedly return to, dashboards teams rely on, pipelines that support business processes, documentation, and decision threads that reveal how organizations make decisions.

This approach is intended to create an understanding based on actual usage rather than relying exclusively on documentation or manually maintained models. Modus says the Context Warehouse continuously updates its understanding as the business evolves and then composes only the context relevant to a particular AI interaction.

The company says this can allow agents to reason on "signal instead of noise" while reducing unnecessary retrieval and token consumption by up to 10x.

Context Without a New Data Silo

One of the company's central design choices is that the Context Warehouse is independent of any particular data warehouse, AI model, or application platform.

That means enterprises can change models and adopt new AI tools without having to rebuild their approach to context management. The platform is also designed to work with the agents organizations already use, including through MCP.

Modus says companies do not need to centralize sensitive business information to use the platform. Instead, it learns from metadata and usage patterns across the enterprise while sensitive customer data remains within the customer's environment.

The company also emphasizes governance at the point where context is delivered to AI. According to Modus, governance is enforced before context reaches the model, ensuring that each AI interaction receives only information it is authorized to access.

The Infrastructure Behind the Company Brain

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 led to a shared conclusion: the systems enterprises depend on were not designed with AI agents in mind. Modus was created around the idea that enterprises need a dedicated infrastructure layer to maintain the business context those agents require.

The company is also addressing a problem that has emerged as organizations begin building their own context layers and company brains. While an initial implementation may be achievable, maintaining its accuracy as the organization changes can become an ongoing burden.

"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 New Foundation for AI at Scale

Modus says its platform is already deployed with enterprise customers across financial services, technology, and SaaS. According to the company, those organizations have used the Context Warehouse to improve AI accuracy, strengthen governance, accelerate response times, and reduce the cost of operating AI at scale.

Insight Partners sees the emerging category as part of the broader infrastructure shift accompanying enterprise AI adoption.

"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."

The immediate use case for the Context Warehouse is helping AI agents deliver more accurate, efficient, and governed results. But Modus is ultimately aiming at a larger role for the technology.

As its understanding of an enterprise continuously evolves, the company believes the same infrastructure could eventually support AI systems that do more than answer questions. They could surface what matters, identify changes across the business, and help organizations move from trusted answers to trusted action.

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