Modus has raised $10 million in seed funding to build infrastructure designed to solve a growing problem in enterprise AI: giving agents the right business context at the right time. The Tel Aviv-based startup is developing what it calls the Context Warehouse, a platform designed to map how an organization operates and provide AI agents with relevant business knowledge instead of exposing them to large amounts of disconnected enterprise data. Insight Partners led the round, with participation from Soma Capital, Bullet Ventures and technology founders and operators associated with Cyera, Wix, Dazl and Epsagon. The funding gives Modus room to expand its platform as companies move from experimenting with AI agents toward deploying them across more demanding enterprise workloads.

Modus Targets the Context Gap in Enterprise AI

The problem Modus is addressing starts with an uncomfortable reality: enterprises already have enormous amounts of data, but AI agents often struggle to understand which information matters. Companies operate across data warehouses, databases, dashboards, code repositories, project-management platforms and internal documents, yet those systems rarely provide an agent with a unified view of how the business works. Modus wants its Context Warehouse to connect these sources and build a continuously updated understanding of the relationships between them. The company sees context as a dedicated infrastructure layer that can sit between enterprise data and the AI agents using it.

Modus connects with enterprise systems including GitHub, dbt, Jira, Snowflake and Postgres. Its Context Miner continuously collects information from these systems and identifies changes that could affect an organization’s operating context. The Context Composer then turns that understanding into dynamically generated skills that provide agents with information relevant to a specific task. This approach is intended to prevent agents from receiving excessive information when they only need a smaller, more precise set of business context.

Continuous Context Becomes a Core AI Challenge

Tomer Mesika, Modus co-founder and chief technology officer, said the company designed its platform around the idea that enterprise context changes constantly. “We have a lot of mechanisms in place to know what to mine from the organization, at what cadence, how to look for deltas, when to dive deeper in, and when not to,” Mesika says. That distinction becomes important as businesses change their systems, processes, data structures and operating rules. An AI agent working from outdated context can produce an answer that appears credible while still reflecting an older version of the business.

Modus therefore treats context maintenance as an ongoing infrastructure problem rather than a one-time knowledge-ingestion task. Daniel Shimoni, Modus co-founder and chief executive officer, described the gap by comparing context management with the role data warehouses play in enterprise data. “There’s a logic behind data warehouses — companies already know that is where they manage their data,” Shimoni tells The New Stack. “But where do they manage their context? Where do they actually understand what contexts exist in their organization, that they can actually use to ensure agents only have what they need?”

That question goes beyond simple retrieval. Enterprise agents need to understand which information is authoritative, how different systems relate to one another and which business rules apply to a particular task. A conventional retrieval system can surface documents or records, but it may not understand the operational relationships connecting those sources. Modus is attempting to build that layer of understanding continuously, giving agents a more structured representation of the organization they are operating inside.

Maintaining Context May Be Harder Than Creating It

Shimoni said the company discovered that creating an initial context layer was only part of the challenge. “We’ve noticed that building the context the first time is already a challenge, but maintaining it is the bigger issue,” Shimoni says. “So Modus always learns from what the company is doing, and whenever something shifts or changes in the business, it makes sure that only the relevant and updated context is fed to agents.” The approach places continuous updates at the center of Modus’ architecture rather than treating context as a static repository.

This becomes particularly important when companies operate hundreds of internal tools and workflows. A change in a database can affect a dashboard, a change in a code repository can alter an application, and a change in a business process can invalidate instructions that an agent previously relied on. Modus aims to identify those changes and reflect them in the context supplied to agents. The objective is to reduce the gap between what an enterprise currently does and what its AI systems believe the enterprise does.

Modus Puts Focus on AI Token Economics

Context infrastructure also has an economic dimension. Every unnecessary piece of information supplied to an AI model can increase token usage, latency and potentially the cost of running an agent. As enterprises deploy agents for more complex workloads, those costs can become significant, particularly when systems repeatedly retrieve large volumes of information simply to find a small amount of relevant context.

Modus argues that better context selection can allow companies to use expensive frontier models where they create the most value rather than spending model capacity processing irrelevant enterprise information. The company says its approach can reduce unnecessary retrieval and token consumption by as much as tenfold, although the actual benefit depends on the workload, architecture and deployment environment. The broader argument is that enterprises may be able to control AI costs by improving the infrastructure around models instead of relying solely on smaller models or reduced workloads.

“Even last year […] we could already see that model capabilities weren’t the bottleneck,” Shimoni says. “It was more making sure that they actually have access to the context they need in order to give you the right answers.” His point reflects a broader change in enterprise AI priorities. As model capabilities improve, companies increasingly need infrastructure that can determine what information those models should receive before they begin performing complex tasks.

$10M Funding Supports Modus Expansion

Insight Partners led Modus’ $10 million seed round, with Soma Capital, Bullet Ventures and a group of technology founders and operators also participating. The investor group includes Eyal Kishon, Nadav Avrami of Wix and Dazl, the co-founders of Cyera and the founders of Epsagon, according to the company. Modus began hiring its first employees in January 2026 as it moved from its founding phase toward commercial development. The company now plans to use the new capital to expand its technology and support the growing demand for infrastructure built specifically around enterprise AI agents.

Modus is not positioning the Context Warehouse as a replacement for existing data warehouses, AI models or enterprise applications. Instead, the company wants its technology to work across those systems and provide an additional layer that understands how their information connects. That positioning could give Modus access to a broader market because enterprises would not necessarily need to replace their existing infrastructure to adopt the platform. The challenge will be proving that another infrastructure layer can deliver enough value to justify its place inside increasingly complex AI technology stacks.

Founders See Context as the Missing AI Layer

Shimoni and Mesika bring experience from enterprise technology companies where data and software infrastructure play a central role. Shimoni previously served as vice president of product at Lusha, while Mesika worked as an architecture leader at Cyera. The two founders left their previous roles in September 2025 after identifying similar problems from different positions within the enterprise technology stack. “Some of the challenges were very similar — how do we combine a lot of various data assets into one place where AI can work?” Shimoni says.

The founders concluded that the missing layer was not simply another database or retrieval system. “We just started to notice that this is the gap — to make AI run with confidence, at scale, across a company.” That thesis places Modus in a part of the AI infrastructure market that is becoming increasingly important as companies move from individual copilots toward autonomous or semi-autonomous agents. Agents can perform more sophisticated work, but their usefulness depends heavily on whether they understand the organization around them.

The company believes the market is increasingly recognizing this problem. “We decided this is a problem worth solving, and it seems like we were spot on, because everybody’s talking about context.” The statement reflects how quickly context has become a central theme in discussions around agentic AI. For Modus, the opportunity is to turn that attention into a durable infrastructure category rather than another temporary feature of AI application development.

Enterprise Adoption Will Test the Model

Modus says its platform already serves enterprises in financial services, technology and software. These organizations typically operate complex environments where data lives across multiple systems and business processes can change independently. Modus says its platform has helped improve AI accuracy, strengthen governance and reduce the operating cost of enterprise AI deployments. The next challenge will be demonstrating that these benefits continue as customers expand their agent workloads and introduce more complex production use cases.

The company is targeting engineering organizations, CTO offices, research and development teams, data groups and AI enablement teams. Shimoni said the role responsible for AI deployment is also changing as businesses combine traditional data responsibilities with broader AI mandates. “AI teams weren’t really around last year; it seems that a lot of data teams are transitioning to becoming VP of data and AI, or AI enablement,” Shimoni says. “So really, it’s the people who are in charge of having this AI enablement mandate in the organization, making sure AI is scaled in the organization.”

This shift creates a new potential buyer for infrastructure products such as Modus. AI leaders need to scale deployments while maintaining accuracy, governance and cost controls across multiple business functions. Data teams already understand how to manage enterprise information, but agentic AI introduces a different requirement: making that information usable in a dynamic operational context. Modus is betting that this responsibility will become important enough to support a dedicated infrastructure category.

Modus Bets Context Will Become AI Infrastructure

The strategic case for Modus is built around treating enterprise context as an infrastructure asset rather than a collection of prompts, documents and retrieval pipelines. As agents take on more complex responsibilities, they need systems that can distinguish authoritative information from outdated material and determine which business rules apply to each task. Modus is effectively proposing a counterpart to the enterprise data warehouse, focused not only on where information exists but also on how that information should be interpreted and used. If the model gains traction, context could become another foundational component of the enterprise AI stack alongside data, identity, compute and orchestration.

The company’s thesis also challenges the idea that larger models alone will solve enterprise AI reliability and cost problems. “You want the bigger models to do the heavy and complex tasks to get great value,” Shimoni says. The implication is that enterprises can improve AI economics by making the surrounding infrastructure more intelligent before simply increasing model size or retrieval volume. Modus is therefore using its new funding to pursue a problem that sits outside the model itself but could determine how effectively models perform inside real businesses.

The next phase will show whether companies view context as a distinct infrastructure problem worthy of dedicated technology. Modus must demonstrate that its continuously maintained context can remain accurate across fragmented enterprise systems while delivering measurable improvements in reliability, governance and cost. The timing gives the startup an opportunity as enterprises move from AI experimentation toward production-grade agent deployments. Its $10 million funding round gives Modus the resources to make the case that the next major bottleneck in enterprise AI may not be model intelligence, but the quality of the context surrounding it.