Modus’s operandi: To give AI agents just the right amount of context
Modus has emerged from stealth with $10 million to build a context warehouse that gives AI agents only the enterprise information they need.
As more companies plug AI agents into the deepest depths of their internal data banks, how can they be sure those agents actually understand how the business works? Right now, many of these organizations are stuck manually building a Markdown file, hoping they find time to rewrite it each time the business changes.
Modus, for its part, thinks it has found a better way. The startup that formally exits stealth this week with $10 million in funding in tow is building what is coming to be known in industry parlance as a “context warehouse” — a layer that sits alongside a company’s existing data warehouse, continuously mapping how the business operates across its systems, and handing an AI agent only the relevant slice of that map when it needs it.
In real terms, Modus crawls relevant assets from sources like GitHub, dbt, Jira, Snowflake, and Postgres, using what it calls a Context Miner to continuously learn how the business operates. What it finds gets turned into “dynamically generated skills”: Short, purpose-built briefs, assembled in real time by a second system, the Context Composer, and handed to an agent the moment it’s given a task.
Modus co-founder and CTO Tomer Mesika tells The New Stack that this mining runs continuously, guided by its own internal logic for what to check and how often.
“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.
Daniel Shimoni, Modus co-founder and CEO, draws a direct line to data warehousing: “There’s a logic behind data warehouses — companies already know that is where they manage their data. 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?”
Shimoni says building context the first time is already a challenge, but maintaining it is the bigger issue. Modus always learns from what the company is doing, and whenever something shifts, it makes sure that only the relevant and updated context is fed to agents.
Shimoni says Modus is targeting engineering teams, the CTO office, and VPs of R&D, as well as data teams and a newer category of AI teams / AI enablement roles.
Mesika argues frontier models waste token budget on menial tasks such as combing through pull requests or Jira tickets. Rather than retrieving that context at question time, Modus uses small language models alongside search engines, vector search, and a graph database built up in advance. By the time an expensive frontier model gets involved, it’s only handed a finished brief of exactly what it needs.
Shimoni and Mesika come from Lusha and Cyera respectively; they left their roles in September 2025 to start Modus. Modus closed a hitherto unannounced $10 million seed round shortly after founding, led by Insight Partners. Other backers include Soma Capital and angel investors, among them founders from Cyera and Wix.com. The company began hiring its first employees in January 2026.
Note: TNS owner Insight Partners is an investor in Modus.