Published customer proof · managed IT services
How K3 Technology uses Private GPT for managed IT work
“We needed an AI platform that understood our business, remembered our clients, and kept our data private.”
Dedicated environment
Microsoft Azure deployment configured for K3 Technology
Approved sources
business documentation connected for retrieval
Sourced responses
answers grounded in connected company knowledge
Managed operation
deployment, access configuration, updates, and support handled together
The problem: public AI creates a data-handling gap for an MSP
K3 Technology manages IT infrastructure for client businesses, which means their internal knowledge includes exactly the kind of material that must never end up in a consumer chatbot: client environment details, configurations, and operational documentation. At the same time, their engineers wanted AI leverage for the daily grind of tickets, documentation, and client communication.
The deployment
We provisioned a dedicated Azure environment, applied K3’s branding, and connected their approved documentation and business sources. Their team got a single assistant that answers from company knowledge with sources, remembers client context across conversations, and provides selected model options through one interface under the data-handling terms configured for the deployment.
The result
AI moved from a liability conversation to daily infrastructure. Engineers ask questions and get answers grounded in K3’s own documentation. The assistant helps prepare client communication drafts for review. And when K3’s own customers ask where their data goes, the team can explain the dedicated environment and configured data controls.
This page uses the existing K3 statement above and the deployment details already published by HummingAgent. The corresponding case study also lives on hummingagent.ai.
Follow the evidence
From the customer story to the deployment details
Use the case study for customer context, then inspect the architecture, controls, service boundary, and proposal inputs on the pages built for those questions.
IT services use cases
See the MSP workflows this deployment pattern is designed to support.
Review →Reference architecture
Trace the request path and the model-provider boundary.
Review →Security review
Review the controls and the questions that remain deployment-specific.
Review →Managed service
Understand what HummingAgent configures and operates.
Review →Preparing internal rules first? Use the AI data handling policy workbook. Ready to discuss fit? Review the proposal factors or book a meeting.
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