Guidde Raises $50 Million Series B to Strengthen Enterprise AI Training Infrastructure

Tel Aviv–based Guidde has secured a $50 million Series B round as enterprises confront a growing challenge: how to train employees to work effectively with AI tools while also preparing AI agents to operate reliably inside complex software environments.

The round was led by PSG Equity, with participation from monday.com and existing investors Norwest, Entrée Capital, Qualcomm Ventures, and Inkberry Ventures. The funding follows several years of rapid expansion for the company, which now serves more than 4,500 organizations worldwide, including large enterprises such as Anheuser-Busch, Bayer, Nasdaq, Yahoo, and SentinelOne.

The new capital is intended to support broader deployment of Guidde’s platform as businesses scale AI initiatives across operations, finance, IT, legal, and customer-facing teams.

Turning Workflows Into Structured Knowledge and Living SOPs

Guidde’s core product enables teams to document software workflows simply by performing them. As an employee completes a task inside an application, the platform automatically generates step-by-step visual documentation, enriched with video, captions, voiceovers, and structured data.

A key component of this process is the creation and maintenance of standard operating procedures (SOPs). Instead of static PDF documents that quickly become outdated, Guidde transforms real user actions into living SOPs that reflect how work is actually done. Because these procedures are captured directly from day-to-day activity, they are grounded in operational reality rather than idealized process diagrams. This makes them more accurate, easier to update, and more aligned with compliance and governance requirements.

The documentation can then be embedded directly inside enterprise software, allowing employees to access contextual guidance without leaving the application they are learning. This “in-the-flow” approach addresses a long-standing friction point in corporate training, where static manuals and disconnected video libraries often fail to keep up with evolving systems. Instead of searching for documentation, employees encounter the relevant SOP at the moment they need it.

The same structured workflow data can also be exposed to AI systems through APIs. In this way, SOPs are not only reference material for humans but machine-readable instructions that can guide AI agents. The documentation becomes operational context for automation systems, helping ensure that AI agents follow approved processes and execute tasks consistently within defined boundaries.

Addressing the AI Adoption Gap

AI deployment across enterprises is accelerating, but many organizations are discovering that implementation alone does not guarantee impact. While large language models and automation tools are becoming more conversational and accessible, enterprise environments remain complex and fragmented.

Employees frequently struggle to generate consistent, high-quality results from AI tools without access to real prompts and real workflows. Meanwhile, AI agents intended to automate work can fail when they lack structured guidance drawn from how tasks are actually performed.

Guidde’s approach centers on observing real-world usage across tens of thousands of enterprise applications and millions of workflows. By capturing how employees complete tasks in practice, the company is building a growing dataset of operational knowledge that can be reused for both training and automation.

Building the Knowledge Layer for Enterprise AI

The broader shift underway is not just about adopting AI models, but about building the knowledge infrastructure those models depend on. As organizations invest heavily in AI, they must also ensure that internal processes are documented, standardized, and accessible in machine-readable formats.

Guidde positions itself within that emerging layer: transforming everyday workflows into structured, reusable assets that can guide both people and software agents.

With fresh funding and sustained growth, the company now enters its next phase focused on scaling that infrastructure globally. As enterprises move from AI experimentation to operational integration, the ability to map how work is actually done may prove as important as the models themselves.