TL;DR

Modus has raised $10M in a seed round led by Insight Partners to launch the Context Warehouse, a foundational layer for enterprise AI that captures how business information is actually used rather than just storing it. The company argues that connecting AI to more data does not make it smarter, and that enterprises need a system that composes only the relevant context for each interaction, cutting unnecessary retrieval and token costs by up to 10x.

The enterprise AI market is moving into a new phase. As companies shift from experimenting with individual AI applications to deploying agents across business operations, the challenge is increasingly less about giving models access to information and more about helping them understand which information actually matters.

That is the problem Modus is targeting as it emerges from stealth with a $10 million seed round led by Insight Partners, as first reported by Axios. The round 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.

Alongside the funding, Modus is introducing the Context Warehouse, which it describes as an infrastructure layer designed to give AI systems a continuously maintained understanding of how an enterprise actually operates.

Enterprise AI’s Context Problem

Modern AI systems can already retrieve information from many of the places where companies store and manage it. Data warehouses, BI platforms, documentation, tickets, code repositories, and collaboration systems can all be made accessible to models and agents.

But simply connecting an AI system to more data does not necessarily make it more useful. According to Modus, agents can repeatedly query enterprise systems, retrieve excessive amounts of information, and consume more tokens than necessary because they lack an understanding of which pieces of context are relevant to the task at hand.

The result can be higher costs, slower performance, and answers that are difficult for organizations to trust.

Modus calls the underlying problem the “Context Gap“, the distance between what AI can access and what it actually understands about the business. The company argues that enterprises need a system that can bridge that gap without requiring engineering teams to continually maintain a growing collection of context layers and company-specific AI infrastructure.

“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.”

A Warehouse for Business Understanding

Modus is positioning its Context Warehouse as a foundational layer for enterprise AI, drawing a parallel with the role data warehouses have played in organizing enterprise information.

The distinction is that the Context Warehouse is intended to capture how information is actually used rather than simply storing the information itself. It learns from metadata and usage patterns across data warehouses, BI tools, pipelines, code repositories, documentation, and collaboration systems.

It can also incorporate signals from the way employees work, including queries analysts repeatedly use, dashboards teams depend on, pipelines, documentation, and decision threads. Modus says these sources help the system develop an understanding based on actual business activity rather than relying solely on documentation or manually maintained semantic models.

The company says it then composes only the context relevant to a particular AI interaction. According to Modus, 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.

Designed to Sit Across the Enterprise Stack

The Context Warehouse is designed to operate independently of an organization’s existing data warehouse, AI model, or application platform. That architecture is intended to allow enterprises to adopt new AI models and tools without rebuilding the way their context is managed.

Modus also says the platform works with agents teams already use, including through MCP. Rather than requiring companies to centralize sensitive business information, the platform learns from metadata and usage patterns while keeping sensitive customer data inside the customer’s environment.

Security and governance are also built into the company’s approach. Modus says governance is enforced before context reaches the model, ensuring that AI interactions receive only information the user or system is authorized to access.

The company was founded by Daniel Shimoni, former VP of Product at Lusha, and Tomer Mesika, former Head of Architecture at Cyera. Their experience led them to what Modus describes as a shared realization: the systems enterprises depend on were not originally designed for AI agents.

The Cost of Keeping Context Current

The rise of internally built context layers and company brains has created another challenge. While enterprises can build systems that provide AI with additional business understanding, keeping those systems accurate as organizations evolve can become an ongoing engineering responsibility.

“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. The company says those organizations have used the Context Warehouse to improve AI accuracy, strengthen governance, accelerate response times, and reduce the cost of running AI at scale.

For Insight Partners, the investment reflects a view that enterprise AI will require infrastructure beyond models and data access.

“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’ broader ambition is to provide the foundation for what companies are increasingly calling a company brain or context layer. Today, that means helping AI agents deliver more accurate, efficient, and governed results. Over time, the company sees the same continuously maintained understanding supporting AI that can identify changes, surface what matters, and help organizations progress from trusted answers to trusted action.