Whitepaper · Free download

How to build self-healing networks with AI

Building Self-healing Networks with AI shows how to change the workflow to diagnose, decide and remediate, and the architecture, guardrails and proof required before anyone should trust it on a live network.

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Get your free copy of Building Self-healing Networks with AI.
  • Three levels of autonomy
  • The six-step loop
  • An evaluation framework
Fiber operators running on AEX
  • Arrive Internet
  • Evotel
  • Foam Internet
  • Foothills Broadband
  • LinkLayer
  • Magnolia Tree Fibre
  • MFibre
  • OpenFibre
  • Prime One
  • Redrock Telecommunications
  • Residence Fiber
  • Ripple Fiber
  • Unifybr
  • ZoomFibre
The most overused phrase in telecom

Every vendor says "self-healing." Few can show how it works.

Most vendors sell self-healing as a destination that arrives once you've applied enough AI. That leaves operators with nothing concrete to evaluate and nothing to hold a vendor to. This whitepaper treats it as a mechanism instead.

A mechanism can be built, tested and proven. A destination can only be promised.

The workflow, rebuilt
  1. 1
    DiagnoseIdentify what failed and why
  2. 2
    DecideChoose the right action, within guardrails
  3. 3
    RemediateAct, then confirm the fault is closed
Inside the whitepaper

Four things you'll take away

The architecture, the guardrails and the proof required before anyone should trust self-healing on a live network.

  1. 1

    Three levels of autonomy

    Why "does your platform do self-healing?" is the wrong question to ask a vendor.

  2. 2

    Where generic AI breaks

    The four specific ways it fails on a live network, and the engineering decisions built around each.

  3. 3

    The six-step loop

    Every step a fault must pass through to close itself, including the two most vendors skip.

  4. 4

    Proof, fault class by fault class

    How autonomy gets proven, with an evaluation framework built to survive a change board.