JLINC Your Data
The model isn't what makes enterprise AI unique. Your context is. Enterprise data, institutional knowledge, customer relationships, business rules, workflows and proprietary information are what transform a general-purpose model into an AI system that actually understands your business.
That's why enterprises are investing heavily in knowledge graphs, semantic layers, data fabrics, RAG architectures, MCP servers and other technologies designed to make proprietary knowledge available to AI. But the more valuable context an organization exposes to autonomous agents, the more important the trust problem becomes. Access control can determine whether an agent gets through the door, but it doesn't necessarily establish why the agent is accessing the information, whose authority it carries, what it may do with the information, where that information goes next or what actually happened after access was granted.
JLINC provides the trust layer between AI and enterprise context. Verifiable Contractual Agreements establish the terms under which agents interact with enterprise information, while cryptographic evidence creates a verifiable history of what happens under those terms. Enterprises can give AI access to richer, more valuable context while retaining provenance, chain of control and independent evidence of how that context was used.
Traditional access control asks whether a user or application is allowed into a system. Agentic AI requires more: Which agent is asking? Who does it represent? Why does it need this information? What may it do with it? Can it share it? What happens after access is granted?
A VCA can establish agent identity, delegated authority, permitted information, purpose, usage policies, downstream sharing, obligations and evidence requirements. The agreement becomes part of the interaction rather than a policy document sitting somewhere else.
Authorization tells you what an agent may do. JLINC creates evidence of what the agent actually did. Our trust layer unites the other softrware vendors powering context by creating an audiitable traiil from policy to authorization and then interaction.
JLINC can establish where information originated, which agent accessed it, under whose authority, which policies applied, how information moved through the workflow, which other agents or systems received it and what actions followed.
JLINC can operate alongside knowledge graphs, semantic layers, enterprise data platforms, data fabrics, RAG architectures, vector databases, MCP servers, APIs, agent platforms, identity and authorization systems.
Enterprises are investing heavily in semantic layers, knowledge graphs and other technologies that make proprietary information accessible to AI. But organizing enterprise context is only the first step.
As agents begin accessing and actingon that information, organizations also need a way to establish who is authorized to use it, under what terms, and what happens to it next.
JLINC partners with AI, data and context-platform providers to combine those capabilities into a more complete enterprise solution. Our partners help customers build and activate their proprietary context, while JLINC provides the verifiable trust, provenance and auditability layer governing how agents interact with it. CloudGo is one of the partners helping usbring this model to market. CloudGo helps enterprises rapidly create AI environments that bring together models,data and semantic context, while JLINC establishes trusted relationships between the agents, information and systems operating within those environments. Together, we give customers a practical way to begin experimenting with their own enterprise context without losing visibility and control as AI starts using it.
This creates a repeatable go-to-market model for AI platforms, data companies and consulting partners. Start by helping a customer activate its proprietary context, establish trusted access for agents, test new workflows and then expand successful use cases into production. Partners provide the technology and expertise that make enterprise context useful; JLINC provides the trust infrastructure that helps make its use verifiable.
Help enterprises turn proprietary knowledge into AI they can trust.
