Resources

Whitepapers

One paper is ours. The other is an open-access article our case study cites. Both are the files in the library, not a rewritten summary standing in for them.

OrcaMonitor case study

Trust by Design: A Case Study of Human-Approved AI Automation in Network Operations

Nikhil · Synetal Solutions Private Limited

Network engineers hesitate to give an automated system write access to production. This case study looks at how OrcaMonitor is built around that constraint: a cloud console, a per-customer instance, and an on-prem agent that is the only component to reach devices. The agent path is outbound, over WireGuard or IPsec, so the customer network does not open an inbound port.

Design choiceTrust it addressesWhat it does
Read-only chatIntegrity, predictabilityOrca AI Chat can answer from live inventory. It cannot change a device. A change still has to go through a ticket.
Staged ticket approvalControl, benevolenceAn agent drafts a remediation plan. The plan does not run until a person in the approval chain accepts it.
Audit loggingTransparencyWhat an agent observed, proposed, and did stays with the ticket and the approval record.
Multi-vendor coverageAbilityThe case is Cisco-first and also covers other device families, plus cloud and virtualization platforms.

The paper maps those four choices onto trust-in-automation research and a 2026 Cisco and Omdia survey of 1,000 IT and network leaders. It also says, in its own words, that a 94 percent drop in alert volume and a resolution time under five minutes come from the vendor's materials. The study did not measure them independently. It notes that an emergency override and granular role-based access were not clearly documented in the materials it reviewed.

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Cited open-access article

Automating Troubleshooting Network Issues with AIOps and Machine Learning

Mohit Bajpai · Journal of Artificial Intelligence & Cloud Computing, 2024

This is not an OrcaMonitor paper. Trust by Design cites it as prior work on using machine learning to detect anomalies, find a root cause, and trigger remediation. It is an open-access article under the Creative Commons Attribution License. The author and the journal hold the copyright.

Bajpai, M. (2024). Automating troubleshooting network issues with AIOps and machine learning. Journal of Artificial Intelligence & Cloud Computing, 3(1), 1–3.

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