The hardest part of scaling a logistics team isn't finding talent; it's transferring tribal knowledge.

Our client, a Global Freight Forwarder, faced a 6-month ramp-up period for new hires. Answers to critical questions—"How do we quote for hazardous materials in Germany?" or "What is the latest rate for Client X?"—were buried in thousands of old PDFs, Slack threads, and email chains.

Staff were bypassing the documentation and just asking senior managers, creating a massive bottleneck.

Note: Identities have been anonymized for privacy. Detailed case studies are available upon request.

The Solution: The "Super-Brain"

We didn't just build a chatbot; we built a Model Context Protocol (MCP) server. This acts as a secure bridge that allows their instance of ChatGPT Enterprise to "read" their internal tools live.

• Live Connection: Unlike a standard RAG database (which updates nightly), the MCP connection allows the AI to query the live Slack history and Google Drive folders in real-time.

• Context-Aware Drafting: Staff can now command: "Draft a quote for Client X based on their last 3 shipments and our current fuel surcharge." The AI retrieves the exact pricing history and tone preferences to generate a near-perfect draft.

The Technical Stack

• Core Brain: ChatGPT-4o (Enterprise Tenant).

• Protocol: Custom MCP Server hosted on AWS Fargate.

• Connectors: Google Drive API, Slack Enterprise Grid API.

• Security: OAuth2 authentication ensuring the AI respects existing user permissions (e.g., a junior rep cannot ask the AI to read the CEO’s private DMs).

The Impact

• 90% Faster Onboarding: New hires achieved full quota in Week 3, down from Month 6.

• Manager Relief: "Quick questions" sent to senior staff dropped by 70%.

• Zero Hallucination: Because the AI cites the specific PDF or Slack thread it used, staff trust the answers.

The Verdict: Your data is useless if it's disconnected. MCP turns your scattered files into a unified corporate brain.