SageOx, a Seattle-based startup developing infrastructure for AI-native teams, has raised $15 million in seed funding to address a growing challenge in modern workplaces: keeping humans and AI agents aligned. The round was led by Canaan Partners, with participation from A.Capital, Pioneer Square Labs, and Founders’ Co-op. The company says its mission is to turn scattered workplace knowledge into a shared context layer that supports collaboration between people and AI systems.
Building Context for AI-Native Work
As companies increasingly adopt AI agents across engineering, product, operations, and other functions, coordination is becoming more complex. Many agents still operate in isolated sessions, without durable memory of prior decisions, project history, architectural intent, or team conversations. SageOx aims to close that gap by capturing workplace context as it is created and making it available across future AI interactions.
The company’s platform is designed to collect signals from conversations, chat tools, coding sessions, and existing workplace systems. It then converts those inputs into structured knowledge that can be reused by both humans and AI agents. In practical terms, SageOx says this allows agents to begin tasks with awareness of earlier decisions and team standards rather than requiring constant manual recaps.
Addressing a Fast-Growing Coordination Problem
SageOx frames the issue as a new form of workplace misalignment created by the speed and scale of agentic systems. When agents lack shared history, teams may have to repeatedly correct outputs, restate goals, and manage drift across tools and workflows. The company argues that this problem becomes more serious as organizations move from experimenting with individual AI tools to coordinating fleets of AI agents.
Ajit Banerjee, SageOx’s founder and CEO, said faster work cycles are putting pressure on traditional processes. He noted that as teams begin operating many times faster than before, the ability to share decisions, intent, and history becomes essential infrastructure. SageOx plans to position its product as a foundational layer for this new model of work.
Early Customer Use and Investor Support
The startup is already working with early design partners and has adopted what it calls an “open work” model. Through that approach, users can observe how SageOx’s own team collaborates with AI agents while building the product. The company presents this as both a product philosophy and a live demonstration of continuous knowledge capture.
Marius Ciocirlan, CEO at Mark OS, said SageOx has helped bring AI agents closer to the core of team collaboration. He said that before using the platform, agents felt disconnected from in-person decisions and required frequent explanations. With SageOx, he said, conversations can turn into usable output more quickly and with less translation between humans and machines.
Leadership and Technical Background
SageOx was founded by Banerjee, CTO Ryan Snodgrass, and CPO Milkana Brace. Banerjee previously worked on foundational infrastructure at AWS, Apple, and Facebook, and founded XetHub, which was acquired by Hugging Face. Snodgrass was an early Amazon engineer involved in the company’s shift from monolithic systems to microservices and later worked on cloud infrastructure supporting Kindle devices.
Brace is a repeat founder and former executive vice president of product at Remitly, where she joined after the acquisition of her startup, Jargon. The broader team includes Galex Yen, described by the company as an experienced builder, leader, and entrepreneur. SageOx is also supported by advisors including author Steve Yegge and researcher Dr. Rupak Majumdar.
SageOx plans to use the new capital to continue product development and make a limited number of strategic hires. The company says it intends to remain lean while relying heavily on AI agents within its own operations. With AI increasingly embedded in daily knowledge work, SageOx is betting that shared context will become as important to the next generation of software teams as cloud infrastructure was to the last.