Modus Launches With $10M Seed Led by Insight Partners to Tackle Enterprise AI's Context Challenge

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

Enterprise companies have spent years building systems to organize their data. Now, as AI agents move into production, a different infrastructure problem is becoming harder to ignore: knowing what that data actually means in the context of a particular business.

According to a report by Axios, Modus is emerging from stealth with a $10 million seed round led by Insight Partners to address that challenge. The funding also includes Soma Capital, Bullet Ventures, and a group of 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 an organization operates and provide AI agents with the specific context they need for each interaction.

The Next Enterprise AI Bottleneck

The first phase of enterprise AI adoption was largely about access. Companies connected models to databases, documents, business intelligence systems, code repositories, tickets, and collaboration platforms in an effort to make AI more useful.

But greater access has also created a new problem. AI agents can retrieve too much information, repeatedly query the same enterprise systems, and consume unnecessary tokens because they do not inherently understand which sources, definitions, or pieces of business logic are most relevant.

Modus describes this as the "Context Gap"—the distance between the information AI can reach and its ability to understand how the business actually works.

That distinction becomes increasingly important as organizations move from AI pilots to production deployments. More data does not necessarily translate into better answers. In some cases, it can lead to higher costs, slower performance, and less confidence in the output.

"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."

From Data Storage to Business Understanding

Modus is positioning the Context Warehouse as a new layer in the enterprise technology stack. The company's premise is that, just as data warehouses became systems of record for enterprise data, AI applications will need a system that provides an ongoing understanding of the business itself.

The platform is designed to learn from how organizations actually work. It analyzes metadata and usage patterns across data warehouses, BI tools, pipelines, code repositories, documentation, and collaboration systems.

That includes signals that may not appear in formal documentation. Recurring queries from analysts, dashboards that teams consistently use, pipelines, decision threads, and other activity can all help reveal how a business operates in practice.

Modus says this allows context to be derived from real usage rather than simply documented once and left to become outdated. The platform then composes the relevant context for individual AI interactions, helping agents work with what the company describes as signal rather than noise.

According to Modus, the approach can reduce unnecessary retrieval and token consumption by up to 10x.

An Independent Layer for AI Agents

The Context Warehouse is designed to sit independently of any specific data warehouse, AI model, or application platform. For enterprises, that means adopting new models and tools without having to rebuild the underlying approach to context management.

The platform also works with agents teams already use, including through MCP. Modus says it does not require organizations to centralize sensitive business data because it learns from metadata and usage patterns while sensitive customer information remains within the customer's environment.

Governance is also intended to be applied before context reaches the model. According to the company, each AI interaction receives only the information it 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 backgrounds helped shape the company's view that the infrastructure enterprises currently rely on was not originally designed for the way AI agents operate.

The Maintenance Problem

For enterprises already building their own context layers and company brains, Modus argues that the biggest challenge may come after the initial implementation.

Businesses change constantly. New processes emerge, teams reorganize, data sources shift, and the way employees use information evolves. As a result, the context provided to AI can quickly become outdated unless it is continuously maintained.

"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 customers have used it to improve AI accuracy, strengthen governance, accelerate response times, and reduce the cost of operating AI at scale.

Building Infrastructure for Production AI

Insight Partners' investment reflects a broader thesis about what enterprises will need as AI becomes embedded in everyday 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."

For Modus, the immediate goal is to improve the accuracy, efficiency, and governance of AI agents. The longer-term opportunity is broader: creating a continuously maintained understanding of enterprise operations that could allow AI systems to identify changes, surface important information, and help organizations move beyond answering questions toward taking action.

That vision places the Context Warehouse in an emerging layer of enterprise AI infrastructure, where the challenge is no longer simply connecting models to company data, but helping those models understand how the company actually works.

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