The hardest part of enterprise AI is not a clever prompt. It is an agent that does real work, day after day, against your real data, with your real policies, and survives in production. Z7 ships production MCP servers, runs its own agent product line, and operates its own AI infrastructure to deliver exactly that.
These solve the two structural reasons enterprises rejected agentic AI: data egress and network exposure.
The agent’s reasoning happens on Anthropic; its execution, file storage, and tool calls happen inside your perimeter. Confidential data never leaves your tenancy, and existing DLP and logging sit in front of every action.
Your Claude agent reaches MCP servers inside your private network with no public exposure: an outbound tunnel, no inbound holes. The integration that used to need a six-week security review becomes a config step.
A one-week engagement to identify your highest-impact agentic use case. Output: a one-pager defining the agent’s role, tools, success metrics, and deployment architecture.
In four to eight weeks we design, build, and deploy a production pilot: persona engineering, tool inventory, sandbox configuration, MCP tunnel setup, guardrails, audit logging, and an end-user interface.
Once a pilot proves out, we deploy across the business: multi-tenant architecture, high-availability sandbox infrastructure, cost optimization, and SLA-backed operations.
A monthly retainer: 24/7 monitoring, model version management, prompt-engineering refresh as Claude evolves, policy updates, and governance audits.
The integrations the agent needs that do not exist off the shelf: CRM, ERP, EHR, LMS, billing, and ticketing. Z7 has shipped production MCP servers for GoHighLevel, WordPress, and customer systems.
A defensible program: acceptable use, audit logging and retention, an incident-response playbook, regulatory mapping (FERPA, HIPAA, CMMC, PCI, GLBA), and board-ready reporting.
We build the agent, configure the sandbox, integrate the tools, harden it, deploy it, and operate it. Let’s find your highest-impact use case.
