The Problem: AI Agents Lack Business Context

Enterprise AI agents often fail to deliver value because they lack an understanding of company-specific nuances, such as internal terminology, organizational hierarchies, and data relationships. While vendors often deploy engineers to manually integrate these systems, this approach is unscalable. Jedify addresses this by building a 'context graph' that connects an enterprise's fragmented knowledge sources—including databases, SaaS apps, documentation, codebases, and communication tools like Slack—into a unified, model-agnostic layer.

How the Context Graph Works

Unlike traditional semantic layers or metadata catalogs, Jedify’s context graph is multi-dimensional. It captures relationships across entities, people, permissions, and operational workflows. This allows an AI agent to narrow its focus to relevant data for a specific task rather than searching the entire corporate knowledge base.

Key features include:

• Real-time Updates: The graph updates as information flows through connected systems.
• Permission Inheritance: The platform integrates with existing identity and file systems to enforce row-, column-, and table-level access rules, ensuring agents do not expose sensitive data to unauthorized users.
• Governance: It provides observability tools to monitor agent behavior and ensure compliance.

Strategic Value and Market Positioning

Jedify positions itself as a complementary layer to large data platforms like Snowflake (a strategic investor). While cloud providers encourage centralizing data, most enterprises operate across multiple databases and warehouses. Jedify bridges these silos, offering a more comprehensive view of institutional knowledge. By providing this context layer, Jedify helps companies avoid the high costs of training models on proprietary data while creating a durable 'moat' as AI models become increasingly commoditized and interchangeable.