The visual gap in customer support

Most AI agents in the customer service sector operate within the confines of a chat box. They ingest documentation, process user queries, and return text-based answers. While this solves for basic information retrieval, it fails for complex software support. When a user is stuck in a multi-step workflow or encounters a specific UI error, text descriptions are often insufficient. Fullview is building an agent that bridges this gap by adding a visual layer to the interaction.

Instead of relying solely on natural language processing of a knowledge base, Fullview’s agent is designed to see what the user sees. The company started in the cobrowsing and session replay market, providing tools for human agents to take control of a user’s screen to solve problems. They are now applying that technical foundation to autonomous agents. This move represents a transition from human-led screen control to an AI agent that can navigate an application independently to resolve issues.

Contextual awareness through screen vision

The differentiator for Fullview is its ability to understand the state of a software application in real-time. Standard support bots are blind to the user's current screen; they know only what the user types. Fullview integrates into the web application, allowing its agent to observe UI elements, console errors, and user behavior. This context allows the agent to provide visual guidance—pointing to specific buttons or walking through a workflow—rather than just sending a link to a help article.

This approach solves a major friction point in technical support: the description lag. Users often struggle to name the specific parts of an interface they are using. By seeing the screen, the agent removes the need for the user to act as a technical translator. The agent can verify if a setting is toggled correctly or if a specific error message has appeared without asking the user to take a screenshot.

From chat to autonomous action

The long-term goal for the company is to move beyond guidance and into resolution. If an agent can see the screen and understand UI components, it can theoretically take actions on behalf of the user. This places Fullview in the emerging category of UI-based agents, rather than just Large Language Models.

In a typical support scenario, a user might ask how to change their billing cycle. A standard bot provides instructions. Fullview’s agent can guide the user to the page and, with permission, perform the click path to complete the task. This level of autonomy requires a high degree of precision, as the agent is interacting with the live production environment of a customer's account. Fullview handles this through a zero-download architecture, which was a hallmark of their original cobrowsing product. This architecture ensures that the agent can observe and interact with the application without requiring the user to install additional software.

Market position and competition

Fullview enters a market of AI support tools dominated by incumbents and specialized support bots. Most competitors focus on the reasoning and the knowledge retrieval. Fullview is focusing on the visual interaction layer. By owning this layer, they create a barrier to entry that is difficult for pure-chat companies to replicate without significant infrastructure changes. Their ideal customers are SaaS companies with complex, multi-step products where visual context is the difference between a resolved ticket and a frustrated user.