Graft

Graft is defined as a unified intelligence layer designed to centralize, manage, and leverage an organization's internal knowledge. This platform acts as a bridge between employees and the vast amount of information scattered across different digital silos, allowing users to perform queries via conversational AI to obtain precise, contextualized answers. Its primary function is to transform raw data into searchable strategic assets without requiring advanced technical knowledge in programming or artificial intelligence.

The technology behind Graft is based on deep integration with more than 150 data sources, ranging from databases and business tools to internal documents stored on a computer or in the cloud. By connecting these assets, the tool employs advanced search algorithms to locate relevant information in seconds. This approach enables technical and product teams to improve code quality, streamline employee onboarding, and clarify complex project requirements efficiently, optimizing decision-making based on real data.

Key functional capabilities of this enterprise AI solution include:

• Personalized smart search: Locating specific data across multiple systems from a single centralized interface.
• Content generation and summarization: The ability to extract key points from lengthy documents and synthesize critical information.
• No-code data extraction: Allowing non-technical users to get answers and process information without relying on developers.
• Natural language interaction: Facilitating intuitive communication with the company’s knowledge repository.

Using Graft is particularly beneficial for medium and large companies that manage high volumes of documentation and suffer from information fragmentation. By drastically reducing the time spent on manual searches, the platform maximizes operational productivity. Additionally, the tool prioritizes corporate information security by complying with the SOC 2 Type II standard. A fundamental aspect is that it guarantees privacy, ensuring that internal data is not used to train external third-party models.

Ultimately, this tool positions itself as an intelligent internal search engine that solves the problem of data dispersion. By offering a simplified access layer, it allows any team member to interact with the company's global knowledge, whether from a mobile device or a desktop computer, substantially improving agility in the daily workflow.